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 .
PRELIMINARY AMENDMENT
Acknowledgment is made of applicant/s preliminary amendments to the claims filed on 03/12/2025 and the claims have been examined accordingly.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP24162977, filed on 03/12/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 4 is objected to because of the following informalities:
Replace “Generating” in first limitation to “generating”.
Replace “Integrating” in second limitation to “integrating.
Appropriate correction is required.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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 – 4, 6 – 11 and 13 – 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jonnal et al. (US 20250143566 A1; hereafter referred to as Jonnal) in view of Makihira (See Machine Translation for JP 2019150554 A; hereafter referred to as Makihira).
Regarding Claim 1, Jonnal teaches:
A computer-implemented method of processing each set of optical coherence tomography, OCT, images comprising a first OCT image and a second OCT image of a plurality of different sets of OCT images in a sequence of OCT images of a common portion of a retina of an eye acquired by a Fourier-domain OCT imaging system to generate a respective indication of a tissue velocity at a position along an axial direction in the OCT images, the method comprising performing, for each set of the sets of OCT image (Jonnal, [0032] FIG. 2 schematically illustrates an embodiment of our custom optical coherence tomography (OCT) system 200”; Jonnal, [0037] “The OCT system was configured to scan in one dimension only, acquiring a series of B-scans in a single location. B-scans consisting of 250 A-scans (scan width approximately 750 μm on the retina) were collected at a rate of 400 Hz”; Jonnal, [0040] Our custom signal processing takes place in the OCT computer 218 using signal processing instructions that are stored in a memory 268 for execution on a processor 270. The signal processing can be implemented using, for example, a standalone signal processing unit that is operatively connected to the OCT computer, a signal processing unit that is integrated into the OCT computer, or customized OCT software”; Jonnal, [0042] In the first stage, the raw spectral data is converted into cross-sectional OCT images… complex-valued B-scans are generated by Fourier transforming the processed spectra. The B-scans are also flattened, such that the IS/OS and COST reflections lay at the same height for each A-scan in the image”; Jonnal, [0045] “FIG. 4, in one embodiment the B-scans are converted into estimates of tissue velocity”):
calculating a respective comparison value being one of:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Jonnal, [0062] “When analyzing a series of B-scans to measure instantaneous velocity, we monitored the residual least-square error and the correlation of B-scan amplitude”; Jonnal, [0061] “the noise floor of the present method could potentially be reduced with improved bulk-motion correction”; Jonnal, [0067] “The amplitude of the OCT signal provides a way to measure the impact of eye movements and other sources of noise on groups of OCT B-scans. Correlation of the OCT amplitude over time improves confidence in the resulting ORG signal”; Jonnal, [0076] “the variance of the complex OCT signal (or its amplitude or its phase) with respect to time could be used to quantify the instantaneous activity of the retinal tissue. This variance increases in response to light stimuli and exhibits dose dependence. While it may have a limited dynamic range compared to mechanistic figures of merit, it has the advantage of obviating location, segmentation, and correct labeling of retinal features”; Note: at least one of amplitude and/or noise is being read as image quality metric);
calculating the respective indication of tissue velocity at the position along the axial direction using a phase value at the position along the axial direction in an A-scan of the first OCT image in the set, and a phase value at the position along the axial direction in a corresponding A-scan of the second OCT image in the set (Jonnal, [0045] “The B-scans are converted into estimates of tissue velocity as follows. First, at step 402, a moving 10 ms time window is used to select groups of five sequential B-scans at a time. Next, at step 404, a histogram-based bulk-motion correction algorithm is utilized to compensate for the axial eye movement during the 10 ms interval, with motion corrected relative to the first B-scan in the series”; Jonnal, [0092] “ obtaining optical coherence tomography (OCT) B-scans of an eye of a subject; (b) selecting groups of sequential B-scans using a moving time window; (c) using a histogram-based bulk-motion correction algorithm to compensate for axial eye movement during the time window, wherein motion is corrected relative to the first B-scan in the series”; Jonnal, [0095] “assessing tissue velocity comprises: (a) obtaining a 2-dimensional profile of retinal tissues from B-scans of the subject at multiple time points”; Jonnal, [0106] “wherein said assessing tissue velocity further comprises: (a) serially acquiring a plurality of B-scans from the OCT system; (b) dividing the B-scans into temporally overlapping blocks; and (c) performing registration, bulk motion estimation, phase unwrapping, and slope calculation to yield corresponding tissue velocities”);
However, Jonnal does not explicitly recite:
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set;
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold;
in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction;
in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction.
In the same field of endeavor, Makihira teaches:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Makihira, [0033] “a plurality of OCT images are acquired, luminance changes in the images are extracted, and luminance changes are integrated in the x direction”; Makihira, [0054] In step S505, the signal processing unit 053 calculates the absolute value of the complex signal obtained by the Fourier transform executed in step S504. This value is the pixel value (luminance value) of the tomographic image of the scan. In step S506, the signal processing unit 053 determines whether the index j has reached a predetermined number (m). That is, it is determined whether the luminance calculation of the tomographic image at the position yk has been repeated m times. If it is less than the predetermined number, the process returns to S502, and the luminance calculation of the tomographic image at the same Y position is repeated. When the predetermined number is reached, the process proceeds to the next step”);
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set (Makihira, [0055] “In step S507, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain yk position. Specifically, the signal processing unit 053 selects an arbitrary one of m frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images”);
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold (Makihira, [0056] “In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”);
in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction (Makihira, [0056] In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”; Makihira, [0043] “the signal processing unit 053 integrates the luminance change values of the pixel columns in the axial direction of the approximate straight line defined by the linear approximation”; Makihira, [0104] “Each segmentation is linearly approximated, the average value of the inclination is calculated as the inclination θ of the blood vessel layer, and the luminance on the MC image is integrated in the axial direction of the inclination θ.);
in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction (Makihira, [0057] “If the correlation of the entire image is low, an error occurs as if the whole image is a tissue with a flow. It will be recognized. In this step, in order to avoid such erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected. As a result of image selection, images of m frames acquired at the same position yk are appropriately selected”).
Jonnal and Makihira are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing of Optical Coherence Tomography images. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Jonnal with the invention of Makihira to make the invention that calculated image quality metric based on at least one of the first OCT image in the set and the second OCT image in the set; calculates image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set; comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold; in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction; and in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction; doing so, in order to avoid erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected and noise can be reduced (Makihira, [0056], [0067]) and the OCT images can be used for extracting highly accurate information on retinal or tissue changes with time that would accurately diagnose disease and abnormalities (Makihira, [0101]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 2, Jonnal teaches:
A computer-implemented method of processing each set of optical coherence tomography, OCT, images comprising a first OCT image and a second OCT image of a plurality of different sets of OCT images in a sequence of OCT images of a common portion of a retina of an eye acquired by a Fourier-domain OCT imaging system to generate a respective indication of a tissue velocity at a position along an axial direction in the OCT images, the method comprising performing, for each set of the sets of OCT image (Jonnal, [0032] FIG. 2 schematically illustrates an embodiment of our custom optical coherence tomography (OCT) system 200”; Jonnal, [0037] “The OCT system was configured to scan in one dimension only, acquiring a series of B-scans in a single location. B-scans consisting of 250 A-scans (scan width approximately 750 μm on the retina) were collected at a rate of 400 Hz”; Jonnal, [0040] Our custom signal processing takes place in the OCT computer 218 using signal processing instructions that are stored in a memory 268 for execution on a processor 270. The signal processing can be implemented using, for example, a standalone signal processing unit that is operatively connected to the OCT computer, a signal processing unit that is integrated into the OCT computer, or customized OCT software”; Jonnal, [0042] In the first stage, the raw spectral data is converted into cross-sectional OCT images… complex-valued B-scans are generated by Fourier transforming the processed spectra. The B-scans are also flattened, such that the IS/OS and COST reflections lay at the same height for each A-scan in the image”; Jonnal, [0045] “FIG. 4, in one embodiment the B-scans are converted into estimates of tissue velocity”):
calculating a respective comparison value being one of:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Jonnal, [0062] “When analyzing a series of B-scans to measure instantaneous velocity, we monitored the residual least-square error and the correlation of B-scan amplitude”; Jonnal, [0061] “the noise floor of the present method could potentially be reduced with improved bulk-motion correction”; Jonnal, [0067] “The amplitude of the OCT signal provides a way to measure the impact of eye movements and other sources of noise on groups of OCT B-scans. Correlation of the OCT amplitude over time improves confidence in the resulting ORG signal”; Jonnal, [0076] “the variance of the complex OCT signal (or its amplitude or its phase) with respect to time could be used to quantify the instantaneous activity of the retinal tissue. This variance increases in response to light stimuli and exhibits dose dependence. While it may have a limited dynamic range compared to mechanistic figures of merit, it has the advantage of obviating location, segmentation, and correct labeling of retinal features”; Note: at least one of amplitude and/or noise is being read as image quality metric);
However, Jonnal does not explicitly recite:
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set;
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold;
in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction;
in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction.
In the same field of endeavor, Makihira teaches:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Makihira, [0033] “a plurality of OCT images are acquired, luminance changes in the images are extracted, and luminance changes are integrated in the x direction”; Makihira, [0054] In step S505, the signal processing unit 053 calculates the absolute value of the complex signal obtained by the Fourier transform executed in step S504. This value is the pixel value (luminance value) of the tomographic image of the scan. In step S506, the signal processing unit 053 determines whether the index j has reached a predetermined number (m). That is, it is determined whether the luminance calculation of the tomographic image at the position yk has been repeated m times. If it is less than the predetermined number, the process returns to S502, and the luminance calculation of the tomographic image at the same Y position is repeated. When the predetermined number is reached, the process proceeds to the next step”);
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set (Makihira, [0055] “In step S507, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain yk position. Specifically, the signal processing unit 053 selects an arbitrary one of m frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images”);
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold (Makihira, [0056] “In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”);
in a case the comparison value is determined to be equal to or greater than the threshold, using a phase value at the position along the axial direction in an A-scan of the first OCT image in the set, and a phase value at the position along the axial direction in a corresponding A-scan of the second OCT image in the set, in a calculation of the respective indication of tissue velocity at the position along the axial direction (Makihira, [0056] In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”; Makihira, [0043] “the signal processing unit 053 integrates the luminance change values of the pixel columns in the axial direction of the approximate straight line defined by the linear approximation”; Makihira, [0104] “Each segmentation is linearly approximated, the average value of the inclination is calculated as the inclination θ of the blood vessel layer, and the luminance on the MC image is integrated in the axial direction of the inclination θ”; Makihira, [0060] “the index representing the similarity can be variously changed as long as it is a scale representing the similarity between the template and the feature of the image in the frame. For example, … Phase Only Correlation (POC)”);
in case the comparison value is determined not to be equal to or greater than the threshold, omitting a phase value at the position along the axial direction in an A-scan of the first OCT image in the set, and a phase value at the position along the axial direction in a corresponding A-scan of the second OCT image in the set, in the calculation of the respective indication of tissue velocity at the position along the axial direction (Makihira, [0057] “If the correlation of the entire image is low, an error occurs as if the whole image is a tissue with a flow. It will be recognized. In this step, in order to avoid such erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected. As a result of image selection, images of m frames acquired at the same position yk are appropriately selected”; See Makihira, [0065] – [0067] describes that when the comparison value is equal or less than the threshold, the motion contrast value MC is set to zero (omitted)).
Jonnal and Makihira are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing of Optical Coherence Tomography images. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Jonnal with the invention of Makihira to make the invention that calculated image quality metric based on at least one of the first OCT image in the set and the second OCT image in the set; calculates image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set; comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold; in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction; and in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction; doing so, in order to avoid erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected and noise can be reduced (Makihira, [0056], [0067]) and the OCT images can be used for extracting highly accurate information on retinal or tissue changes with time that would accurately diagnose disease and abnormalities (Makihira, [0101]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 3, Jonnal in view of Makihira teaches the computer-implemented method according to Claim 1, wherein the comparison value is a maximum value of a calculated cross- correlation between the first OCT image and the second OCT image (Makihira, [0055] In step S507, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain yk position. Specifically, the signal processing unit 053 selects an arbitrary one of m frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images.”; Makihira, [ [0057] “in order to avoid such erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected. As a result of image selection, images of m frames acquired at the same position yk are appropriately selected and become q frame images”; Makihira, [0059] “the position deviation amounts (δX, δY, δθ) are obtained by matching each frame with a template. Specifically, Normalized Cross-Correlation (NCC), which is an index representing similarity while changing the position and angle of the template image, is calculated, and the difference between the image positions when this value is maximized is obtained as the amount of positional deviation”).
The reasons for combining Jonnal and Makihira are similar to that stated in claim 1. In addition, this same reasoning is pertinent and applicable to the rejections of claim 4 below.
Regarding Claim 4, Jonnal in view of Makihira teaches the computer-implemented method according to Claim 1, further comprising:
generating a concatenation of the generated indications of tissue velocity such that the concatenation is indicative of how the tissue velocity at the position along the axial direction changes over time (Jonnal, [0045] More specifically, referring to the process flow 400 illustrated in FIG. 4, in one embodiment the B-scans are converted into estimates of tissue velocity as follows. First, at step 402, a moving 10 ms time window is used to select groups of five sequential B-scans at a time. Next, at step 404, a histogram-based bulk-motion correction algorithm is utilized to compensate for the axial eye movement during the 10 ms interval, with motion corrected relative to the first B-scan in the series…. step 406, the phase data cube θ(x,z,t) is unwrapped in the temporal dimension by adding or subtracting 2π to θ in order to minimize where t, and t.sub.r−1 represent consecutive phase B-scans.”; Jonnal, [0057] “a time series of OS velocity, can be integrated to approximate the results of earlier, position-based optoretinographic methods”), (Makihira, [0051] “the signal processing unit 053 generates motion contrast information of substantially the same part by using a plurality of pieces of tomographic information captured at predetermined time intervals of the substantially identical part of the eye to be examined”; Makihira, [0068] “In step S514, a motion contrast front image integrated in the depth direction with respect to the generated three-dimensional MC value (motion contrast information), a so-called OCTA front image is generated”); and
integrating the concatenation to generate data indicating an optical path length variation over time at the position along the axial direction (Makihira, [0017] “the reference light is reflected by the mirror 033 that can move in the optical axis direction and can correct the optical path length difference with the sample optical system 102 while maintaining the collimated state, and is folded back to the same optical path”; Makihira, [0033] “a plurality of OCT images are acquired, luminance changes in the images are extracted, and luminance changes are integrated in the x direction”; Makihira, [0034] “the signal processing unit 053 acquires OCT images 301 to 304 (four OCT images obtained from the same portion of the fundus: m = 4). As shown in FIG. 3A, the horizontal direction is the x direction, the vertical direction is the z direction, the vertical direction is the y direction, and the depth direction is t (time)”).
Regarding Claim 6, Jonnal in view of Makihira teaches the computer-implemented method according to Claim 1, further comprising processing the phase value at the position along the axial direction in the A-scan of the first OCT image in the set, and the phase value at the position along the axial direction in the corresponding A-scan of the second OCT image in the set, before the calculation of the respective indication of tissue velocity at the position along the axial direction, to compensate for a bulk motion of the common portion of the retina during acquisition of the sequence of OCT images by the Fourier-domain OCT imaging system (Jonnal, [0045] “a histogram-based bulk-motion correction algorithm is utilized to compensate for the axial eye movement during the 10 ms interval, with motion corrected relative to the first B-scan in the series”; Jonnal, [0042] “complex-valued B-scans are generated by Fourier transforming the processed spectra”; Jonnal, [0076] “The statistical dispersion of phase, aggregated spatially, is an example of such a method. For instance, after the numerical correction of bulk-motion phase artifacts, the variance of the complex OCT signal (or its amplitude or its phase) with respect to time could be used to quantify the instantaneous activity of the retinal tissue”; Jonnal, [0091] “ (a) selecting groups of sequential B-scans using a moving time window; (b) using a histogram-based bulk-motion correction algorithm to compensate for axial eye movement during the time window, wherein motion is corrected relative to the first B-scan in the series, and wherein after bulk-motion correction, a resulting complex data cube (V) may be described as V(x,z,t)=A(x,z,t)e.sup.jθ(x,z,t), where x and z are the lateral and depth coordinates, respectively, and t is time within the window… computing a rate of phase change is computed for each coordinate pair”).
Regarding Claim 7, Jonnal in view of Makihira teaches a computer program comprising computer-readable instructions which, when executed by a processor, cause the processor (Jonnal, [0110] “computer program instructions, such as embodied in computer-readable program code logic means, for performing the specified function(s)”) to perform a method according to Claim 1 (see rejection for claim 1).
Regarding Claim 8, Jonnal teaches:
A data processing apparatus arranged to process each set of optical coherence tomography, OCT (Jonnal, Fig. 2), images comprising a first OCT image and a second OCT image of a plurality of different sets of OCT images in a sequence of OCT images of a common portion of a retina of an eye acquired by a Fourier-domain OCT imaging system to generate a respective indication of a tissue velocity at a position along an axial direction in the OCT images, the method comprising performing, for each set of the sets of OCT image (Jonnal, [0032] FIG. 2 schematically illustrates an embodiment of our custom optical coherence tomography (OCT) system 200”; Jonnal, [0037] “The OCT system was configured to scan in one dimension only, acquiring a series of B-scans in a single location. B-scans consisting of 250 A-scans (scan width approximately 750 μm on the retina) were collected at a rate of 400 Hz”; Jonnal, [0040] Our custom signal processing takes place in the OCT computer 218 using signal processing instructions that are stored in a memory 268 for execution on a processor 270. The signal processing can be implemented using, for example, a standalone signal processing unit that is operatively connected to the OCT computer, a signal processing unit that is integrated into the OCT computer, or customized OCT software”; Jonnal, [0042] In the first stage, the raw spectral data is converted into cross-sectional OCT images… complex-valued B-scans are generated by Fourier transforming the processed spectra. The B-scans are also flattened, such that the IS/OS and COST reflections lay at the same height for each A-scan in the image”; Jonnal, [0045] “FIG. 4, in one embodiment the B-scans are converted into estimates of tissue velocity”):
calculating a respective comparison value being one of:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Jonnal, [0062] “When analyzing a series of B-scans to measure instantaneous velocity, we monitored the residual least-square error and the correlation of B-scan amplitude”; Jonnal, [0061] “the noise floor of the present method could potentially be reduced with improved bulk-motion correction”; Jonnal, [0067] “The amplitude of the OCT signal provides a way to measure the impact of eye movements and other sources of noise on groups of OCT B-scans. Correlation of the OCT amplitude over time improves confidence in the resulting ORG signal”; Jonnal, [0076] “the variance of the complex OCT signal (or its amplitude or its phase) with respect to time could be used to quantify the instantaneous activity of the retinal tissue. This variance increases in response to light stimuli and exhibits dose dependence. While it may have a limited dynamic range compared to mechanistic figures of merit, it has the advantage of obviating location, segmentation, and correct labeling of retinal features”; Note: at least one of amplitude and/or noise is being read as image quality metric);
calculating the respective indication of tissue velocity at the position along the axial direction using a phase value at the position along the axial direction in an A-scan of the first OCT image in the set, and a phase value at the position along the axial direction in a corresponding A-scan of the second OCT image in the set (Jonnal, [0045] “The B-scans are converted into estimates of tissue velocity as follows. First, at step 402, a moving 10 ms time window is used to select groups of five sequential B-scans at a time. Next, at step 404, a histogram-based bulk-motion correction algorithm is utilized to compensate for the axial eye movement during the 10 ms interval, with motion corrected relative to the first B-scan in the series”; Jonnal, [0092] “ obtaining optical coherence tomography (OCT) B-scans of an eye of a subject; (b) selecting groups of sequential B-scans using a moving time window; (c) using a histogram-based bulk-motion correction algorithm to compensate for axial eye movement during the time window, wherein motion is corrected relative to the first B-scan in the series”; Jonnal, [0095] “assessing tissue velocity comprises: (a) obtaining a 2-dimensional profile of retinal tissues from B-scans of the subject at multiple time points”; Jonnal, [0106] “wherein said assessing tissue velocity further comprises: (a) serially acquiring a plurality of B-scans from the OCT system; (b) dividing the B-scans into temporally overlapping blocks; and (c) performing registration, bulk motion estimation, phase unwrapping, and slope calculation to yield corresponding tissue velocities”);
However, Jonnal does not explicitly recite:
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set;
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold;
in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction;
in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction.
In the same field of endeavor, Makihira teaches:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Makihira, [0033] “a plurality of OCT images are acquired, luminance changes in the images are extracted, and luminance changes are integrated in the x direction”; Makihira, [0054] In step S505, the signal processing unit 053 calculates the absolute value of the complex signal obtained by the Fourier transform executed in step S504. This value is the pixel value (luminance value) of the tomographic image of the scan. In step S506, the signal processing unit 053 determines whether the index j has reached a predetermined number (m). That is, it is determined whether the luminance calculation of the tomographic image at the position yk has been repeated m times. If it is less than the predetermined number, the process returns to S502, and the luminance calculation of the tomographic image at the same Y position is repeated. When the predetermined number is reached, the process proceeds to the next step”);
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set (Makihira, [0055] “In step S507, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain yk position. Specifically, the signal processing unit 053 selects an arbitrary one of m frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images”);
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold (Makihira, [0056] “In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”);
in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction (Makihira, [0056] In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”; Makihira, [0043] “the signal processing unit 053 integrates the luminance change values of the pixel columns in the axial direction of the approximate straight line defined by the linear approximation”; Makihira, [0104] “Each segmentation is linearly approximated, the average value of the inclination is calculated as the inclination θ of the blood vessel layer, and the luminance on the MC image is integrated in the axial direction of the inclination θ.);
in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction (Makihira, [0057] “If the correlation of the entire image is low, an error occurs as if the whole image is a tissue with a flow. It will be recognized. In this step, in order to avoid such erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected. As a result of image selection, images of m frames acquired at the same position yk are appropriately selected”).
Jonnal and Makihira are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing of Optical Coherence Tomography images. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Jonnal with the invention of Makihira to make the invention that calculates image quality metric based on at least one of the first OCT image in the set and the second OCT image in the set; calculates image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set; comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold; in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction; and in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction; doing so, in order to avoid erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected and noise can be reduced (Makihira, [0056], [0067]) and the OCT images can be used for extracting highly accurate information on retinal or tissue changes with time that would accurately diagnose disease and abnormalities (Makihira, [0101]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 9, Jonnal teaches:
A data processing apparatus arranged to process each set of optical coherence tomography, OCT (Jonnal, Fig. 2), images comprising a first OCT image and a second OCT image of a plurality of different sets of OCT images in a sequence of OCT images of a common portion of a retina of an eye acquired by a Fourier-domain OCT imaging system to generate a respective indication of a tissue velocity at a position along an axial direction in the OCT images, the method comprising performing, for each set of the sets of OCT image (Jonnal, [0032] FIG. 2 schematically illustrates an embodiment of our custom optical coherence tomography (OCT) system 200”; Jonnal, [0037] “The OCT system was configured to scan in one dimension only, acquiring a series of B-scans in a single location. B-scans consisting of 250 A-scans (scan width approximately 750 μm on the retina) were collected at a rate of 400 Hz”; Jonnal, [0040] Our custom signal processing takes place in the OCT computer 218 using signal processing instructions that are stored in a memory 268 for execution on a processor 270. The signal processing can be implemented using, for example, a standalone signal processing unit that is operatively connected to the OCT computer, a signal processing unit that is integrated into the OCT computer, or customized OCT software”; Jonnal, [0042] In the first stage, the raw spectral data is converted into cross-sectional OCT images… complex-valued B-scans are generated by Fourier transforming the processed spectra. The B-scans are also flattened, such that the IS/OS and COST reflections lay at the same height for each A-scan in the image”; Jonnal, [0045] “FIG. 4, in one embodiment the B-scans are converted into estimates of tissue velocity”):
calculating a respective comparison value being one of:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Jonnal, [0062] “When analyzing a series of B-scans to measure instantaneous velocity, we monitored the residual least-square error and the correlation of B-scan amplitude”; Jonnal, [0061] “the noise floor of the present method could potentially be reduced with improved bulk-motion correction”; Jonnal, [0067] “The amplitude of the OCT signal provides a way to measure the impact of eye movements and other sources of noise on groups of OCT B-scans. Correlation of the OCT amplitude over time improves confidence in the resulting ORG signal”; Jonnal, [0076] “the variance of the complex OCT signal (or its amplitude or its phase) with respect to time could be used to quantify the instantaneous activity of the retinal tissue. This variance increases in response to light stimuli and exhibits dose dependence. While it may have a limited dynamic range compared to mechanistic figures of merit, it has the advantage of obviating location, segmentation, and correct labeling of retinal features”; Note: at least one of amplitude and/or noise is being read as image quality metric);
However, Jonnal does not explicitly recite:
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set;
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold;
in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction;
in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction.
In the same field of endeavor, Makihira teaches:
a value of an image quality metric calculated based on at least one of the first OCT image in the set and the second OCT image in the set (Makihira, [0033] “a plurality of OCT images are acquired, luminance changes in the images are extracted, and luminance changes are integrated in the x direction”; Makihira, [0054] In step S505, the signal processing unit 053 calculates the absolute value of the complex signal obtained by the Fourier transform executed in step S504. This value is the pixel value (luminance value) of the tomographic image of the scan. In step S506, the signal processing unit 053 determines whether the index j has reached a predetermined number (m). That is, it is determined whether the luminance calculation of the tomographic image at the position yk has been repeated m times. If it is less than the predetermined number, the process returns to S502, and the luminance calculation of the tomographic image at the same Y position is repeated. When the predetermined number is reached, the process proceeds to the next step”);
a value of an image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set (Makihira, [0055] “In step S507, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain yk position. Specifically, the signal processing unit 053 selects an arbitrary one of m frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images”);
comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold (Makihira, [0056] “In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”);
in a case the comparison value is determined to be equal to or greater than the threshold, using a phase value at the position along the axial direction in an A-scan of the first OCT image in the set, and a phase value at the position along the axial direction in a corresponding A-scan of the second OCT image in the set, in a calculation of the respective indication of tissue velocity at the position along the axial direction (Makihira, [0056] In step S508, the signal processing unit 053 selects an image having a high correlation in which the correlation with another image is equal to or greater than a certain threshold among the correlation values calculated in step S507”; Makihira, [0043] “the signal processing unit 053 integrates the luminance change values of the pixel columns in the axial direction of the approximate straight line defined by the linear approximation”; Makihira, [0104] “Each segmentation is linearly approximated, the average value of the inclination is calculated as the inclination θ of the blood vessel layer, and the luminance on the MC image is integrated in the axial direction of the inclination θ”; Makihira, [0060] “the index representing the similarity can be variously changed as long as it is a scale representing the similarity between the template and the feature of the image in the frame. For example, … Phase Only Correlation (POC)”);
in case the comparison value is determined not to be equal to or greater than the threshold, omitting a phase value at the position along the axial direction in an A-scan of the first OCT image in the set, and a phase value at the position along the axial direction in a corresponding A-scan of the second OCT image in the set, in the calculation of the respective indication of tissue velocity at the position along the axial direction (Makihira, [0057] “If the correlation of the entire image is low, an error occurs as if the whole image is a tissue with a flow. It will be recognized. In this step, in order to avoid such erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected. As a result of image selection, images of m frames acquired at the same position yk are appropriately selected”; See Makihira, [0065] – [0067] describes that when the comparison value is equal or less than the threshold, the motion contrast value MC is set to zero (omitted)).
Jonnal and Makihira are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing of Optical Coherence Tomography images. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Jonnal with the invention of Makihira to make the invention that calculates image quality metric based on at least one of the first OCT image in the set and the second OCT image in the set; calculates image similarity metric that provides a measure of a degree of similarity between images, the value of the image similarity metric being calculated based on the first OCT image in the set and the second OCT image in the set; comparing the comparison value with a threshold to determine whether the comparison value is equal to or greater than the threshold; in case the comparison value is determined to be equal to or greater than the threshold, retaining the respective indication of tissue velocity at the position along the axial direction; and in case the comparison value is determined not to be equal to or greater than the threshold, discarding the respective indication of tissue velocity at the position along the axial direction; doing so, in order to avoid erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected and noise can be reduced (Makihira, [0056], [0067]) and the OCT images can be used for extracting highly accurate information on retinal or tissue changes with time that would accurately diagnose disease and abnormalities (Makihira, [0101]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 10, Jonnal in view of Makihira teaches the data processing apparatus according to Claim 8, wherein the comparison value is a maximum value of a calculated cross- correlation between the first OCT image and the second OCT image (Makihira, [0055] In step S507, the signal processing unit 053 calculates image similarity in m frames of the same tomographic image at a certain yk position. Specifically, the signal processing unit 053 selects an arbitrary one of m frame tomographic images as a template, and calculates a correlation value with the remaining m−1 frame images.”; Makihira, [0057] “in order to avoid such erroneous recognition, a tomographic image having a low correlation is excluded in advance as an image, and only an image having a high correlation is selected. As a result of image selection, images of m frames acquired at the same position yk are appropriately selected and become q frame images”; Makihira, [0059] “the position deviation amounts (δX, δY, δθ) are obtained by matching each frame with a template. Specifically, Normalized Cross-Correlation (NCC), which is an index representing similarity while changing the position and angle of the template image, is calculated, and the difference between the image positions when this value is maximized is obtained as the amount of positional deviation”).
The reasons for combining Jonnal and Makihira are similar to that stated in claim 1. In addition, this same reasoning is pertinent and applicable to the rejections of claim 11 below.
Regarding Claim 11, Jonnal in view of Makihira teaches the data processing apparatus according to Claim 8, further comprising:
generating a concatenation of the generated indications of tissue velocity such that the concatenation is indicative of how the tissue velocity at the position along the axial direction changes over time (Jonnal, [0045] More specifically, referring to the process flow 400 illustrated in FIG. 4, in one embodiment the B-scans are converted into estimates of tissue velocity as follows. First, at step 402, a moving 10 ms time window is used to select groups of five sequential B-scans at a time. Next, at step 404, a histogram-based bulk-motion correction algorithm is utilized to compensate for the axial eye movement during the 10 ms interval, with motion corrected relative to the first B-scan in the series…. step 406, the phase data cube θ(x,z,t) is unwrapped in the temporal dimension by adding or subtracting 2π to θ in order to minimize where t, and t.sub.r−1 represent consecutive phase B-scans.”; Jonnal, [0057] “a time series of OS velocity, can be integrated to approximate the results of earlier, position-based optoretinographic methods”), (Makihira, [0051] “the signal processing unit 053 generates motion contrast information of substantially the same part by using a plurality of pieces of tomographic information captured at predetermined time intervals of the substantially identical part of the eye to be examined”; Makihira, [0068] “In step S514, a motion contrast front image integrated in the depth direction with respect to the generated three-dimensional MC value (motion contrast information), a so-called OCTA front image is generated”); and
integrating the concatenation to generate data indicating an optical path length variation over time at the position along the axial direction (Makihira, [0017] “the reference light is reflected by the mirror 033 that can move in the optical axis direction and can correct the optical path length difference with the sample optical system 102 while maintaining the collimated state, and is folded back to the same optical path.”; Makihira, [0033] “a plurality of OCT images are acquired, luminance changes in the images are extracted, and luminance changes are integrated in the x direction”; Makihira, [0034] “the signal processing unit 053 acquires OCT images 301 to 304 (four OCT images obtained from the same portion of the fundus: m = 4). As shown in FIG. 3A, the horizontal direction is the x direction, the vertical direction is the z direction, the vertical direction is the y direction, and the depth direction is t (time)”).
Regarding Claim 13, Jonnal in view of Makihira teaches the data processing apparatus according to Claim 8, wherein the data processing apparatus is further arranged to process the phase value at the position along the axial direction in the A-scan of the first OCT image in the set, and the phase value at the position along the axial direction in the corresponding A-scan of the second OCT image in the set, before the calculation of the respective indication of tissue velocity at the position along the axial direction, to compensate for a bulk motion of the common portion of the retina during acquisition of the sequence of OCT images by the Fourier-domain OCT imaging system (Jonnal, [0045] “a histogram-based bulk-motion correction algorithm is utilized to compensate for the axial eye movement during the 10 ms interval, with motion corrected relative to the first B-scan in the series”; Jonnal, [0042] “complex-valued B-scans are generated by Fourier transforming the processed spectra”; Jonnal, [0076] “The statistical dispersion of phase, aggregated spatially, is an example of such a method. For instance, after the numerical correction of bulk-motion phase artifacts, the variance of the complex OCT signal (or its amplitude or its phase) with respect to time could be used to quantify the instantaneous activity of the retinal tissue”; Jonnal, [0091] “ (a) selecting groups of sequential B-scans using a moving time window; (b) using a histogram-based bulk-motion correction algorithm to compensate for axial eye movement during the time window, wherein motion is corrected relative to the first B-scan in the series, and wherein after bulk-motion correction, a resulting complex data cube (V) may be described as V(x,z,t)=A(x,z,t)e.sup.jθ(x,z,t), where x and z are the lateral and depth coordinates, respectively, and t is time within the window… computing a rate of phase change is computed for each coordinate pair”).
Regarding Claim 14, Jonnal in view of Makihira teaches a Fourier-domain optical coherence tomography imaging system comprising the data processing apparatus according to Claim 8 (Jonnal, Fig.2, [0032] “FIG. 2 schematically illustrates an embodiment of our custom optical coherence tomography (OCT) system 200”; Jonnal, [0072] A variety of stimulus patterns have been tested, each with its own virtues. … Flicker stimuli can further improve the ORG SNR by permitting analysis of the signal in the frequency domain).
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Jonnal et al. (US 20250143566 A1; hereafter referred to as Jonnal) in view of Makihira (See Machine Translation for JP 2019150554 A; hereafter referred to as Makihira) further in view of Brugger et al. (US 20240335105 A1; hereafter referred to as Brugger).
Regarding Claim 5, Jonnal in view of Makihira teaches computer-implemented method according to Claim 4, wherein
in the case where the comparison value is determined not to be equal to or greater than the threshold, the respective indication of tissue velocity at the position along the axial direction is set to indicate zero velocity (Makihira, [0063] “the MC value can be applied as long as it is an index representing a change (e.g., luminance and phase after Fourier transform) of each pixel of a plurality of tomographic images at the same Y position. When q = 1, that is, when the correlation is low as an image due to the effect of blinking or fixation micromotion, and the MC value cannot be calculated at the same position yk, different processing is performed. For example, the MC value may be set to 0 and the step may be terminated”; Makihira, [0066] The signal processing unit 053 sets the MC value corresponding to the area where each luminance value is equal to or less than the threshold value to 0. By this threshold processing, noise can be reduced by removing MC values derived from random noise”),
the generated data comprises one or more sets of equal consecutive values (Makihira, [0034] “the signal processing unit 053 acquires OCT images 301 to 304 (four OCT images obtained from the same portion of the fundus: m = 4)”; Makihira, [0062] “signal processing unit 053 calculates the MC value. In this embodiment, a variance value is calculated for each pixel at the same position between q-frame luminance images selected in step S508 and aligned in step S509, and the variance value is calculated as an MC value (motion contrast value)”),
However, Jonnal in view of Makihira does not explicitly recite:
the data processing apparatus is further arranged to smooth the generated data by replacing one or more values in a set of the one or more sets of equal consecutive values with one or more estimated values calculated based on neighbouring values that neighbour the set of equal consecutive values in the generated data.
In the same field of endeavor, Brugger teaches:
the data processing apparatus is further arranged to smooth the generated data by replacing one or more values in a set of the one or more sets of equal consecutive values with one or more estimated values calculated based on neighbouring values that neighbour the set of equal consecutive values in the generated data (Brugger, [0124] “during the gaze reset routine, the supra-threshold object with a predefined velocity pattern along a horizontal (from the test persons perspective) direction, and a vertically going direction particularly in a sequential fashion, wherein a deviation between a velocity of the detected eye movement following the supra-threshold object and the velocity pattern of the supra-threshold object is determined for each supra-threshold object movement direction, such that a smooth pursuit can be determined from the gaze reset routine”; Brugger, [0227] “the maximal pupil size before the switch of the bright stimulus and the minimal pupil size just after the switch are determined by the processor, particularly wherein the temporal course for each eye is smoothed for outliers with a smoothing algorithm”).
Jonnal, Makihira and Brugger are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing of Optical Coherence Tomography images. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Jonnal in view of Makihira with the invention of Brugger to make the invention that smooths the generated data by replacing one or more values in a set of the one or more sets of equal consecutive values with one or more estimated values; doing so can result in accurate values for ocular parameters from tracking the eye movements by reducing complexities and eliminating error sources (Brugger, [0013]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Regarding Claim 12, Jonnal in view of Makihira teaches the data processing apparatus according to Claim 8,
in the case where the comparison value is determined not to be equal to or greater than the threshold, the respective indication of tissue velocity at the position along the axial direction is set to indicate zero velocity (Makihira, [0063] “the MC value can be applied as long as it is an index representing a change (e.g., luminance and phase after Fourier transform) of each pixel of a plurality of tomographic images at the same Y position. When q = 1, that is, when the correlation is low as an image due to the effect of blinking or fixation micromotion, and the MC value cannot be calculated at the same position yk, different processing is performed. For example, the MC value may be set to 0 and the step may be terminated”; Makihira, [0066] The signal processing unit 053 sets the MC value corresponding to the area where each luminance value is equal to or less than the threshold value to 0. By this threshold processing, noise can be reduced by removing MC values derived from random noise”),
the generated data comprises one or more sets of equal consecutive values (Makihira, [0034] “the signal processing unit 053 acquires OCT images 301 to 304 (four OCT images obtained from the same portion of the fundus: m = 4)”; Makihira, [0062] “signal processing unit 053 calculates the MC value. In this embodiment, a variance value is calculated for each pixel at the same position between q-frame luminance images selected in step S508 and aligned in step S509, and the variance value is calculated as an MC value (motion contrast value)”),
However, Jonnal in view of Makihira does not explicitly recite:
the data processing apparatus is further arranged to smooth the generated data by replacing one or more values in a set of the one or more sets of equal consecutive values with one or more estimated values calculated based on neighbouring values that neighbour the set of equal consecutive values in the generated data.
In the same field of endeavor, Brugger teaches:
the data processing apparatus is further arranged to smooth the generated data by replacing one or more values in a set of the one or more sets of equal consecutive values with one or more estimated values calculated based on neighbouring values that neighbour the set of equal consecutive values in the generated data (Brugger, [0124] “during the gaze reset routine, the supra-threshold object with a predefined velocity pattern along a horizontal (from the test persons perspective) direction, and a vertically going direction particularly in a sequential fashion, wherein a deviation between a velocity of the detected eye movement following the supra-threshold object and the velocity pattern of the supra-threshold object is determined for each supra-threshold object movement direction, such that a smooth pursuit can be determined from the gaze reset routine”; Brugger, [0227] “the maximal pupil size before the switch of the bright stimulus and the minimal pupil size just after the switch are determined by the processor, particularly wherein the temporal course for each eye is smoothed for outliers with a smoothing algorithm”).
Jonnal, Makihira and Brugger are considered analogous art as they are reasonably pertinent to the same field of endeavor of image processing of Optical Coherence Tomography images. Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Jonnal in view of Makihira with the invention of Brugger to make the invention that smooths the generated data by replacing one or more values in a set of the one or more sets of equal consecutive values with one or more estimated values; doing so can result in accurate values for ocular parameters from tracking the eye movements by reducing complexities and eliminating error sources (Brugger, [0013]); thus one of the ordinary skill in the art would have been motivated to combine the references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230196525 A1 METHOD AND SYSTEM FOR AXIAL MOTION CORRECTION A method and system for correcting axial motion in optical coherence tomography (OCT) data is provided. The method includes collecting, by a processor disposed of in an OCT device, a volume scan of an eye; segmenting a first retinal layer within the volume scan; applying an algorithm for periodic pattern removal of OCT data in the first retinal layer by determining a model of a Fourier transform applicable to a segment of the first retinal layer; and removing transform frequencies associated with the OCT data using the model of the Fourier transform; determining a measure of an amount of axial motion in accordance with a difference of an amount of OCT data captured on a surface of the first retinal layer before and after application of the algorithm for periodic pattern removal; and correcting, the amount of axial motion in the OCT data of the first retinal layer.
US 20170140560 A1 Method And Apparatus For Motion Correction And Image Enhancement For Optical Coherence Tomography Images of an object, such as OCT scans of a human eye, can include distortions and data gaps due to relative motion of the object and the image acquisition device. Methods and systems for correction of such distortions and data gaps are described herein. Motion-corrected data is arrived at by applying three-dimensional transforms to input three-dimensional data sets that represent at least partially overlapping regions of the imaged object. The three dimensional transforms are computed based on an objective function that accounts for similarity between the transformed three-dimensional data sets and the estimated motion of the object relative to an imaging instrument. Methods and systems described herein advantageously eliminate the need for postulated assumptions and reliance on landmarks and are capable of filling data gaps, thereby producing high quality, undistorted images of objects subject to movement during imaging. Multiple motion-corrected data sets can be merged or combined to produce a data set with improved image quality.
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VAISALI RAO. KOPPOLU
Examiner
Art Unit 2664
/VAISALI RAO KOPPOLU/Examiner of Art Unit 2664
