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 .
Response to Arguments
Applicant’s response to the last Office Action, filed 7/13/2026, has been entered and made of record.
Applicant has amended claims 1, 6, 9-11, 27 and 41. Claims 1-13, 27, 41, and 90-94 are currently pending. Claim 41 is withdrawn.
Applicant’s arguments, filed 7/13/2026, with respect to the rejection of claim 10 under 35 U.S.C. 112(b) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant's arguments filed 7/13/2026, with respect to the rejection of claims 1, 6, 11 and 27 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Regarding claims 1 and 27, applicant argues that neither Vaz nor Hsieh disclose the amended limitation of the perfusion data being obtained after the scanning, and only disclose real-time, dynamic scan control.
Examiner respectfully disagrees. While Vaz does teach real-time scan control and the obvious benefits that it provides. ¶18 shows the clear benefits of being able to control the scan, “patient x-ray radiation dose may be reduced, a reconstruction computation load may be reduced, and/or a scan duration may be shortened while still acquiring high quality diagnostic images to support patient diagnosis”. Even though Vaz teaches why real-time scan control is more beneficial to the patient, Vaz also teaches “¶43 to measure the AIF or TUC signals from a plurality of reconstructed images after receiving the reconstructed images from image reconstructor 230. The computing device 216 may then enter the AIF or TUC signal to a model to estimate the AIF and VOF curves, as described below, in order to optimally plan personalized contrast scan prescriptions, as described below.” This clearly shows the data is received after a scan in order to optimally produce a personalized scan.
Applicant argues that the combination of Vaz and Hsieh is improper.
Examiner respectfully disagrees, in response to the combination of references, the cited prior art does not criticize, discredit, or otherwise discourage the solutions claimed in the other prior art, therefore, their combination is proper.
Applicant argues that Vaz nor Hsieh teach the amended limitation in claim 6 of
“obtaining compressed perfusion scanning data by compressing the perfusion scanning data using a compression algorithm, the compression algorithm including at least one of lossy compression, lossless compression, discrete cosine change, quantization, or neural networks”.
Examiner respectfully disagrees. Vaz teaches in ¶61 “the TUC signal may include a measured segment of the TUC that may be used as input to a model, and the model may output the estimated AIF curve, the estimated VOF curve, and/or the remainder of the TUC. The model may be a suitable machine learning model, such as a decision tree, regression model, neural network, and so forth.”
Applicant argues that Vaz nor Hsieh teach the amended limitation in claim 11 of “performing the vascular analysis using only based-on-the reconstruction result of the peak phase” or the limitations of claim 13.
Examiner respectfully disagrees. Vaz teaches, ¶67 “method 600 may include updating the estimated TUC and AIF and VOF curves using an updated TUC signal obtained from coarse images reconstructed as the CTP scan progresses, as indicated at 618. For example, one or more images may be reconstructed from one or more of the CTP acquisitions”
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-13, 27, and 90-94 are rejected under 35 U.S.C. 103 as being unpatentable over Vaz (U.S. Patent Pub. No. 2021/0153830) in view of Hsieh (U.S. Patent Pub. No. 2016/0078619).
Regarding Claim 1, Vaz teaches a method for vascular analysis implemented on a machine including one or more processors (Fig. 1-2; ¶23 the CT system 100 further includes an image processor unit 110 configured to reconstruct images of a target volume of the subject 112) and one or more storage devices, comprising (Fig. 1-2; 218 Mass Storage):
Automatically determining a peak phase in a plurality of phases based on perfusion scanning data of the plurality of phases (¶9 Fig. 3 shows a graph illustrating an example arterial inflow function (AIF) curve, an example a venous outflow function (VOF) curve, and an example tissue uptake curve (TUC) generated during a contrast scan (3 phases); Fig 6.; ¶60 At 612, method 600 includes determining if a peak in the TUC signal has been detected, and if the detected peak is a plausible peak; ¶61 If a plausible TUC peak is detected in the TUC signal, method 600 proceeds to 614 to estimate an AIF curve, a VOF curve, and/or the remainder of the TUC from the TUC signal (and/or estimate the time for the arterial peak (AP), venous peak (VP), venous return to baseline (VRTB), and/or other time points of interest))
wherein the perfusion scanning data of the plurality of phases is obtained after performing perfusion scanning and is perfusion scanning data of a plurality of time points of the perfusion scanning, and (¶43 to measure the AIF or TUC signals from a plurality of reconstructed images after receiving the reconstructed images from image reconstructor 230. The computing device 216 may then enter the AIF or TUC signal to a model to estimate the AIF and VOF curves, as described below, in order to optimally plan personalized contrast scan prescriptions, as described below;) the peak phase corresponds to one time point of the plurality of time points (¶60 method 600 includes determining if a peak in the TUC signal has been detected, and if the detected peak is a plausible peak. For example, a peak detector may be executed that is configured to directly detect a peak in the TUC signal and evaluate whether the detected peak is the TUC peak (e.g., time point V on FIG. 3) by determining if the detected peak meets one or more rules that define the TUC peak)
obtaining a reconstruction result of the peak phase by performing, based on the perfusion scanning data of the peak phase, image reconstruction; and (Fig. 6; ¶68 At 620, one or more diagnostic images are reconstructed based on data acquired during the CT Perfusion scan.)
performing vascular analysis based on the reconstruction result of the peak phase (¶73 when the acquisitions are complete and as projection data is sent for image reconstruction/post-processing, the actual AIF/VOF curves (or TUC) may be generated as a first step to the perfusion map computation. In some examples, a post-scan workflow may include displaying to the user a comparison of the AIF/VOF/TUC estimates used to generate the CTP scan prescription vs the actual measured TUC and/or AIF and VOF curves. The differences between the estimated and measured AIF/VOF/TUC may be used to inform the user of the accuracy of the AIF/VOF estimates, inform the user of any errors in the estimates that might have impacted diagnostic image quality, and/or update the machine learning estimation models.)
Vaz hints at but does not explicitly disclose obtaining a reconstruction result of the peak phase by performing, based on the perfusion scanning data of the peak phase, image reconstruction.
Hsieh is in the same field of art of image analysis. Further, Hsieh teaches obtaining a reconstruction result of the peak phase by performing, based on the perfusion scanning data of the peak phase, image reconstruction (¶75 At 634, it is determined if an additional image is desired (e.g., an image for another perfusion phase, or an image for a later portion of a given perfusion phase) is desired. If another image is desired, the method 600 may proceed to 616. If no further images are desired, the method 600 may proceed to 636. At 636, one or more images are reconstructed (e.g., using reconstruction module 122 or other aspect of processing unit 120). An image may be reconstructed for each imaging scan performed.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Vaz by reconstructing a result based on perfusion scanning data of the phase that is taught by Hsieh; thus, one of ordinary skilled in the art would be motivated to combine the references to maintain accuracy between the differences in patients (Hsieh ¶4).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Vaz in view of Hsieh discloses the method of claim 1, wherein determining the peak phase in the plurality of phases based on the perfusion scanning data of the plurality of phases includes:
for the perfusion scanning data of each phase in the plurality of phases, obtaining an initial image reconstruction result of the phase by performing initial image reconstruction (Vaz, ¶58 At 608, one or more “coarse” images are reconstructed from the data acquired during the CTP acquisitions. The coarse images may be reconstructed using a coarse reconstruction process that has a low computational load and thus may be performed rapidly. Because the images reconstructed at 608 are not diagnostic images but instead are images reconstructed to monitor the tissue uptake of the contrast agent, the coarse reconstruction process may sacrifice diagnostic quality in order to allow the images to be quickly reconstructed;)
determining a perfusion time attenuation curve based on the initial image reconstruction results of the plurality of phases; and determining the peak phase based on the perfusion time attenuation curve (Vaz, ¶60 At 612, method 600 includes determining if a peak in the TUC signal has been detected, and if the detected peak is a plausible peak; ¶61 If a plausible TUC peak is detected in the TUC signal, method 600 proceeds to 614 to estimate an AIF curve, a VOF curve, and/or the remainder of the TUC from the TUC signal (and/or estimate the time for the arterial peak (AP), venous peak (VP), venous return to baseline (VRTB), and/or other time points of interest). The AIF and VOF curves and TUC may be estimated from the TUC signal by inputting the TUC signal into a machine learning model.)
Regarding Claim 5, Vaz in view of Hsieh discloses the method of claim 2, wherein a count of reconstruction operations of the initial image reconstruction are less than a count of reconstruction operations of the image reconstruction (Vaz, ¶69 As explained above, monitoring for the TUC peak includes performing fast image reconstructions in order to segment the tissue of interest and measure the contrast level signal in the segmented tissue. These fast reconstructions, while less processing intensive than the actual diagnostic image reconstructions, still use up processing resources that could otherwise be devoted to performing the diagnostic image reconstructions. Thus, the fast image reconstructions performed as part of the TUC monitoring may delay the output of the final diagnostic images. Accordingly, any reduction in the overall scan time provided by the adaptive scan prescription may be weighed against the delay provided by the TUC monitoring; This shows that the initial (coarse) reconstruction can be reduced if the final reconstruction requires that.)
Regarding Claim 6, Vaz in view of Hsieh discloses the method of claim 5, wherein the reconstruction operations of the initial image reconstruction include: obtaining compressed perfusion scanning data by compressing the perfusion scanning data using a compression algorithm, the compression algorithm including at least one of lossy compression, lossless compression, discrete cosine change, quantization, or neural networks (Vaz, ¶61 the TUC signal may include a measured segment of the TUC that may be used as input to a model, and the model may output the estimated AIF curve, the estimated VOF curve, and/or the remainder of the TUC. The model may be a suitable machine learning model, such as a decision tree, regression model, neural network, and so forth;) preprocessing the compressed perfusion scanning data; and obtaining a perfusion scanning image and a reconstruction result of the initial image reconstruction by reconstructing the preprocessed compressed perfusion scanning data (Vaz, ¶69 As explained above, monitoring for the TUC peak includes performing fast image reconstructions in order to segment the tissue of interest and measure the contrast level signal in the segmented tissue. These fast reconstructions, while less processing intensive than the actual diagnostic image reconstructions, still use up processing resources that could otherwise be devoted to performing the diagnostic image reconstructions. Thus, the fast image reconstructions performed as part of the TUC monitoring may delay the output of the final diagnostic images. Accordingly, any reduction in the overall scan time provided by the adaptive scan prescription may be weighed against the delay provided by the TUC monitoring; This shows that the initial (coarse) reconstruction can be reduced if the final reconstruction requires that. In this case lower number of coarse images to be reconstructed is the “compressed scanning data”)
Regarding Claim 7, Vaz in view of Hsieh discloses the method of claim 5, wherein the reconstruction operations of the image reconstruction include:
obtaining the perfusion scanning data; preprocessing the perfusion scanning data (Vaz, ¶31 The data collected by the detector array 108 undergoes pre-processing and calibration to condition the data to represent the line integrals of the attenuation coefficients of the scanned subject 204;)
obtaining a perfusion scanning image by reconstructing the preprocessed perfusion scanning data; and (Vaz, Fig. 6; ¶68 At 620, one or more diagnostic images are reconstructed based on data acquired during the CT Perfusion scan.)
obtaining a postprocessed reconstructed result by postprocessing the perfusion scanning image (¶73 In some examples, when the acquisitions are complete and as projection data is sent for image reconstruction/post-processing, the actual AIF/VOF curves (or TUC) may be generated as a first step to the perfusion map computation.)
Regarding Claim 8, Vaz in view of Hsieh discloses the method of claim 2, wherein the perfusion time attenuation curve is a perfusion time attenuation curve in an arterial region (Vaz, ¶50 FIG. 5 shows a graph 500 depicting an estimated AIF curve 502, an estimated VOF curve 504, and an estimated TUC 506 each estimated according to an AIF estimation method. The inflow of the contrast agent of the contrast bolus may be monitored and used to set parameters for the contrast scan. As shown, a first segment 508 of the AIF curve is measured as described above (e.g., in a ROI based on change in HU level relative to a baseline level);)
determining the perfusion time attenuation curve based on the initial image reconstruction results of the plurality of phases includes (Vaz, ¶51 Thus, the TUC and AIF and VOF curves (or selected time points of the TUC and AIF and VOF curves) may be estimated using a relatively short measured segment of the TUC or the AIF curve that is entered into a machine learning model. While the AIF estimation method was described as being based on a single arterial ROI, it is to be understood that multiple arterial ROIs could be measured and combined (e.g., averaged) to measure the AIF curve:)
identifying the arterial region based on the initial image reconstruction results of the plurality of phases; and determining the perfusion time attenuation curve based on image data of the arterial region in the initial image reconstruction results of the plurality of phases (Vaz, ¶52 The arterial ROI and venous ROI described above may be positioned at any suitable location where arterial inflow and venous outflow, respectively, of contrast agent may be detectable, and the selection of where to position the arterial ROI and/or venous ROI may depend on the scan protocol (e.g., what anatomy is going to be imaged in the contrast scan)… the AIF, TUC, and VOF curves (or selected time points of the AIF, TUC, and VOF curves) may be estimated using a relatively short measured segment of the TUC that is entered into a machine learning model.)
Regarding Claim 9, Vaz in view of Hsieh discloses the method of claim 2, wherein the perfusion scanning data is perfusion scanning data of a region of interest (ROI) (Vaz, ¶4 In one embodiment, a method includes, upon an injection of a contrast agent, initiating a contrast scan of a subject according to a fallback scan prescription, processing acquired projection data of an anatomical region of interest (ROI).)
Regarding Claim 10, Vaz in view of Hsieh discloses the method of claim 9, further comprising:
obtaining a reference image; obtaining, based on the reference image, the ROI selected by a user (Vaz, ¶44 In one embodiment, the display 232 allows the operator to evaluate the imaged anatomy, view measured and/or estimated AIF and VOF curves, trigger aspects of the contrast scans, and the like. The display 232 may also allow the operator to select a region of interest (ROI) and/or request patient information, for example, via a graphical user interface (GUI) for a subsequent scan or processing;) and obtaining the initial image reconstruction results of the plurality of phases of the ROI after performing the perfusion scanning (Vaz, ¶58 At 608, one or more “coarse” images are reconstructed from the data acquired during the CTP acquisitions; also see rejection of claim 2.)
Regarding Claim 11, Vaz in view of Hsieh discloses the method of claim 1, wherein performing the vascular analysis based on the reconstruction result of the peak phase includes:
determining whether a motion amplitude of the peak phase is greater than a preset amplitude based on the reconstruction result of the peak phase; and (Vaz, ¶60 If a confirmed peak is found, the found peak is considered as an internal peak candidate (IPC). If the IPC occurs before a threshold time since the contrast injection (e.g., 14 seconds), the IPC may be discarded and the process may be repeated on the next IPC. If the IPC does not occur before the first threshold time, the IPC is further analyzed to determine if the slope of the IPC is greater than a threshold slope, such as 3 HU/s. If so, that IPC is considered a spike and is discarded. If not, the time between the ascent knee (e.g., time point U on FIG. 3) and the IPC is determined. If this time is less than a second threshold time, such as 4 seconds, the IPC is considered a spike and discarded. If not, it is determined if the median HU before the IPC is greater than a threshold value, such as the IPC HU minus 2. If so, the IPC is discarded. If not, the segmented tissue (e.g., brain) volume of the image acquisition at the IPC is compared to the segmented tissue volume from the previous image acquisition. If the segmented tissue volume at the IPC is different from the previous tissue volume by an amount that is greater than a threshold (e.g., 4.25%), the IPC is discarded. If not, (and if none of these described conditions are triggered), the IPC is confirmed as the tissue peak.)
in response to determining that the motion amplitude of the peak phase is less than or equal to the preset amplitude, performing the vascular analysis using only the reconstruction result of the peak phase (see rejection of claim 1 for mapping of performing vascular analysis; ¶67 method 600 may include updating the estimated TUC and AIF and VOF curves using an updated TUC signal obtained from coarse images reconstructed as the CTP scan progresses, as indicated at 618. For example, one or more images may be reconstructed from one or more of the CTP acquisitions)
Regarding Claim 12, Vaz in view of Hsieh discloses the method of claim 11, further comprising:
in response to determining that the motion amplitude of the peak phase is greater than the preset amplitude, prompting the user for relevant information of the motion amplitude of the peak phase by generating prompting information (Vaz, ¶70 an operator of the imaging system (or an administrator of the medical facility housing the imaging system, or another qualified personnel) may determine that continued monitoring for the TUC peak is not justified if the peak is not detected within a threshold amount of time, such as within 45-65 seconds. The determination of whether or not continued monitoring is justified may be made automatically based on the amount of elapsed time since the contrast agent was injected or the first CTP acquisition was performed relative to a threshold amount of time, which may be 45-65 seconds or another suitable time. The threshold time may be set in advance by the operator or another clinician or administrator.)
Regarding Claim 13, Vaz in view of Hsieh discloses the method of claim 11, wherein in response to determining that the motion amplitude of the peak phase is greater than the preset amplitude, obtaining a reconstruction result of an adjacent phase of the peak phase by performing, based on the perfusion scanning data of the adjacent phase of the peak phase, image reconstruction; and (Vaz, ¶71 If it is determined at 622 that further monitoring is justified, method 600 proceeds to 624 to continue performing CTP acquisitions at the fallback parameters, such as at the fallback temporal sampling rate (e.g., of one acquisition every 2 seconds))
performing the vascular analysis based on the reconstruction result of the peak phase and the reconstruction result of the adjacent phase of the peak phase (see rejection of claim 1 for mapping of performing vascular analysis; ¶67 method 600 may include updating the estimated TUC and AIF and VOF curves using an updated TUC signal obtained from coarse images reconstructed as the CTP scan progresses, as indicated at 618. For example, one or more images may be reconstructed from one or more of the CTP acquisitions)
Regarding claim 27, claim 27 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Vaz further teaching on: A system for vascular analysis, comprising: at least one storage device including a set of instructions; and at least one processor in communication with the at least one storage device, wherein when executing the set of instructions, the at least one processor is directed to cause the device to perform operations (¶43 The various methods and processes (such as the method described below with reference to FIG. 6) described further herein may be stored as executable instructions in non-transitory memory on a computing device (or controller) in imaging system 200. In an embodiment, computing device 216 may include the instructions in non-transitory memory)
Claim 90 recites limitations similar to claim 2 and is rejected under the same rationale and reasoning.
Claim 91 recites limitations similar to claim 3 and is rejected under the same rationale and reasoning.
Claim 92 recites limitations similar to claim 4 and is rejected under the same rationale and reasoning.
Claim 93 recites limitations similar to claim 5 and is rejected under the same rationale and reasoning.
Claim 94 recites limitations similar to claim 6 and is rejected under the same rationale and reasoning.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Vaz (U.S. Patent Pub. No. 2021/0153830) in view of Hsieh (U.S. Patent Pub. No. 2016/0078619) in view of Yi (U.S. Patent Pub. No. 2013/0335408).
Regarding Claim 3, Vaz in view of Hsieh teaches the method of claim 2.
Vaz in view of Hsieh does not explicitly disclose wherein a reconstructed slice thickness of the initial image reconstruction is greater than the reconstructed slice thickness of the image reconstruction.
Yi is in the same field of art of image analysis. Further, Yi teaches wherein a reconstructed slice thickness of the initial image reconstruction is greater than the reconstructed slice thickness of the image reconstruction (Vaz teaches ¶58 The coarse reconstruction process may include 128×128 slices that are 5 mm thick, and the reconstruction process may take about 1 second per acquisition. However, Vaz does not explicitly disclose the final reconstruction thickness being less than the initial reconstruction.
Yi teaches ¶58 In general, the thick-slice data is a set of medical image slices reconstructed at intervals of about 3 to 5 mm, and the thin-slice data is a set of medical image slices reconstructed at intervals of about 0.3 to 1 mm. The thick-slice data can be easily stored and kept, and more detailed data for reading can be obtained from the thin-slice data. In the present invention, an embodiment in which a 3D medical image is generated based on thin-slice data including more detailed data. This would allow someone skilled in the art to have the initial reconstruction thickness be greater than the final reconstruction thickness.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Vaz in view of Hsieh by using a slice that is thinner for more detail that is taught by Yi; thus, one of ordinary skilled in the art would be motivated to combine the references to reduce computational load (Yi ¶17).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 4, Vaz in view of Hsieh in view of Yi discloses the method of claim 3, wherein the reconstructed slice thickness of the initial image reconstruction is not less than 3mm, and the reconstructed slice thickness of the image reconstruction is less than 3mm (Vaz teaches ¶58 The coarse reconstruction process may include 128×128 slices that are 5 mm thick, and the reconstruction process may take about 1 second per acquisition.
Yi teaches ¶58 In general, the thick-slice data is a set of medical image slices reconstructed at intervals of about 3 to 5 mm, and the thin-slice data is a set of medical image slices reconstructed at intervals of about 0.3 to 1 mm. The thick-slice data can be easily stored and kept, and more detailed data for reading can be obtained from the thin-slice data. In the present invention, an embodiment in which a 3D medical image is generated based on thin-slice data including more detailed data.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
/CHARLOTTE M BAKER/Primary Examiner, Art Unit 2664
