DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/10/2025 has been entered.
Applicant' s arguments, filed 11/10/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 11/10/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-20 are the currently pending claims hereby under examination. Claims 1, 5-11, 13, 15-18, and 20 have been amended.
Claim Interpretation
In claim 1, the phrase “discrete or separate fluorescence data values … being discrete or separate from fluorescence data values of each of the other frames” (lines 11-13) is interpreted consistent with the specification as requiring that each “frame” is its own separately grouped set of fluorescence samples (e.g., one set per rotation), not that the underlying imaged tissue region must be spatially non-overlapping between successive frames. The specification describes each “frame” as a distinct set of fluorescence data values collected for a given acquisition interval (e.g., one rotation / one B-scan), i.e., “the plurality of fluorescence data values is grouped into one or more frames … collected from one full rotation of the imaging catheter”. (Instant Application, [0052]). Accordingly, “the discrete or separate fluorescence data values of one frame … being discrete or separate from fluorescence data values of each of the other frames” is reasonably interpreted as separately collected frame datasets (separate sets of values per frame), without importing any requirement that successive frames must correspond to completely non-overlapping spatial regions. (Instant Application, [0072]-[0073]).
Similarly, claim 11 recites “discrete or separate fluorescence data values … being discrete or separate from fluorescence data values of each of the other frames” (lines 11-14). The same interpretation as found above in claim 1 also applies to claim 11.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1- 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Wang, Hao et al. “Ex Vivo Catheter-Based Imaging of Coronary Atherosclerosis Using Multimodality OCT and NIRAF Excited at 633 Nm.” Biomedical optics express 6.4 (2015): 1363–1375. Web.), hereto referred as Wang 2015, and further in view of Ishihara (US-20120323072-A1), hereto referred as Ishihara, and further in view of Ughi et al. (Ughi, Giovanni J et al. “Clinical Characterization of Coronary Atherosclerosis With Dual-Modality OCT and Near-Infrared Autofluorescence Imaging.” JACC. Cardiovascular imaging 9.11 (2016): 1304–1314. Web.), hereto referred as Ughi 2016.
Regarding claim 1, Wang 2015 teaches that a method for accurately quantifying fluorescence emitted from within a bodily lumen (Wang 2015, Abstract: “In this study, we used a recently developed imaging system and double-clad fiber (DCF) catheter capable of simultaneously acquiring both OCT and red excited near-infrared autofluorescence (NIRAF) images (excitation: 633 nm, emission: 680nm to 900nm)... We found that NIRAF is elevated in lesions that contain necrotic core – a feature that is critical for vulnerable plaque diagnosis... These results suggest that multimodality intracoronary OCT-NIRAF imaging technology may be used in the future to provide improved characterization of coronary artery disease in human patients”, this shows Wang 2015 teaches a catheter-based method of quantifying fluorescence emitted from within a bodily lumen for medical imaging purposes); the method comprising: scanning the bodily lumen with an imaging catheter that transmits a light of wavelength capable of stimulating emission of fluorescence from within different regions of the bodily lumen (Wang 2015, p. 4, Sec. 2.1.1: “The multimode inner cladding (NA ≥ 0.46, diameter = 124–126 µm, fiber outer diameter = 250 µm) was used to guide 633 nm excitation light and to collect NIRAF tissue emission. The working distances for OCT and NIRAF were 2 mm and 0.5 mm, respectively. The focal spot size for OCT and NIRAF was 27 µm and 100 µm, respectively”, this teaches that the catheter transmits excitation light at 633 nm to stimulate fluorescence emission from within tissue regions of the bodily lumen); collecting a plurality of fluorescence data values corresponding to the fluorescence emitted from the different regions within the bodily lumen (Wang 2015, p. 8, Sec. 2.2.6: “A total of 15 coronary arteries from 5 human cadaver hearts were imaged using the multimodality OCT-NIRAF system and catheter. From these 15 coronary arteries, we analyzed NIRAF intensity from 37 distinct artery wall regions... Individual NIRAF intensity data points were selected from tissue sites on the coronary ROIs... A total of 1200, 1253, 1491, 1554 sites were selected from NC, PIT, CA, and IH regions, respectively”, this shows Wang 2015 teaches collecting multiple fluorescence data values corresponding to emission from different regions within the bodily lumen); grouping the plurality of fluorescence data values into one or more frames, each frame of the one or more frames having a number of discrete or separate fluorescence data values of the plurality of fluorescence data values collected from one full rotation of the imaging catheter within the bodily lumen (Wang 2015, p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line. Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, which teaches that each frame comprises individual A-line-based fluorescence measurements that are separately acquired within a single rotation, such that the fluorescence data values within a frame are discrete from one another and from fluorescence data values of other frames); and the discrete or separate fluorescence data values of one frame of the one or more frames being discrete or separate from fluorescence data values of each of the other frames of the one or more frames (Wang 2015, p. 7, Sec. 2.2.3: “Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, this teaches that each frame is a distinct set of A-line based data acquired during a single rotation, such that the fluorescence data values of one frame are separate from the fluorescence data values of other frames corresponding to other rotations).
Also regarding claim 1, Wang 2015 does not fully teach calculating a central tendency of the discrete or separate fluorescence data values in the one or more frames. Rather, Wang 2015 teaches collecting fluorescence intensity data and performing normalization (including normalizing by a maximum intensity), but Wang 2015 does not teach calculating a central tendency (e.g., an average/mean) of the discrete or separate fluorescence data values in each frame collected from one full rotation of the imaging catheter (Wang 2015, p. 8, Sec. 2.2.6: “The NIRAF intensity was normalized by the maximum intensity among the selected coronary tissue sites”).
Ishihara teaches calculating an “average” over the “entire image” of a “frame”, namely calculating an “average gradation value m of the entire image of the Subsequent frame”, which is a central tendency calculation applied to the discrete values that make up that single acquired frame (Ishihara, ¶[0070]: “the threshold-value setting unit 45 calculates an average gradation value m of the entire image of the Subsequent frame on the basis of calculation equation (2)”). In Ishihara, the ‘entire image’ is the image of the acquired single frame being processed (i.e., ‘the Subsequent frame’), not the entire collection of frames, because the calculation is performed upon acquisition of ‘a corrected fluorescence image of a Subsequent frame’ and the calculated value is stored and compared on a frame-to-frame basis” (Ishihara, ¶[0094]: “when the threshold-value setting unit 45 acquires a corrected fluorescence image of a Subsequent frame (step SB6), the threshold-value setting unit 45 calculates an average gradation value m of the entire image (step SB7), and compares it with the stored average gradation value m of the entire image of the previous frame (step SB8)”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang 2015 in view of Ishihara to calculate a central tendency of the discrete or separate fluorescence data values in the one or more frames by calculating an average (mean) value for the fluorescence data values of each frame. This modification would have been possible because Wang 2015 already forms rotation-synchronized frames from many discrete fluorescence measurements per frame, and Ishihara’s teaching is expressly implemented on a per-frame basis (“entire image of the Subsequent frame”), such that applying the same per-frame average computation to Wang’s per-rotation frames is a routine software-side calculation applied to already-collected frame data (Ishihara, ¶[0070]: “average gradation value m of the entire image of the Subsequent frame”). The benefit of the combination is that a frame-level central tendency provides a consistent statistical basis for subsequent frame-level processing and threshold-related processing, improving robustness to frame-to-frame variation while remaining within the existing catheter imaging processing pipeline (Ishihara, ¶[0072]: “the threshold value S is updated on the basis of the average gradation value m of the entire image”).
Also regarding claim 1, the modified Wang 2015 does not fully teach assigning the central tendency of one of the one or more frames as a background fluorescence threshold by determining a lowest central tendency of the one or more frames and assigning the lowest central tendency as the background fluorescence threshold. Rather, the modified Wang 2015 teaches collecting fluorescence data in rotation-synchronized frames and performing background removal using PBS solution and performing maximum-based normalization, but it does not teach determining a lowest central tendency among the one or more frames and assigning that lowest central tendency as the background fluorescence threshold (Wang 2015, p. 7–8, Sec. 2.2.5; Wang 2015, p. 8, Sec. 2.2.6).
Ishihara teaches that per-frame central tendency values are calculated and stored for comparison, and that threshold setting is not required for every subsequent frame because the threshold value S can be maintained and used for subsequent adjustment unless a reset is triggered. For example, Ishihara teaches that “the threshold-value setting unit 45 calculates an average gradation value m of the entire image (step SB7), and compares it with the stored average gradation value m of the entire image of the previous frame (step SB8)” (where "entire image" = frame)(Ishihara, ¶[0094]). Ishihara further teaches that under stable conditions, “the threshold value S can be maintained so that the calculation amount of the standard deviation O can be reduced” (Ishihara, ¶[0097]). Ishihara also teaches an operator-controlled approach where “an operator may manually change the threshold value by operating the threshold-value change command unit 261”, and “instead of always setting a threshold value every time a corrected fluorescence image of a Subsequent frame is generated, the corrected fluorescence image can be adjusted when the operator determines that the threshold value is not appropriate during observation” (Ishihara, ¶[0098]). Ishihara then teaches that if no change command is input, “the image adjuster 51 may adjust the gradation values of the corrected fluorescence image on the basis of the current threshold value S” and the process returns for subsequent frames (Ishihara, ¶[0100]). These teachings establish that Ishihara’s processing pipeline supports selecting a threshold value S based on a subset of frames by permitting operator-controlled cessation of threshold updating, which inherently defines the set of frames used to establish the operative threshold value S (Ishihara, ¶[0097]; ¶[0098]; ¶[0100]). Under Ishihara’s framework, maintaining a stable threshold value S presupposes that the selected threshold represents background-dominant conditions, such that subsequent frames need not trigger recalculation; selecting the lowest per-frame average from an initial background-dominant subset directly satisfies this stability criterion.
Ughi 2016 teaches that quantitative fluorescence processing routinely uses the lowest acquired fluorescence level as a reference derived from the acquired dataset itself, specifically teaching normalization using the minimum acquired NIRAF value (Ughi 2016, p. 1306, DATA PROCESSING: “Quantitative NIRAF data were normalized between 0 and 1 using the minimum and maximum NIRAF values acquired in the study”). This supports the routine understanding that the lowest acquired fluorescence signal corresponds to background conditions and is suitable for defining a background reference level when background information is derived from the acquired data.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara and Ughi 2016 to assign the central tendency of one of the one or more frames as a background fluorescence threshold by determining a lowest central tendency of the one or more frames and assigning the lowest central tendency as the background fluorescence threshold. This combination does not change the principle of operation of Ishihara because Ishihara already calculates and stores a per-frame average gradation value m and uses a threshold value S as the operative basis for subsequent adjustment, and the modification is limited to selecting, from among the already-calculated per-frame average gradation values m that Ishihara already computes and stores, a conservative baseline corresponding to the lowest such per-frame average observed during frames acquired in a background-dominant region during frames acquired in a background-dominant region, and then using that corresponding threshold value as the current threshold value S for subsequent adjustment (Ishihara, ¶[0094], ¶[0100]). Ughi 2016 confirms the routine understanding in fluorescence imaging that the lowest acquired signal corresponds to background conditions and is suitable as a reference level by expressly teaching minimum-based normalization (Ughi 2016, p. 1306, DATA PROCESSING). The modification is feasible because it is a software-side selection step applied to values (central tendencies) that Ishihara already calculates and stores on a per-frame basis, and because the modified Wang 2015 already collects rotation-synchronized frames suitable for frame-level statistics and threshold-based correction.
The benefit of the combination is improved robustness in subsequent fluorescence image thresholding and contrast enhancement by selecting a conservative background fluorescence threshold from the acquired frame data, wherein background fluorescence characteristics, including those associated with PBS exposure, are reflected in the acquired frame data during normal pull-back imaging and used to establish the background fluorescence threshold in situ.
Also regarding claim 1, the modified Wang 2015 does not fully teach adjusting the fluorescence data values of the one or more frames based on the background fluorescence threshold, thereby generating adjusted fluorescence data values. Rather, it teaches adjusting fluorescence data via background removal by subtracting a PBS-derived background signal, but it does not teach adjusting the fluorescence data values of the one or more frames based on a background fluorescence threshold derived from central tendency processing of the acquired frame data as recited (Wang 2015, p. 7–8, Sec. 2.2.5: “the NIRAF background signal of PBS solution was also averaged and subtracted from the coronary NIRAF intensity profile”).
Ishihara teaches threshold-based adjustment applied to a single frame image by modifying image values based on a threshold value S, including replacing values below the threshold with zero, thereby producing adjusted values for that frame (Ishihara, ¶[0065]: “the image adjuster 51 replaces the gradation values of pixels having gradation values Smaller than the threshold value S (=1575) with zero”; Ishihara, ¶[0100]: “the image adjuster 51 may adjust the gradation values of the corrected fluorescence image on the basis of the current threshold value S”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to adjust the fluorescence data values of the one or more frames based on the background fluorescence threshold by applying the threshold to the frame data and modifying values relative to that threshold. This modification is feasible because the modified Wang 2015 already produces rotation-synchronized frame data for image rendering after post-processing, and Ishihara’s adjustment is a software-side operation applied to the already-generated fluorescence image values of a frame without changing catheter structure, rotation rate, or pullback acquisition. The benefit of the combination is improved suppression of background-level fluorescence contributions within each frame while retaining lesion-related fluorescence signal, thereby improving contrast and interpretability in the resulting fluorescence images (Ishihara, ¶[0066]: “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50”).
Also regarding claim 1, the modified Wang 2015 does not fully teach generating an image based on the adjusted fluorescence data values. Rather, it teaches generating images from fluorescence data after background subtraction and normalization, including rendering the fluorescence signal as a ring-shaped image co-registered with OCT A-lines, but it does not teach generating an image based on fluorescence data values that have been adjusted using a background fluorescence threshold derived from central tendency processing of the acquired frame data as recited (Wang 2015, p. 7–8, Sec. 2.2.5).
Ishihara teaches generating and displaying a corrected fluorescence image after applying threshold-based adjustment to fluorescence image values. Specifically, Ishihara states that “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50” after the image adjuster modifies the image values “on the basis of the current threshold value S” (Ishihara, ¶[0066]; ¶[0100]). These passages teach that the adjusted fluorescence data values resulting from threshold-based processing are used as the basis for image generation and display.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to generate an image based on fluorescence data values adjusted using the background fluorescence threshold. This modification is feasible because the modified Wang 2015 already renders fluorescence images from per-rotation frame data after post-processing, and Ishihara teaches that threshold-adjusted fluorescence values are directly used to generate and display a corrected fluorescence image on a monitor. The benefit of the combination is improved visual contrast and suppression of background fluorescence in the generated images while preserving lesion-related fluorescence signal, without altering the catheter hardware, pullback procedure, or image rendering architecture.
Regarding claim 2, the modified Wang 2015 partially teaches generating the image includes generating an image of the bodily lumen based on fluorescence data values of the plurality of fluorescence data values that are larger than a value of the background fluorescence threshold. Specifically, the modified Wang 2015 renders NIRAF images after background subtraction and normalization, as shown in claim 1, but it does not specify that the rendered image is generated based on only those fluorescence data values that are larger than a background fluorescence threshold (Wang 2015, p. 7–8, Sec. 2.2.5; Wang 2015, p. 8–9, Sec. 2.2.5).
Ishihara teaches generating a corrected fluorescence image by suppressing values below a threshold value S, specifically teaching that “the image adjuster 51 replaces the gradation values of pixels having gradation values Smaller than the threshold value S (=1575) with zero”, which results in an image that is generated based on values larger than the threshold value because values lower than the threshold do not contribute to the displayed fluorescence signal (Ishihara, ¶[0065]). Ishihara further teaches displaying the resulting corrected fluorescence image on a monitor (Ishihara, ¶[0066]: “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to generate an image of the bodily lumen based on fluorescence data values that are larger than a value of the background fluorescence threshold by applying a background fluorescence threshold to suppress fluorescence data values at or below the threshold (e.g., setting values below the threshold to zero) and generating the image from the remaining values above the threshold (Ishihara, ¶[0065]; Ishihara, ¶[0066]). This modification is feasible because the modified Wang 2015 already renders fluorescence images from per-rotation frame data after post-processing, and Ishihara’s threshold-based suppression is a software-side masking operation applied to the already-generated fluorescence values without changing catheter structure, rotation rate, pullback acquisition, or image rendering architecture. The benefit of the combination is improved image contrast and robustness by suppressing background-level fluorescence contributions and emphasizing true fluorescence signal associated with lesions during display.
Regarding claim 3, the modified Wang 2015 partially teaches generating the image includes generating an image of the bodily lumen based on fluorescence data values of the plurality of fluorescence data values that are larger than a value of the background fluorescence threshold and/or removing fluorescence data values of the plurality of fluorescence data values that are lower than or equal to the value of the background fluorescence threshold. Specifically, the modified Wang 2015 renders NIRAF images after background subtraction and normalization, as shown in claim 1, but it does not specify that the rendered image is generated based on only those fluorescence data values that are larger than a background fluorescence threshold and/or that fluorescence data values lower than or equal to the background fluorescence threshold are removed (Wang 2015, p. 7–8, Sec. 2.2.5; Wang 2015, p. 8–9, Sec. 2.2.5–2.2.6).
Ishihara teaches generating a corrected fluorescence image by suppressing values below a threshold value S, specifically teaching that “the image adjuster 51 replaces the gradation values of pixels having gradation values Smaller than the threshold value S (=1575) with zero”, which removes or suppresses values at or below the threshold and yields an image that is generated based on values larger than the threshold because values lower than the threshold do not contribute to the displayed fluorescence signal (Ishihara, ¶[0065]). Ishihara further teaches displaying the resulting corrected fluorescence image on a monitor (Ishihara, ¶[0066]: “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to generate an image of the bodily lumen based on fluorescence data values that are larger than a value of the background fluorescence threshold and/or to remove fluorescence data values that are lower than or equal to the value of the background fluorescence threshold by applying a background fluorescence threshold to suppress fluorescence data values at or below the threshold (e.g., setting values below the threshold to zero) and generating the image from the remaining values above the threshold. This modification is feasible because the modified Wang 2015 already renders fluorescence images from per-rotation frame data after post-processing, and Ishihara’s threshold-based suppression is a software-side masking operation applied to the already-generated fluorescence values without changing catheter structure, rotation rate, pullback acquisition, or image rendering architecture. The benefit of the combination is improved image contrast and robustness by suppressing background-level fluorescence contributions and emphasizing true fluorescence signal associated with lesions during display.
Regarding claim 4, the modified Wang 2015 teaches that the one or more frames includes one B-scan frame (Wang 2015, p. 7, Sec. 2.2.3: “Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, Wang uses the terms “frame” and “image” to denote a cross-sectional acquisition produced by one full rotation, which corresponds to a B-scan frame in the claim); the plurality of fluorescence data values respectively corresponds to a plurality of A-lines of the one B-scan frame collected by the imaging catheter in one full revolution within the bodily lumen (Wang 2015, p. 7-8, Sec. 2.2.5–2.2.6: “The normalized NIRAF signal was rendered as a ring shaped image, where each NIRAF data point matches the corresponding OCT A-line at each given rotational position of the catheter”, this shows that NIRAF fluorescence data points (values) correspond one-to-one with OCT A-lines within a rotation-synchronized frame; p. 7, Sec. 2.2.3: “The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, this shows that each frame/image is produced during one full revolution of the catheter within the lumen); collecting the plurality of fluorescence data values includes collecting one fluorescence data value for each of the A-lines (Wang 2015, p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line”, this shows one fluorescence measurement integrated per A-line; p. 7-8, Sec. 2.2.5–2.2.6: “each NIRAF data point matches the corresponding OCT A-line”, this corroborates one data value per A-line).
Also regarding claim 4, the modified Wang 2015 does not fully teach calculating the central tendency include calculating a mean of the fluorescence data values corresponding to all of the A-lines in the one B-scan frame. Specifically, the modified Wang 2015 teaches rotation-synchronized frames (images) composed of A-lines, acquisition of a fluorescence value integrated per A-line, and one-to-one correspondence between fluorescence data values and A-lines within a single revolution, but it does not teach calculating a mean of the fluorescence data values corresponding to all of the A-lines in a single frame (Wang 2015, p. 7, Sec. 2.2.3; Wang 2015, p. 8–9, Sec. 2.2.5–2.2.6).
Ishihara teaches calculating a per-frame mean of fluorescence image values by teaching that “when the threshold-value setting unit 45 acquires a corrected fluorescence image of a Subsequent frame (step SB6), the threshold-value setting unit 45 calculates an average gradation value m of the entire image (step SB7)”, which is a central tendency (mean) computed for a single acquired frame (Ishihara, ¶[0094]). In Ishihara, the “entire image” is the image of the acquired single frame being processed (the “Subsequent frame”), rather than the entire collection of frames, because the average gradation value m is calculated upon acquisition of the Subsequent frame (Ishihara, ¶[0094]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to calculate a mean of the fluorescence data values corresponding to all of the A-lines in a single rotation-synchronized frame by calculating, for each acquired frame, an average of the frame’s fluorescence image values as a frame-level mean prior to downstream thresholding and display (Ishihara, ¶[0094]). This modification is feasible because Wang 2015 already acquires one fluorescence data value per A-line and groups those A-line values into a per-rotation frame, and Ishihara’s per-frame average calculation is a software-side statistical operation applied to the already-acquired frame data without changing catheter structure, rotation rate, pullback acquisition, or image rendering architecture. The benefit of the combination is improved robustness and comparability by providing a frame-level mean that supports consistent threshold setting and subsequent image correction and display across frames.
Regarding claim 5, as shown in claim 1, the modified Wang 2015 teaches that the one or more frames include a plurality of B-scan frames collected by the imaging catheter during an entire pullback within the bodily lumen (Wang 2015, p. 7, Sec. 2.2.3: “Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation. The pullback rate was 5 mm/s”, which teaches that the rotation-synchronized frames established in claim 1 are acquired repeatedly at a frame rate while the catheter is pulled back through the lumen, thereby yielding a plurality of B-scan frames during pullback; p. 7, Sec. 2.2.3: “The sheath was inserted into the coronary artery and the starting point of the pullback was labeled with tissue marking ink… The endpoint of the pullback was also labeled by tissue marking ink”, which teaches that the imaging pullback has defined start and end points along the bodily lumen, consistent with collecting the rotation-synchronized frames of claim 1 throughout the pullback length; p. 7, Sec. 2.2.3: “Since the start and end points of the OCT-NIRAF pullback were marked on the coronary artery tissue before cutting, the position of each histology slide was matched with OCT-NIRAF frame”, which teaches that multiple OCT-NIRAF rotation-synchronized frames were acquired along the pullback such that the frames correspond to positions spanning from the start to the end of the pullback).
Regarding claim 6, the modified Wang 2015 does not fully teach collecting the plurality of fluorescence data values includes collecting one fluorescence data value for each of the A-lines in 1% of the plurality of B-scan frames of the entire pullback, and wherein calculating the central tendency includes calculating a mean of the discrete or separate fluorescence data values in the 1% of the plurality of B-scan frames. Specifically, the modified Wang 2015 teaches (i) collecting one fluorescence data value per A-line, (ii) grouping A-lines into rotation-synchronized frames, and (iii) acquiring those frames sequentially during an entire pullback (Wang 2015, p. 7, Sec. 2.2.3). However, the modified Wang 2015 does not disclose selecting 1% of the pullback frames and calculating a mean of the fluorescence data values in that 1% subset.
Ishihara teaches that a fluorescence-image processing pipeline may set (or update) a threshold “for every several frames” rather than for every frame, such that a threshold is set only when the frame number reaches an “n-th frame” (Ishihara, ¶[0101]-[0102]). In Ishihara, the threshold-setting event is based on a per-frame mean by teaching that “the threshold value S is set on the basis of the sum of the average gradation value m of the entire image of the Subsequent frame and the standard deviation σ” (Ishihara, ¶[0102]). In Ishihara, the “entire image” refers to the acquired image of the single “Subsequent frame” being processed at that step (not the entire collection of frames), because the threshold update is triggered when the frame number i reaches the “n-th frame” for a Subsequent frame, and then the threshold is set based on that Subsequent frame’s average gradation value m (Ishihara, ¶[0101]-[0102]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to calculate a mean of discrete or separate fluorescence data values using only a subset of pullback frames (i.e., not every frame), by configuring the processor to compute a per-frame mean for periodically selected frames and to use that subset for background/threshold determination (Ishihara, ¶[0101]-[0102]). This modification is feasible because Wang 2015 already digitizes/collects fluorescence values per A-line and organizes them into sequential rotation-synchronized frames during a pullback, and Ishihara’s “every n-th frame” threshold-setting is a software-side selection of a subset of frames combined with a per-frame mean calculation.
Further, selecting a particular fraction such as “1%” corresponds to a routine choice of the update/selection frequency (i.e., setting n so that one frame out of every 100 frames is used), which is an optimization of a result-effective variable balancing computational burden and estimation stability. The benefit of the combination is reducing computation (by calculating means on only a small representative fraction of frames) while still providing a robust frame-based mean suitable for background/threshold setting within the pullback processing pipeline.
Regarding claim 7, Wang 2015 teaches that the one or more frames include one or more B-scan frames acquired by the imaging catheter in a region of low or no fluorescence within the bodily lumen (Wang 2015, Fig. 5, p. 10: “…The color map ranges from blue (low NIRAF intensity) to green, yellow and white (highest NIRAF intensity)”, this shows that some regions of the pullback image correspond to low or no fluorescence, indicating that the underlying B-scan frames also captured regions of low or no fluorescence within the lumen; p. 10, Sec. 3.1: “The x-axis of the 2D NIRAF intensity map corresponds to the longitudinal pullback position, and the y-axis, the scanning angle (i.e., 0 to 360 degrees)”, this explains that the en face map represents a series of B-scan frames acquired sequentially during pullback, confirming that some frames are located in regions of low or no fluorescence; p. 7, Sec. 2.2.3: “Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024)”, this shows the definition of a frame and supports that multiple such frames are acquired sequentially during pullback, some of which correspond to low- or no-fluorescence regions).
Regarding claim 8, the modified Wang 2015 does not fully teach the fluorescence data values includes collecting one fluorescence data value for each of the A-lines of the one or more B-scan frames, and wherein calculating the central tendency includes calculating an average of the discrete or separate fluorescence data values for each of the one or more B-scan frames, and selecting a lowest average among the averages for the one or more B-scan frames. Specifically, as set forth in claim 1 and claim 7, the modified Wang 2015 teaches collecting one fluorescence data value per A-line and organizing those A-lines into rotation-synchronized B-scan frames acquired sequentially during a pullback (Wang 2015, p. 7, Sec. 2.2.3), and further teaches that such B-scan frames include frames acquired in regions of low or no fluorescence within the bodily lumen, as evidenced by NIRAF intensity maps showing low-intensity regions along the pullback (Wang 2015, Fig. 5; Wang 2015, p. 10, Sec. 3.1). In addition, Ishihara teaches calculating a per-frame central tendency by calculating “an average gradation value m of the entire image of the Subsequent frame” (Ishihara, ¶[0070]), which together provide a framework for (i) identifying frames corresponding to low- or no-fluorescence regions and (ii) calculating a per-frame mean of discrete or separate fluorescence data values for those frames. However, the modified Wang 2015 does not disclose selecting a lowest average among the calculated per-frame averages of the one or more B-scan frames from the low/np fluorescence areas as the background fluorescence threshold.
Ishihara further teaches that the calculation of per-frame averages and the setting of a threshold value are not required to be performed continuously for every acquired frame, but instead may be selectively enabled, disabled, or updated under operator control. Specifically, Ishihara teaches embodiments in which a threshold value is calculated based on a per-frame average and then maintained without recalculation for subsequent frames, as well as embodiments in which the threshold value is updated only when certain conditions are met or when instructed by the operator (Ishihara, ¶[0098]–[0102]). These teachings establish that Ishihara’s processing pipeline supports using a subset of frames to determine a threshold value and then applying that threshold to subsequent frames without further averaging.
In this framework, a clinician may allow per-frame average calculations to occur while imaging a region identified as having low or no fluorescence, as taught by Wang 2015’s NIRAF pullback maps showing regions of low NIRAF intensity along the pullback (Wang 2015, Fig. 5; Wang 2015, p. 10, Sec. 3.1), and then disable further threshold updating once sufficient background frames have been acquired. The per-frame averages calculated during this background-dominant region therefore define a candidate set of average values corresponding to low- or no-fluorescence frames.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to select, from among the per-frame averages calculated during such a clinician-selected background region, a lowest average as the background fluorescence threshold. This selection does not alter Ishihara’s principle of operation because Ishihara already (i) uses a per-frame average as the decision statistic for threshold setting and (ii) permits operator-controlled threshold updating and holding; selecting the lowest average among values already calculated within Ishihara’s framework is merely a conservative selection criterion applied to those existing values.
Ughi 2016 further confirms, in the same OCT–NIRAF field, the routine practice of using the minimum signal derived from an acquired dataset as a background reference for normalization (Ughi 2016, p. 4, DATA PROCESSING), reinforcing that selecting the lowest per-frame average from background frames as the background fluorescence threshold is consistent with established fluorescence imaging practices.
The benefit of the combination is improved robustness in subsequent fluorescence image thresholding and contrast enhancement by allowing a clinician to derive a conservative background fluorescence threshold from frames acquired in low- or no-fluorescence regions and to apply that threshold consistently during subsequent imaging, while retaining the background removal processing taught by the modified Wang 2015.
Regarding claim 9, the modified Wang 2015 does not fully teach that the one or more frames includes a first B-scan frame acquired by the imaging catheter from a first region within the bodily lumen, wherein collecting the plurality of fluorescence data values includes collecting one fluorescence data value for each of the A-lines of the first B-scan frame acquired from the first region, and wherein calculating the central tendency includes calculating an average of the discrete or separate fluorescence data values for the first B-scan frame. As shown in claim 1, the modified Wang 2015 already teaches catheter pullback imaging that yields rotation-synchronized frames along the bodily lumen and fluorescence data values aligned to individual A-lines within those frames (Wang 2015, p. 7, Sec. 2.2.3; Wang 2015, p. 8, Sec. 2.2.5). It further teaches that frames acquired during pullback correspond to different longitudinal positions within the lumen, i.e., different regions, as reflected by the pullback axis of the two-dimensional NIRAF intensity map (Wang 2015, p. 10: “The x-axis of the 2D NIRAF intensity map corresponds to the longitudinal pullback position”), and illustrates using a specific pullback location as a region of interest associated with a specific OCT-NIRAF frame (Wang 2015, p. 10: “taken at the location of the dashed line in Fig. 5”). However, while the modified Wang 2015 establishes that frames inherently correspond to different regions within the bodily lumen, it does not teach, as an explicit processing choice, selecting a particular region-designated frame, such as a first region, and applying the per-frame central tendency calculation specifically to the B-scan frame acquired from that first region.
Ishihara teaches that frame-level threshold calculations are not required to be applied uniformly to all frames, but may instead be selectively applied to particular frames as observation proceeds. In particular, Ishihara teaches configuring threshold setting to occur “for every several frames” and resetting the threshold when a frame number reaches an “n-th frame” (Ishihara, ¶[0101]: “a threshold value may be set for every several frames”, “the threshold value should be changed once when a frame number i reaches an n-th frame”; ¶[0102]: “when the frame number i of a Subsequent frame … reaches the n-th frame … steps SB2 to SD7 may be repeated”). Ishihara further teaches that the statistic used for such threshold setting is an average computed for the “entire image of the Subsequent frame,” meaning the entire image of an individual frame rather than an entire multi-frame pullback dataset (Ishihara, ¶[0070]: “calculates an average gradation value m of the entire image of the Subsequent frame”). Together, these teachings demonstrate that it is appropriate to designate particular frames, including an initial or first set of frames corresponding to an initial region during observation, as the frames to which the average-based central tendency calculation is applied. Ishihara therefore supplies the missing teaching that frame-level averaging may be selectively applied to frames associated with a designated region, consistent with selecting a first region in Wang’s pullback and applying the average calculation to the B-scan frame acquired from that region.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to select a first region within the bodily lumen during pullback and to calculate the central tendency as an average for the rotation-synchronized B-scan frame acquired from that first region. This modification is feasible because the modified Wang 2015 already associates frames with pullback position and collects discrete A-line-aligned fluorescence values within each frame, and Ishihara teaches frame-indexed processing in which average-based calculations are performed only at selected frames, such as at initial frames or at frames designated by progression of observation. The benefit is improved robustness of threshold determination by reducing the influence of localized outliers and pullback-to-pullback variability through region-specific frame selection for central tendency calculation, thereby improving consistency of fluorescence quantification across different lumen regions.
Regarding claim 10, the modified Wang 2015 does not fully teach that the one or more frames further include a second B-scan frame acquired by the imaging catheter from a second region different from the first region within the bodily lumen, wherein collecting the plurality of fluorescence data values further includes collecting one fluorescence data value for each of the A-lines of the second B-scan frame acquired from the second region, wherein calculating the central tendency further includes calculating an average of the discrete or separate fluorescence data values for the second B-scan frame, and wherein assigning the central tendency of one of the one or more frames as the background fluorescence threshold includes assigning a lowest central tendency among the central tendencies of the first B-scan frame and the second B-scan frame as the background fluorescence threshold. As shown in claim 1, the modified Wang 2015 already teaches catheter pullback imaging that yields rotation-synchronized frames along the bodily lumen and fluorescence data values aligned to individual A-lines within those frames (Wang 2015, p. 7, Sec. 2.2.3; Wang 2015, p. 8, Sec. 2.2.5). As further shown in claim 9, the modified Wang 2015 teaches that frames correspond to longitudinal pullback position, which is a spatial region within the lumen (Wang 2015, p. 10: “The x-axis of the 2D NIRAF intensity map corresponds to the longitudinal pullback position”), and illustrates that different pullback locations correspond to different frames associated with different regions (Wang 2015, p. 10: “taken at the location of the dashed line in Fig. 5”; “taken at the location of the dotted line in Fig. 5”). However, the modified Wang 2015 does not teach the specific decision logic of comparing central tendencies calculated for frames acquired at different longitudinal pullback positions and assigning the lowest of those frame-level central tendencies as the background fluorescence threshold.
Ishihara teaches performing a frame-level average calculation for an individual frame image (Ishihara, ¶[0070]: “calculates an average gradation value m of the entire image of the Subsequent frame”), and further teaches that threshold setting may be performed selectively at particular frames rather than indiscriminately for every frame, including resetting the threshold at defined frame intervals as observation proceeds (Ishihara, ¶[0101]: “a threshold value may be set for every several frames”; ¶[0101]: “the threshold value should be changed once when a frame number i reaches an n-th frame”; ¶[0102]: “when the frame number i of a Subsequent frame … reaches the n-th frame … steps SB2 to SD7 may be repeated”). Ishihara is relied upon here solely for the teachings of calculating a frame-level central tendency for an individual frame and selectively determining which frames are used to update threshold calculations, and does not define or rely on spatial regions within the lumen.
Ughi 2016 teaches that quantitative NIRAF data may be normalized using minimum values acquired across imaging data, demonstrating the use of a lowest-value reference as a conservative baseline for fluorescence signal evaluation (Ughi 2016, p. 1306, DATA PROCESSING: “Quantitative NIRAF data were normalized between 0 and 1 using the minimum and maximum NIRAF values acquired in the study”). Accordingly, once per-frame averages are calculated for frames acquired at different longitudinal pullback positions in the modified Wang 2015 using the frame-level averaging taught by Ishihara, it would have been an expected and routine selection operation to compare the resulting computed frame-level central tendencies and assign the lowest value as a conservative background fluorescence threshold, consistent with Ughi’s use of minimum-based normalization, and without altering Ishihara’s frame-level processing because Ishihara is relied upon only to compute the per-frame average and to support performing that computation at selected frames.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified Wang 2015 in view of Ishihara and Ughi 2016 to assign a lowest central tendency among the central tendencies of the first B-scan frame and the second B-scan frame as the background fluorescence threshold. This modification is feasible because the modified Wang 2015 already produces sequential, region-correlated frames with per-A-line fluorescence values, Ishihara teaches calculating frame-level averages and selectively updating threshold calculations at designated frames during observation, and Ughi 2016 demonstrates using minimum values from acquired NIRAF data as a conservative low reference. The benefit is improved reliability and stability of background fluorescence estimation across spatially distinct lumen regions by reducing sensitivity to localized fluorescence elevations, plaque-related signal variation, and transient acquisition artifacts, thereby improving consistency of fluorescence quantification as the catheter progresses through different regions of the bodily lumen.
Regarding claim 11, Wang 2015 teaches that a catheter-based imaging system for quantifying fluorescence emission from a bodily lumen, the system comprising: an imaging catheter that scans the bodily lumen with light of a wavelength capable of stimulating emission of fluorescence from within the bodily lumen (Wang 2015, Abstract: “In this study, we used a recently developed imaging system and double-clad fiber (DCF) catheter capable of simultaneously acquiring both OCT and red excited near-infrared autofluorescence (NIRAF) images (excitation: 633 nm, emission: 680nm to 900nm)... We found that NIRAF is elevated in lesions that contain necrotic core – a feature that is critical for vulnerable plaque diagnosis... These results suggest that multimodality intracoronary OCT-NIRAF imaging technology may be used in the future to provide improved characterization of coronary artery disease in human patients”, this shows Wang 2015 teaches a catheter-based imaging system for quantitative fluorescence characterization in a bodily lumen; p. 4, Sec. 2.1.1: “The multimode inner cladding (NA ≥ 0.46, diameter = 124–126 µm, fiber outer diameter = 250 µm) was used to guide 633 nm excitation light and to collect NIRAF tissue emission. The working distances for OCT and NIRAF were 2 mm and 0.5 mm, respectively. The focal spot size for OCT and NIRAF was 27 µm and 100 µm, respectively”, this teaches that the catheter transmits excitation light at 633 nm to stimulate fluorescence emission from within tissue regions of the bodily lumen); and a processor configured to: (Wang 2015, p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line. Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, this teaches digital acquisition and grouping of sampled fluorescence signals into rotation-synchronized frames, which necessarily requires a processor to buffer, integrate, and assemble discrete A-line data into image frames; p. 7–8, Sec. 2.2.5: “The OCT background images of PBS solution were averaged and subtracted from each OCT image of the coronary artery; the NIRAF background signal of PBS solution was also averaged and subtracted from the coronary NIRAF intensity profile”, this expressly teaches computational processing steps including averaging and subtraction of fluorescence data values, which are operations performed by a processor; p. 8, Sec. 2.2.6: “The NIRAF intensity was normalized by the maximum intensity among the selected coronary tissue sites. Differences in normalized NIRAF intensities between all lesion types were analyzed using one-way ANOVA. Differences between two different lesion types were analyzed using a one-tailed Student’s t-test”, this teaches normalization and statistical analysis of fluorescence data values, which necessarily require execution of processing algorithms by a processor; Wang 2015’s disclosure of frame construction, averaging, subtraction, normalization, and statistical testing of digitized fluorescence signals necessarily requires a processor executing stored instructions, and therefore teaches a processor configured to perform the claimed functions under the broadest reasonable interpretation); collect a plurality of fluorescence data values corresponding to the fluorescence emitted from different regions within the bodily lumen (Wang 2015, p. 8, Sec. 2.2.6: “A total of 15 coronary arteries from 5 human cadaver hearts were imaged using the multimodality OCT-NIRAF system and catheter. From these 15 coronary arteries, we analyzed NIRAF intensity from 37 distinct artery wall regions... Individual NIRAF intensity data points were selected from tissue sites on the coronary ROIs... A total of 1200, 1253, 1491, 1554 sites were selected from NC, PIT, CA, and IH regions, respectively”, this shows Wang 2015 teaches collecting multiple fluorescence data values corresponding to emission from different regions within the bodily lumen); group the plurality of collected fluorescence data values into one or more frames, each frame of the one or more frames having a number of discrete or separate fluorescence data values of the plurality of fluorescence data values collected from one full rotation of the imaging catheter within the bodily lumen (Wang 2015, p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line. Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, which teaches that each frame comprises individual A-line-based fluorescence measurements that are separately acquired within a single rotation, such that the fluorescence data values within a frame are discrete from one another and from fluorescence data values of other frames); and the discrete or separate fluorescence data values of one frame of the one or more frames being discrete or separate from fluorescence data values of each of the other frames of the one or more frames (Wang 2015, p. 7, Sec. 2.2.3: “Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, this teaches that each frame is a distinct set of A-line based data acquired during a single rotation, such that the fluorescence data values of one frame are separate from the fluorescence data values of other frames corresponding to other rotations).
Also regarding claim 11, Wang 2015 does not fully teach calculate a central tendency of the discrete or separate fluorescence data values in each of the one or more frames. Rather, Wang 2015 teaches collecting fluorescence intensity data and performing normalization (including normalizing by a maximum intensity), but Wang 2015 does not teach calculating a central tendency (e.g., an average/mean) of the discrete or separate fluorescence data values in each frame collected from one full rotation of the imaging catheter (Wang 2015, p. 8, Sec. 2.2.6: “The NIRAF intensity was normalized by the maximum intensity among the selected coronary tissue sites”).
Ishihara teaches calculating an “average” over the “entire image” of a “frame”, namely calculating an “average gradation value m of the entire image of the Subsequent frame”, which is a central tendency calculation applied to the discrete values that make up that single acquired frame (Ishihara, ¶[0070]: “the threshold-value setting unit 45 calculates an average gradation value m of the entire image of the Subsequent frame on the basis of calculation equation (2)”). In Ishihara, the ‘entire image’ is the image of the acquired single frame being processed (i.e., ‘the Subsequent frame’), not the entire collection of frames, because the calculation is performed upon acquisition of ‘a corrected fluorescence image of a Subsequent frame’ and the calculated value is stored and compared on a frame-to-frame basis” (Ishihara, ¶[0094]: “when the threshold-value setting unit 45 acquires a corrected fluorescence image of a Subsequent frame (step SB6), the threshold-value setting unit 45 calculates an average gradation value m of the entire image (step SB7), and compares it with the stored average gradation value m of the entire image of the previous frame (step SB8)”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang 2015 in view of Ishihara to cause the processor of Wang 2015 to calculate a central tendency of the discrete or separate fluorescence data values in each of the one or more frames by calculating an average (mean) value for the fluorescence data values of each frame. This modification would have been possible because Wang 2015 already forms rotation-synchronized frames from many discrete fluorescence measurements per frame, and Ishihara’s teaching is expressly implemented on a per-frame basis (“entire image of the Subsequent frame”), such that applying the same per-frame average computation to Wang’s per-rotation frames is a routine software-side calculation applied to already-collected frame data (Ishihara, ¶[0070]: “average gradation value m of the entire image of the Subsequent frame”). The benefit of the combination is that a frame-level central tendency provides a consistent statistical basis for subsequent frame-level processing and threshold-related processing, improving robustness to frame-to-frame variation while remaining within the existing catheter imaging processing pipeline (Ishihara, ¶[0072]: “the threshold value S is updated on the basis of the average gradation value m of the entire image”).
Also regarding claim 11, the modified Wang 2015 does not fully teach assign the central tendency of one of the one or more frames as a background fluorescence threshold by determining a lowest central tendency of the one or more frames and assigning the lowest central tendency as the background fluorescence threshold. Rather, the modified Wang 2015 teaches collecting fluorescence data in rotation-synchronized frames and performing background removal using PBS solution and performing maximum-based normalization, but it does not teach determining a lowest central tendency among the one or more frames and assigning that lowest central tendency as the background fluorescence threshold (Wang 2015, p. 7–8, Sec. 2.2.5; Wang 2015, p. 8, Sec. 2.2.6).Ishihara teaches that per-frame central tendency values are calculated and stored for comparison, and that threshold setting is not required for every subsequent frame because the threshold value S can be maintained and used for subsequent adjustment unless a reset is triggered. For example, Ishihara teaches that “the threshold-value setting unit 45 calculates an average gradation value m of the entire image (step SB7), and compares it with the stored average gradation value m of the entire image of the previous frame (step SB8)” (where "entire image" = frame)(Ishihara, ¶[0094]). Ishihara further teaches that under stable conditions, “the threshold value S can be maintained so that the calculation amount of the standard deviation O can be reduced” (Ishihara, ¶[0097]). Ishihara also teaches an operator-controlled approach where “an operator may manually change the threshold value by operating the threshold-value change command unit 261”, and “instead of always setting a threshold value every time a corrected fluorescence image of a Subsequent frame is generated, the corrected fluorescence image can be adjusted when the operator determines that the threshold value is not appropriate during observation” (Ishihara, ¶[0098]). Ishihara then teaches that if no change command is input, “the image adjuster 51 may adjust the gradation values of the corrected fluorescence image on the basis of the current threshold value S” and the process returns for subsequent frames (Ishihara, ¶[0100]). These teachings establish that Ishihara’s processing pipeline supports selecting a threshold value S based on a subset of frames by permitting operator-controlled cessation of threshold updating, which inherently defines the set of frames used to establish the operative threshold value S (Ishihara, ¶[0097]; ¶[0098]; ¶[0100]). Under Ishihara’s framework, maintaining a stable threshold value S presupposes that the selected threshold represents background-dominant conditions, such that subsequent frames need not trigger recalculation; selecting the lowest per-frame average from an initial background-dominant subset directly satisfies this stability criterion.
Ughi 2016 teaches that quantitative fluorescence processing routinely uses the lowest acquired fluorescence level as a reference derived from the acquired dataset itself, specifically teaching normalization using the minimum acquired NIRAF value (Ughi 2016, p. 1306, DATA PROCESSING: “Quantitative NIRAF data were normalized between 0 and 1 using the minimum and maximum NIRAF values acquired in the study”). This supports the routine understanding that the lowest acquired fluorescence signal corresponds to background conditions and is suitable for defining a background reference level when background information is derived from the acquired data.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara and Ughi 2016 to assign the central tendency of one of the one or more frames as a background fluorescence threshold by determining a lowest central tendency of the one or more frames and assigning the lowest central tendency as the background fluorescence threshold. This combination does not change the principle of operation of Ishihara because Ishihara already calculates and stores a per-frame average gradation value m and uses a threshold value S as the operative basis for subsequent adjustment, and the modification is limited to selecting, from among the already-calculated per-frame average gradation values m that Ishihara already computes and stores, a conservative baseline corresponding to the lowest such per-frame average observed during frames acquired in a background-dominant region during frames acquired in a background-dominant region, and then using that corresponding threshold value as the current threshold value S for subsequent adjustment (Ishihara, ¶[0094], ¶[0100]). Ughi 2016 confirms the routine understanding in fluorescence imaging that the lowest acquired signal corresponds to background conditions and is suitable as a reference level by expressly teaching minimum-based normalization (Ughi 2016, p. 1306, DATA PROCESSING). The modification is feasible because it is a software-side selection step applied to values (central tendencies) that Ishihara already calculates and stores on a per-frame basis, and because the modified Wang 2015 already collects rotation-synchronized frames suitable for frame-level statistics and threshold-based correction.
The benefit of the combination is improved robustness in subsequent fluorescence image thresholding and contrast enhancement by selecting a conservative background fluorescence threshold from the acquired frame data, wherein background fluorescence characteristics, including those associated with PBS exposure, are reflected in the acquired frame data during normal pull-back imaging and used to establish the background fluorescence threshold in situ.
Also regarding claim 11, the modified Wang 2015 does not fully teach adjust the fluorescence data values of the one or more frames based on the background fluorescence threshold, thereby generating adjusted fluorescence data values. Rather, it teaches adjusting fluorescence data via background removal by subtracting a PBS-derived background signal, but it does not teach adjusting the fluorescence data values of the one or more frames based on a background fluorescence threshold derived from central tendency processing of the acquired frame data as recited (Wang 2015, p. 7–8, Sec. 2.2.5: “the NIRAF background signal of PBS solution was also averaged and subtracted from the coronary NIRAF intensity profile”).
Ishihara teaches threshold-based adjustment applied to a single frame image by modifying image values based on a threshold value S, including replacing values below the threshold with zero, thereby producing adjusted values for that frame (Ishihara, ¶[0065]: “the image adjuster 51 replaces the gradation values of pixels having gradation values Smaller than the threshold value S (=1575) with zero”; Ishihara, ¶[0100]: “the image adjuster 51 may adjust the gradation values of the corrected fluorescence image on the basis of the current threshold value S”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to adjust the fluorescence data values of the one or more frames based on the background fluorescence threshold by applying the threshold to the frame data and modifying values relative to that threshold. This modification is feasible because the modified Wang 2015 already produces rotation-synchronized frame data for image rendering after post-processing, and Ishihara’s adjustment is a software-side operation applied to the already-generated fluorescence image values of a frame without changing catheter structure, rotation rate, or pullback acquisition. The benefit of the combination is improved suppression of background-level fluorescence contributions within each frame while retaining lesion-related fluorescence signal, thereby improving contrast and interpretability in the resulting fluorescence images (Ishihara, ¶[0066]: “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50”).
Also regarding claim 11, the modified Wang 2015 does not fully teach generate an image based on the adjusted fluorescence data values. Rather, it teaches generating images from fluorescence data after background subtraction and normalization, including rendering the fluorescence signal as a ring-shaped image co-registered with OCT A-lines, but it does not teach generating an image based on fluorescence data values that have been adjusted using a background fluorescence threshold derived from central tendency processing of the acquired frame data as recited (Wang 2015, p. 7–8, Sec. 2.2.5).
Ishihara teaches generating and displaying a corrected fluorescence image after applying threshold-based adjustment to fluorescence image values. Specifically, Ishihara states that “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50” after the image adjuster modifies the image values “on the basis of the current threshold value S” (Ishihara, ¶[0066]; ¶[0100]). These passages teach that the adjusted fluorescence data values resulting from threshold-based processing are used as the basis for image generation and display.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to generate an image based on fluorescence data values adjusted using the background fluorescence threshold. This modification is feasible because the modified Wang 2015 already renders fluorescence images from per-rotation frame data after post-processing, and Ishihara teaches that threshold-adjusted fluorescence values are directly used to generate and display a corrected fluorescence image on a monitor. The benefit of the combination is improved visual contrast and suppression of background fluorescence in the generated images while preserving lesion-related fluorescence signal, without altering the catheter hardware, pullback procedure, or image rendering architecture.
Regarding claim 12, the modified Wang 2015 teaches that the fluorescence data values correspond to one or more of an intensity, an amplitude, and a lifetime of the fluorescence (Wang 2015, p.3, Sec. 2: “Scanning the tissue en face generated a NIRAF intensity map”, p. 7–8, Sec. 2.2.5–2.2.6: “The normalized NIRAF signal was rendered as a ring shaped image, where each NIRAF data point matches the corresponding OCT A-line at each given rotational position of the catheter”, this shows that NIRAF signal values are the measured fluorescence intensity used for image generation; p. 4-5, Sec. 2.1.2: “The collected NIRAF light (emission wavelength range 675nm to 900nm) was focused onto a photomultiplier tube (PMT, model H-5784, Hamamatsu, Japan) … The PMT output was digitized by a data acquisition board (PCA-6110, National Instruments, Texas, USA)”, this shows acquisition of fluorescence signal values for quantitation, i.e., intensity).
Regarding claim 13, the modified Wang 2015 teaches that the fluorescence data values include N detected fluorescence data values, where N is a positive integer greater than 2 (Wang 2015, p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line. Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation”, this teaches that for each rotation-synchronized frame the system detects a plurality of discrete fluorescence data values corresponding to individual A-lines, such that N is a positive integer greater than 2).
Also regarding claim 13, the modified Wang 2015 does not fully teach that the processor calculates one or more of an average, a mean, or a median as the central tendency of the discrete or separate fluorescence data values. The modified Wang 2015, as established in claim 11, teaches detecting and processing a large number of discrete fluorescence data values per rotation-synchronized frame (1,024 A-lines per frame), grouping those values into frames corresponding to full catheter rotations, and operating on those frame-level fluorescence datasets for background handling and image generation, thereby satisfying the requirement that the fluorescence data values include N detected values where N is a positive integer greater than 2 (Wang 2015, p. 7, Sec. 2.2.3; p. 7–8, Sec. 2.2.5). It was additionally shown to teach using an average (mean) for a frame as a central tendency by applying Ishihara’s per-frame “average gradation value m of the entire image of the Subsequent frame” to Wang’s rotation-synchronized frames (Ishihara, ¶[0070]; Wang 2015, p. 7, Sec. 2.2.3).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to cause the processor to calculate one or more of an average, a mean, or a median as the central tendency of the detected fluorescence data values for a rotation-synchronized frame. This modification is feasible because the modified Wang 2015 already forms frames from many discrete fluorescence measurements, and Ishihara teaches a per-frame average computation applied to the entire image of a frame using routine image-processing operations. The predictable benefit of this modification is a statistically representative frame-level value that improves robustness and consistency in subsequent fluorescence processing and threshold-related operations without altering the catheter hardware or acquisition procedure.
Regarding claim 14, the modified Wang 2015 partially teaches that the processor is further configured to: discard the fluorescence data values that are equal to or lower than the background fluorescence threshold; and generate the image based on the fluorescence data values that are larger than a value of the background fluorescence threshold. Specifically, the modified Wang 2015 teaches collecting and processing fluorescence data values for catheter-based imaging, including performing background removal by subtracting a background signal and rendering the processed NIRAF signal as an image that is co-registered to OCT A-lines, as established in claim 11 (Wang 2015, p. 7–8, Sec. 2.2.5; Wang 2015, p. 8–9, Sec. 2.2.5–2.2.6). However, the modified Wang 2015 does not specify discarding fluorescence data values that are equal to or lower than a background fluorescence threshold and generating the image based on only those fluorescence data values that are larger than the background fluorescence threshold.
Ishihara teaches generating a corrected fluorescence image by suppressing values at or below a threshold value S, specifically teaching that “the image adjuster 51 replaces the gradation values of pixels having gradation values Smaller than the threshold value S (=1575) with zero”, which discards or suppresses subthreshold fluorescence-related values so the displayed corrected fluorescence image is generated based on values above the threshold (Ishihara, ¶[0065]). Ishihara further teaches that the corrected fluorescence image is displayed with increased contrast after this threshold-based suppression (Ishihara, ¶[0066]: “a new corrected fluorescence image with increased contrast between the area displaying the lesion and the area displaying the background is displayed on the monitor 50”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified Wang 2015 in view of Ishihara to configure the processor to discard fluorescence data values that are equal to or lower than the background fluorescence threshold and to generate the image based on the fluorescence data values that are larger than the background fluorescence threshold by applying a background fluorescence threshold to suppress fluorescence data values at or below the threshold (e.g., setting values at or below the threshold to zero) and generating the image from the remaining values above the threshold. This modification is feasible because the modified Wang 2015 already renders fluorescence images from per-rotation frame data after post-processing, and Ishihara’s threshold-based suppression is a software-side masking operation applied to already-generated fluorescence values without changing catheter structure, rotation rate, pullback acquisition, or image rendering architecture. The benefit of the combination is improved image contrast and robustness by suppressing background-level fluorescence contributions and emphasizing fluorescence signal associated with lesions during display.
Regarding claim 15, the modified Wang 2015 does not fully teach that the processor assigns the central tendency of at least a portion of the discrete or separate fluorescence data values as the background fluorescence threshold of all of the discrete or separate fluorescence data values. Rather, the modified Wang 2015 teaches acquiring fluorescence data values that are matched to OCT A-lines within rotation-synchronized frames, deriving a background fluorescence threshold from acquired fluorescence image data, and applying that threshold at a frame level during image adjustment and rendering, as established in claim 11. However, the modified Wang 2015 does not explicitly teach expanding the application of a background fluorescence threshold derived from only a portion of the acquired fluorescence data values such that the same threshold governs all discrete or separate fluorescence data values across the dataset, rather than being applied to "one or more frames".
Ishihara teaches that a threshold value S may be maintained and continuously applied to subsequent frames unless a recalculation is needed or the operator decides to change it. Specifically, Ishihara teaches that a corrected fluorescence image can be acquired “without having to reset the threshold value S every time a corrected fluorescence image is generated” and that “unless the average value of the gradation values of the pixels changes significantly, the threshold value S can be maintained” (Ishihara, ¶[0097]). Ishihara further teaches an operator-controlled approach where “instead of always setting a threshold value every time a corrected fluorescence image of a Subsequent frame is generated… an operator may manually change the threshold value”, and that if no change command is input, “the image adjuster 51 may adjust the gradation values… on the basis of the current threshold value S… and the process may return to step SB6”, which teaches continuing to apply the current threshold value S across subsequent frames until the operator decides to change it (Ishihara, ¶[0098]-[0100]). These teachings collectively establish deriving a threshold from a portion of acquired data and applying that resulting threshold value S across subsequent processing so that it governs adjustment of the fluorescence data values beyond the originating frame(s).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to assign the central tendency of at least a portion of the discrete or separate fluorescence data values as the background fluorescence threshold of all of the discrete or separate fluorescence data values by deriving a threshold from selected frame data and extending the application of that threshold beyond individual frames so that it governs adjustment of the fluorescence data values across the dataset. This modification is feasible because, as established in claim 11, the modified Wang 2015 already incorporates Ishihara’s in situ, frame-based thresholding framework (including maintaining and applying a current threshold value S across subsequent frames), and the present modification further configures that existing processing to apply a threshold derived from a selected portion of acquired data so that it governs adjustment of all discrete or separate fluorescence data values across the dataset without changing catheter hardware, rotation rate, pullback acquisition, or the underlying thresholding principle of operation. The benefit of the combination is improved consistency and robustness of fluorescence image interpretation across an entire pullback by avoiding frame-to-frame fluctuation in background reference levels, reducing sensitivity to transient noise or local signal variation, and ensuring that all fluorescence data values are evaluated against a common, data-derived background fluorescence threshold established from representative acquired data.
Regarding claim 16, the modified Wang 2015 teaches that the processor (i) receives a first N detected discrete or separate fluorescence values. (Wang 2015, p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line. Each OFDI frame consisted of 1,024 A-lines, with a frame rate of 39 frames/sec (40 kHz/1024). The rotary junction was operated at 39 rotations per second so each OFDI image covered one rotation.”; Wang 2015, p. 6, Sec. 2.2.2: “Signals from the dual balanced detector and the PMT were simultaneously digitized for subsequent processing.”), but does not explicitly teach (ii) calculates a mean of the first N detected discrete or separate fluorescence values. As established in claim 11, the modified Wang 2015 already incorporates Ishihara’s frame-based thresholding framework, including calculating a frame-level average (mean) of detected fluorescence values and maintaining that value for subsequent adjustment unless a change is required. However, the modified Wang 2015 does not explicitly articulate using the first-received subset of detected fluorescence values as an initialization set from which an initial mean is calculated and stored as a reference for later comparison.
Ishihara teaches that when a corrected fluorescence image of a Subsequent frame is acquired, “the threshold-value setting unit 45 calculates an average gradation value m of the entire image” and stores that value for comparison with subsequently calculated averages (Ishihara, ¶[0083]; ¶[0094]). This establishes a processing paradigm in which an initially calculated mean value serves as a baseline against which later-calculated means are compared to determine whether threshold updating is required.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have configured the modified Wang 2015 in view of Ishihara to calculate a mean of a first N detected discrete or separate fluorescence values and to treat that mean as an initial reference value for subsequent comparison and update logic. This modification is feasible because the modified Wang 2015 already receives fluorescence values sequentially during acquisition and organizes them into frame-level datasets suitable for averaging, and Ishihara already teaches calculating and storing an average value for comparison with later-calculated averages. Selecting the first N detected values as the initialization set is a routine software-side choice that does not alter acquisition timing, catheter structure, or thresholding principles. The benefit of the combination is improved stability and predictability during early-stage processing by establishing a well-defined initial mean value from acquired fluorescence data, which supports robust comparison and update decisions in subsequent processing steps.
Regarding claim 17, the modified Wang 2015 does not fully teach that the processor (i) sets the mean of the first N detected discrete or separate fluorescence values as the background fluorescence threshold for a first frame of a pullback procedure, (ii) compares the mean of the first N detected discrete or separate fluorescence values with a second mean of a second N detected discrete or separate fluorescence values to determine whether the second mean is lower than the mean, and (iii) in a case where the second mean is lower than the mean, sets the second mean as the background fluorescence threshold. As established in claim 16, the modified Wang 2015 teaches receiving a first N detected discrete or separate fluorescence values and calculating a mean of the first N detected discrete or separate fluorescence values as part of its in situ fluorescence image processing framework. However, the modified Wang 2015 does not expressly teach the particular update rule of comparing that first mean to a second mean computed from a second N detected discrete or separate fluorescence values and, when the second mean is lower, setting the second mean as the background fluorescence threshold.
Ishihara teaches a frame-to-frame comparison-and-update framework in which, after a threshold value S is set for a frame and a frame-level average is stored, a subsequent frame-level average is calculated and compared to the stored prior average (Ishihara, ¶[0093]-[0094]). Ishihara further teaches that when the subsequent frame-level average is sufficiently lower than the stored prior average, the threshold-setting process is repeated and “a new threshold value S is set and a new average gradation value m is stored” (Ishihara, ¶[0095]). These teachings provide the missing processing principle that a later-computed mean value may be compared against a previously stored mean value and, when the later mean is lower, the operative background threshold value is updated to reflect that lower condition, with the updated threshold then applied to subsequent processing (Ishihara, ¶[0096]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to compare the mean of the first N detected discrete or separate fluorescence values with a second mean of a second N detected discrete or separate fluorescence values and, in a case where the second mean is lower than the mean, set the second mean as the background fluorescence threshold. This modification is feasible because the modified Wang 2015 already processes fluorescence data in sequential frames during pullback and already calculates and applies a mean-based background fluorescence threshold, and Ishihara teaches the software-side logic of calculating a subsequent mean, comparing it to a stored prior mean, and updating the operative threshold when the subsequent mean indicates a lower-background condition (Ishihara, ¶[0093]-[0096]). The benefit of the combination is improved reliability and stability of background fluorescence thresholding during pullback by allowing the background threshold to adapt downward when later-acquired data indicate a lower-background region, thereby reducing false elevation of background reference levels and improving consistency of fluorescence quantification across frames.
Regarding claim 17, the modified Wang 2015 does not fully teach that the processor (i) sets the mean of the first N detected discrete or separate fluorescence values as the background fluorescence threshold for a first frame of a pullback procedure, (ii) compares the mean of the first N detected discrete or separate fluorescence values with a second mean of a second N detected discrete or separate fluorescence values to determine whether the second mean is lower than the mean, and (iii) in a case where the second mean is lower than the mean, sets the second mean as the background fluorescence threshold. As established in claim 16, the modified Wang 2015 teaches receiving a first N detected discrete or separate fluorescence values and calculating a mean of the first N detected discrete or separate fluorescence values as part of its in situ fluorescence image processing framework. However, the modified Wang 2015 does not expressly teach the particular update rule of comparing that first mean to a second mean computed from a second N detected discrete or separate fluorescence values and, when the second mean is lower, setting the second mean as the background fluorescence threshold.
Ishihara teaches a previously-stored mean versus subsequently-computed mean comparison-and-update framework in which, after a threshold value S is set for a frame and a frame-level average is stored, a subsequent frame-level average is calculated and compared to the stored prior average (Ishihara, ¶[0093]-[0094]). Ishihara further teaches that when the subsequent frame-level average is sufficiently lower than the stored prior average, the threshold-setting process is repeated and “a new threshold value S is set and a new average gradation value m is stored” (Ishihara, ¶[0095]). These teachings provide the missing processing principle that a later-computed mean value may be compared against a previously stored mean value and, when the later mean is lower, the operative background threshold value is updated to reflect that lower condition, with the updated threshold then applied to subsequent processing (Ishihara, ¶[0096]).
In the modified Wang 2015 pullback context, the first N detected discrete or separate fluorescence values can be taken from the beginning of the pullback stream for establishing the background fluorescence threshold used for processing of a first frame, while the second N detected discrete or separate fluorescence values can be taken from subsequently detected values during the pullback for determining whether a lower mean exists that should replace the background fluorescence threshold for that first frame.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to compare the mean of the first N detected discrete or separate fluorescence values with a second mean of a second N detected discrete or separate fluorescence values and, in a case where the second mean is lower than the mean, set the second mean as the background fluorescence threshold. This modification is feasible because the modified Wang 2015 already calculates and applies a mean-based background fluorescence threshold from detected fluorescence values acquired during pullback, and Ishihara teaches the software-side logic of calculating a subsequently-computed mean, comparing it to a previously stored mean, and updating the operative threshold when the subsequently-computed mean indicates a lower-background condition (Ishihara, ¶[0093]-[0096]). The benefit of the combination is improved robustness of the background reference used for fluorescence quantification during pullback by preventing an overly high initial background estimate from persisting when later-acquired data show a lower-background condition, thereby reducing false elevation of the background reference and improving quantitative comparability as the catheter traverses regions having different fluorescence levels.
Regarding claim 18, the modified Wang 2015 does not fully teach that the processor (i) sets the mean of the first N detected discrete or separate fluorescence values as the background fluorescence threshold for all frames of an entire pullback procedure, (ii) compares the mean of the first N detected discrete or separate fluorescence values with a second mean of a second N detected discrete or separate fluorescence values to determine whether the second mean is lower than the mean, and (iii) in a case where the second mean is lower than the mean, sets the second mean as the background fluorescence threshold. As established in claim 16, the modified Wang 2015 teaches receiving a first N detected discrete or separate fluorescence values and calculating a mean of the first N detected discrete or separate fluorescence values as part of its in situ fluorescence image processing framework. However, the modified Wang 2015 does not expressly teach the particular update rule of comparing that first mean to a second mean computed from a second N detected discrete or separate fluorescence values and, when the second mean is lower, setting the second mean as the background fluorescence threshold.
Ishihara teaches a previously-stored mean versus subsequently-computed mean comparison-and-update framework in which, after a threshold value S is set for a frame and a frame-level average is stored, a subsequent frame-level average is calculated and compared to the stored prior average (Ishihara, ¶[0093]-[0094]). Ishihara further teaches that when the subsequent frame-level average is sufficiently lower than the stored prior average, the threshold-setting process is repeated and “a new threshold value S is set and a new average gradation value m is stored” (Ishihara, ¶[0095]). These teachings provide the missing processing principle that a later-computed mean value may be compared against a previously stored mean value and, when the later mean is lower, the operative background threshold value is updated to reflect that lower condition, with the updated threshold then applied to subsequent processing (Ishihara, ¶[0096]).
In the modified Wang 2015 pullback context, the first N detected discrete or separate fluorescence values can be taken from the beginning of the pullback stream for establishing an initial background fluorescence threshold, while the second N detected discrete or separate fluorescence values can be taken from subsequently detected values during the pullback for determining whether a lower mean exists that should replace the background fluorescence threshold applied across all frames of the entire pullback procedure.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Wang 2015 in view of Ishihara to compare the mean of the first N detected discrete or separate fluorescence values with a second mean of a second N detected discrete or separate fluorescence values and, in a case where the second mean is lower than the mean, set the second mean as the background fluorescence threshold applied across all frames of the entire pullback procedure. This modification is feasible because the modified Wang 2015 already calculates and applies a mean-based background fluorescence threshold from detected fluorescence values acquired during pullback, and Ishihara teaches the software-side logic of calculating a subsequently-computed mean, comparing it to a previously stored mean, and updating the operative threshold when the subsequently-computed mean indicates a lower-background condition (Ishihara, ¶[0093]-[0096]). The benefit of the combination is improved robustness of the background reference used for fluorescence quantification during pullback by preventing an overly high initial background estimate from persisting when later-acquired data show a lower-background condition, thereby reducing false elevation of the background reference and improving quantitative comparability as the catheter traverses regions having different fluorescence levels.
Regarding claim 19, as shown in claim 11, the modified Wang 2015 teaches that the processor acquires detected fluorescence values of a plurality of A-scan lines that were generated based on detected fluorescence light collected by the imaging catheter while scanning the bodily lumen (Wang 2015, p. 6, Sec. 2.2.2: “Signals from the dual balanced detector and the PMT were simultaneously digitized for subsequent processing”, which teaches that the fluorescence signals collected by the imaging catheter are digitized and acquired by the processor for processing, consistent with the processor framework established in claim 11; p. 7, Sec. 2.2.3: “The sampling rate of NIRAF signal was 40 kHz, enabling integration of the NIRAF emission within the period of a single A-line”, which teaches that detected fluorescence values are generated on a per‑A‑line basis, thereby yielding detected fluorescence values corresponding to a plurality of A‑scan lines during scanning; p. 8–9, Sec. 2.2.6: “The normalized NIRAF signal was rendered as a ring shaped image, where each NIRAF data point matches the corresponding OCT A-line at each given rotational position of the catheter”, which teaches that each detected fluorescence value corresponds to a respective A‑scan line generated during catheter scanning; p. 6, Sec. 2.1.1: “The collected NIRAF signal (emission wavelength 675–900 nm) was focused onto a PMT … and digitized by a data acquisition board”, which teaches that fluorescence light collected while scanning the bodily lumen is converted into detected fluorescence values acquired by the processor; p. 8–9, Sec. 2.2.6: “Since the rotary junction was operated at a constant rotation and pullback speed, NIRAF intensity points were evenly acquired”, which teaches that the detected fluorescence values are acquired continuously while the catheter scans the bodily lumen during pullback).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Wang, Hao et al. “Ex Vivo Catheter-Based Imaging of Coronary Atherosclerosis Using Multimodality OCT and NIRAF Excited at 633 Nm.” Biomedical optics express 6.4 (2015): 1363–1375. Web.), hereto referred as Wang 2015, and further in view of Ishihara (US-20120323072-A1), hereto referred as Ishihara, and further in view of Ughi et al. (Ughi, Giovanni J et al. “Clinical Characterization of Coronary Atherosclerosis With Dual-Modality OCT and Near-Infrared Autofluorescence Imaging.” JACC. Cardiovascular imaging 9.11 (2016): 1304–1314. Web.), hereto referred as Ughi 2016, and further in view of Ughi et al. (Ughi et al. “Dual Modality Intravascular Optical Coherence Tomography (OCT) and near-Infrared Fluorescence (NIRF) Imaging: A Fully Automated Algorithm for the Distance-Calibration of NIRF Signal Intensity for Quantitative Molecular Imaging.” International J. Cardiovascular Imaging, 31.2 (2015) 259–268), hereto referred as Ughi 2015.
The modified Wang 2015 teaches claim 20 as shown above.
Regarding claim 20, the modified Wang 2015 does not fully teach that the processor sorts the central tendencies of all frames from lowest to highest, and assigns the lowest central tendency among the central tendencies of the one or more frames as the background fluorescence threshold for all frames. As shown in claim 11, the modified Wang 2015 already teaches acquiring fluorescence data values in a plurality of rotation-synchronized frames during pullback imaging, where each frame corresponds to one catheter rotation and contains discrete fluorescence data values suitable for frame-level statistical processing (Wang 2015, p. 7, Sec. 2.2.3). As further incorporated via Ishihara, the modified Wang 2015 teaches calculating a central tendency for each frame by calculating an “average gradation value m of the entire image” of a frame, and storing such per-frame average values for comparison and threshold control (Ishihara, ¶[0070]; Ishihara, ¶[0094]). However, while the modified Wang 2015 supports per-frame central tendency calculation and use of a threshold value during subsequent processing, it does not explicitly teach sorting (from lowest to highest) the central tendencies of all frames across an entire pullback and assigning the lowest central tendency as a single background fluorescence threshold applied to all frames, as recited.
Ughi 2015 teaches full-pullback dataset processing in which quantitative analysis is performed using the entire pullback dataset as an input, rather than on a frame-by-frame isolated basis. Specifically, Ughi teaches that “the algorithm receives two inputs: the entire IVOCT pullback and the co-registered NIRF dataset” (Ughi 2015, Sec. 2.2), and further teaches computing statistical quantities over sliding or windowed regions across the pullback, including use of mean-based processing and identification of extreme values across frames (Ughi 2015, Sec. 2.2.1). Ughi further demonstrates automated selection of minimum values across frames in the context of identifying severity metrics (e.g., “minimal cross-sectional area”), evidencing that identifying a lowest value across a pullback dataset is a routine and expected analysis step in catheter-based imaging workflows (Ughi 2015, Sec. 6).
Accordingly, once the modified Wang 2015 (via Ishihara) calculates a per-frame central tendency for each rotation-synchronized frame, it would have been prima facie obvious before the effective filing date of the claimed invention to further have modified the modified Wang 2015 in view of Ughi 2015 to evaluate the per-frame central tendencies across the entire pullback and assign the lowest central tendency as a single background fluorescence threshold applied to all frames. Further, once the per-frame central tendency values are available for the pullback, a person with ordinary skill in the art would have recognized that identifying the lowest value is a routine software selection step, and sorting the values from lowest to highest is merely one straightforward, well-known implementation for selecting the minimum as a design choice. This modification does not require any change to data acquisition, catheter hardware, or per-frame processing, because it operates solely on scalar per-frame values that are already calculated and stored (Ishihara, ¶[0070]; ¶[0094]). Ughi 2015 confirms that whole-pullback statistical evaluation and selection of minimal values is a routine, automated analysis technique in catheter-based fluorescence imaging, making the claimed selection of the lowest per-frame central tendency a predictable and straightforward software implementation. The benefit of this modification is pullback-wide consistency in background normalization and thresholding, because anchoring the background fluorescence threshold to the lowest per-frame central tendency across the pullback reduces sensitivity to localized elevated fluorescence (e.g., plaque-related signal), frame-to-frame acquisition variability, and transient artifacts, thereby improving stability and comparability of fluorescence-based measurements across all frames of the pullback.
Response to Arguments
Status of the pending Application
Applicant's arguments filed 11/26/2025, pages 3-4, regarding the previous Advisory Action have been fully considered abut are not persuasive.
Argument: Arguments were not fully addressed
Applicant alleges that prior arguments were not fully addressed in the Advisory Action, specifically asserting that the Office maintained an improper modification of Wang 2015 in view of the secondary reference and mischaracterized the Schwarzfischer reference. The contention regarding improper modification was expressly addressed in the Advisory Action, which explained that combining Wang 2015 with art teaching alternative background normalization techniques does not change the principle of operation of Wang 2015, but instead represents a predictable substitution of one known background-correction technique for another. In particular, using image-derived background normalization instead of a pre-scanning PBS-only calibration does not render Wang 2015 inoperable, does not alter its fundamental fluorescence imaging workflow, and simplifies the system by eliminating a dedicated pre-scan while improving background removal by accounting for in situ background contributions (see below for more detail).
With respect to Applicant’s assertions regarding alleged mischaracterization of the Schwarzfischer reference, the Office notes that the Advisory Action addressed the substance of Applicant’s contentions and that such assertions relate to the framing of a prior rejection rather than the merits of the present rejection. Applicant does not identify any specific argument material to the present rejection that was not addressed. Accordingly, conclusory allegations of non-consideration are not persuasive. All arguments have been considered.
35 U.S.C. §103
Applicant's arguments filed 11/26/2025, pages 4-18, regarding the previous 103 Rejections of claims 1-20 have been fully considered but are either moot (because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument; that is, there are new grounds of rejection) or are not persuasive as shown below.
Argument: Combination improperly modifies Wang 2015
Applicant contends that the Office’s combination improperly modifies Wang 2015 by replacing or augmenting Wang 2015’s PBS-based background calibration, allegedly requiring substantial redesign, increasing processing complexity, and changing the principle of operation of Wang 2015 because background removal would no longer be limited to PBS-defined background. Applicant’s arguments are not persuasive.
Wang 2015 is directed to an intravascular imaging system and method capable of simultaneously acquiring multiple imaging modalities, including optical coherence tomography and near-infrared autofluorescence imaging, using a single catheter. The core purpose of Wang 2015 is the combined acquisition and quantitative visualization of these imaging modalities during an imaging procedure. Background correction, including the PBS-based calibration described in Wang 2015, is a supporting signal-processing step within this broader imaging framework and is not the principle of operation of Wang 2015.
The PBS-based procedure in Wang 2015 represents one known technique for establishing a background reference for autofluorescence signals. Substituting or supplementing that technique with another known background estimation or normalization approach does not render Wang 2015 inoperable for its intended purpose and does not alter the fundamental multimodal imaging workflow of Wang 2015, which includes excitation, detection, signal processing, and image formation. Rather, such a substitution constitutes a predictable use of one known technique for another to address the same recognized problem of background removal and quantitative correction within an established imaging system.
Applicant’s assertion that the principle of operation of Wang 2015 requires background removal to be purely PBS-defined is not persuasive. A person of ordinary skill in the art would have understood that background contributions in fluorescence imaging can arise from multiple sources, including in situ conditions present during imaging, and that background correction derived from image data acquired during the imaging procedure is consistent with the goal of accurate quantitative imaging. Accordingly, replacing or augmenting PBS-based calibration with an image-derived background normalization approach does not defeat the purpose of Wang 2015, but instead improves background suppression by addressing background present during actual imaging conditions including the PBS and not just PBS alone. Critically, Wang 2015 merely reports that, in its protocol, the catheter was immersed in PBS to acquire an OCT background and NIRAF baseline and that PBS background signals were averaged and subtracted during post-processing; Wang 2015 does not state that PBS calibration must be the only permissible background technique. Applicant’s characterization of a ‘purely PBS-defined baseline’ reads an exclusivity requirement into Wang 2015 that is not expressed, as Wang 2015 recites PBS subtraction as part of its described processing steps, not as a limitation that background correction must be limited to PBS-derived background only.
Applicant further asserts that such a modification would require substantial redesign and impermissibly increase processing complexity. The Office disagrees. Wang 2015 already includes a processor-based framework for analyzing detected fluorescence data and generating corrected multimodal images. Implementing an alternative automated background estimation or normalization technique within that existing processing framework would have been well within the level of ordinary skill in the art and would not require a fundamental redesign of the system. Any additional computational processing represents a routine and predictable tradeoff in automated image analysis and is offset by eliminating a dedicated pre-calibration step and reducing reliance on manual calibration by a clinician. Accordingly, the proposed combination does not change the principle of operation of Wang 2015 and represents a reasonable and predictable modification.
A person of ordinary skill in the art would have been motivated to apply a known image-derived background normalization technique in the system of Wang 2015 in order to improve the accuracy and robustness of quantitative fluorescence imaging under actual imaging conditions. Wang 2015 itself recognizes the importance of background correction for quantitative autofluorescence imaging, and alternative background estimation techniques were well known in the art for addressing background contributions that vary during an imaging procedure. Applying such a technique in Wang 2015 represents the use of a known solution to a known problem and does not rely on hindsight. Rather, the modification follows directly from the desire to reduce procedural steps, improve consistency of background removal, and streamline the imaging workflow by automating background correction within the existing data-processing framework of Wang 2015.
Conclusion
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791