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 .
Claim Objections
Claims 1 and 11are objected to because of the following informalities:
In claim 1, line 6, and claim 11, line 5, “frames” should read –the mini frames–.
In claim 1, lines 10-15, and claim 11, lines 8-14, “a reconstructing process… an image reconstruction” should read –an image reconstruction … the image reconstruction–.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the "varying a percentage of counts to be used in a reconstructing process and to determine the percentage on a basis of a relationship between the percentage and the mean Euclidean distance… while using the determined percentage” in lines 10-15. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --varying a percentage of counts to be used in a reconstructing process and to determine optimal percentage of counts on a basis of a relationship between the percentage of counts and the mean Euclidean distance… while using the determined optimal percentage of counts--.
Claim 3 recites the "to determine the percentage” in lines 2-3. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --to determine the optimal percentage--.
Claim 4 recites the "to determine the percentage” in lines 6-7. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --to determine the optimal percentage--.
Claim 5 recites the "to determine the percentage” in lines 6-7. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --to determine the optimal percentage--.
Claim 6 recites the "to determine the percentage” in lines 2-3. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --to determine the optimal percentage--.
Claim 7 recites the "to determine the percentage” in the last paragraph. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --to determine the optimal percentage--.
Claim 11 recites the "varying a percentage of counts to be used in a reconstructing process and to determine the percentage on a basis of a relationship between the percentage and the mean Euclidean distance… while using the determined percentage” in lines 8-13. This recitation is unclear because values of a percentage of counts represent a range set by varying the percentage of counts. It is therefore unclear what is being determined. For examination purposes, Examiner of record takes this to be --varying a percentage of counts to be used in a reconstructing process and to determine optimal percentage of counts on a basis of a relationship between the percentage of counts and the mean Euclidean distance… while using the determined optimal percentage of counts--.
Claims dependent upon the rejected claims above, but not directly addressed, are also rejected because they inherit the indefiniteness of the claim(s) they respectively depend upon.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Tsui et al (US 20200134886), hereinafter Tsui, in view of Zhao et al (US 20240104705), hereinafter Zhao, and Matsuura et al (US 20200311878), hereinafter Matsuura.
Regarding claim 1, Tsui teaches a nuclear medicine diagnosis apparatus (10) (20) (“To limit radiation exposure to the imaging subject, the radiopharmaceutical dose is kept low, and hence an extended emission imaging data acquisition time typically on the order of minutes to tens of minutes is employed to collect enough data for an acceptable signal-to-noise ratio (SNR). Thus, the acquisition time spans a large number of breaths and heart cycles.” [0003]. “PET imaging) or projections (for SPECT imaging” [0029]; “a PET imaging gantry or scanner 10” [0036]; “processor 20” [0038]; Fig. 1) comprising processing circuitry (“an emission imaging data processing device comprises an electronic processor and a non-transitory storage medium that stores instructions readable and executable by the electronic processor to perform a respiratory motion estimation method.” [0006]; “As diagrammatically shown in FIG. 1, an electronic processor 20 processes emission imaging data 22 acquired by the PET imaging gantry or scanner 10 (comprising LORs in the illustrative PET imaging embodiment, or comprising projections acquired by a gamma camera in an alternative SPECT imaging embodiment) to generate respiratory motion estimation and to generate a reconstructed image.” [0038]) configured:
to generate a feature vector (a feature vector of “The motion vector images” [0010]. “Respiratory (e.g. breathing) motion estimation techniques disclosed herein operate by quantifying motion of an organ, anatomical boundary, or other image feature that serves as the motion assessment image feature.” [0026]; “the motion assessment image feature” [0029]) for each of mini frames (“The top row of FIG. 5 shows idealized “bin images” that visualize what would be reconstructed for each time interval bin in the absence of noise (this is not physically realizable due to the low total counts in each time interval bin which would lead to the bin images having low SNR).” [0048]) (“each LOR or projection (indexed i)” [0050]) from list mode data acquired by performing a nuclear medicine scan (“binning the acquired emission list-mode data into time interval bins based on time stamps of the LORs or projections” [0006]; “generating a displacement versus time curve representing motion of the motion assessment image feature from the acquired emission list mode data.” [0007]) and to calculate a distance (“Metric” Fig. 5) (“In this example, the displacement metric computed for each time interval bin is the total count of LORs or projections that belong to the time interval bin and intersect the motion assessment volume 66.” [0048]; “the displacement metric” [0050]) between each of feature vectors (“the displacement metric computed for each time interval bin” [0048]) corresponding to frames (“bin images” [0053]; Fig. 5) (“A straightforward approach is to reconstruct the emission imaging data of each time interval bin to generate a corresponding “bin image”, and directly measure the displacement of the motion assessment image feature 60 in each bin image. In this case, the displacement metric is not a statistical displacement metric, but rather is a direct measure of the displacement.” [0045]) and a reference phase vector (a reference phase vector corresponds to zero displacement of the “displacement metric” [0048]);
to calculate, on a basis of the distances, a mean distance (“averaging… the statistical displacement metrics” [0057]) (“it is contemplated for the motion assessment volume 66 to comprise two (or more) volume regions which are separated in space. Said another way, the motion assessment volume 66 may comprise two or more constituent volumes, and in the operation 68 the metric can be different per volume. In such a case, the motion assessment volume 66 comprises two or more constituent volumes and the displacement versus time curve 70 is generated by operations including, for each time interval bin, computing a statistical displacement metric for each constituent volume and combining (e.g. adding together, or averaging) the statistical displacement metrics for the constituent volumes.” [0057]; Fig. 5) for a plurality of conditions (“it is contemplated for the motion assessment volume 66 to comprise two (or more) volume regions which are separated in space.” [0057]. A plurality of conditions corresponds to a number of volume regions that may be used in the averaging) obtained by varying a percentage of counts (the percentage of counts is defined by counts in the time bins that are a portion, a percentage of total counts of the scan, and it varies because different time bins or time bin combinations and a different number of time bins can be used) to be used in a reconstructing process (50) (84) (“A respiratory motion estimation method (30) includes reconstructing emission imaging data (22) to generate a reconstructed image (50)." Abstract; Figs. 2 and 7; “FIG. 5 diagrammatically illustrates generation of a one-dimensional (1D) displacement versus time curve by computing total counts of emission imaging data in the motion assessment volume of FIG. 4 for successive time interval bins. [0023]. “(The total count metric is a surrogate for the displacement of the lung/diaphragm boundary—hence this curve is also referred to herein as the displacement metric versus time curve 70).” [0048], Fig. 5; “In an operation 84, the phase-specific emission imaging data sub-set is reconstructed. The image reconstruction operation 84 is intended to generate a high-quality reconstructed image suitable for tasks such as clinical diagnosis or clinical evaluation.” [0058]) and
to determine the percentage (the percentage of counts corresponding to the “phase-specific emission imaging data sub-set” [0058]; Fig. 7) on a basis of a relationship (70) (Abstract; “the displacement versus time curve 70” [0057]) between the percentage (corresponding to the time bins in Fig. 5) and the mean distance (the mean distance is the metric, the “displacement” corresponding to the selected time bins for reconstruction. “In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval)… the end-exhalation phase” [0058]. For the “Metric” for the “end-exhalation phase” in Fig. 5) (the percentage of counts corresponding to the “time interval bins n and n+4” [0058]; Fig. 5); and
to generate a gated image by carrying out an image reconstruction while using the determined percentage as a gating condition (“With returning reference to FIG. 1 and with further reference to FIG. 7, an illustrative embodiment of the respiration-gated image reconstruction process 40 is described. In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval). Typically, the respiratory phase chosen for reconstruction is the end-exhalation phase as this is the longest quiescent phase of the respiratory cycle; however, another phase could be chosen. Note that the selected emission imaging data sub-set will generally include a large number of time interval bins from successive respiratory cycles—for example, the end-exhalation phase for the example of FIG. 5 would include (i.e. combine) the emission imaging data in time interval bins n and n+4 as both of these time bins correspond to end-exhalation.” [0058]).
Tsui does not explicitly teach a reference phase vector and a Euclidean distance.
However, in the medical imaging field of endeavor, Zhao discloses systems and methods for image correction, which is analogous art. Zhao teaches a reference phase vector (“a reference sub-phase” [0057]; one of the “motion vectors” for the “reference time point.” [0176]) (“the determination module 420 may determine multiple motion vector fields of a target object corresponding to multiple motion time points (e.g., multiple sub-phases of the target phase) based on a reference time point (e.g., a reference sub-phase related to the target phase)." [0057]. “As mentioned above, the reference time point may be a time point used to describe relative displacements of the target object during the imaging process (e.g., the tomography). In some embodiments, a relative displacement of the target object at a motion time point may be a position change of the target object between the motion time point and the reference time point.” [0162]; “a motion vector field F.sub.1 of the target object at motion time point T.sub.1 may include motion vectors” [0176]; Fig. 8).
Therefore, based on Zhao’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Tsui to calculate a distance between each of feature vectors and a reference phase vector, as taught by Zhao, in order to facilitate image reconstruction by taking into account a position change of the target object during imaging.
Tsui as modified by Zhao does not teach that the distance is a Euclidean distance, the Euclidean distance being between each of feature vectors corresponding to frames and a reference vector.
However, in the medical image reconstruction field of endeavor, Matsuura discloses apparatus and method for image reconstruction using feature-aware deep learning, which is analogous art. Matsuura teaches that the distance is a Euclidean distance (“Euclidean distance” [0046]), the Euclidean distance being between each of feature vectors (xj) corresponding to frames and a reference phase vector (xl) (“l-2 norm or Euclidean distance, which is given by …can be used the measure of distance between the patches of pixels surrounding pixel j and pixel l,” [0046]).
Therefore, based on Matsuura’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Tsui and Zhao to employ a Euclidean distance, the Euclidean distance being between each of feature vectors and a reference vector, as taught by Matsuura, in order to facilitate image reconstruction by reducing variations/noise in the reconstructed PET images (Matsuura: [0045]-[0046]). In the combined invention of Tsui, Zhao, and Matsuura, the Euclidean distance is between each of feature vectors corresponding to frames and a reference phase vector, and a mean distance is a mean Euclidean distance.
Regarding claim 2, Tsui modified by Zhao and Matsuura teaches the nuclear medicine diagnosis apparatus according to claim 1, wherein Tsui teaches that the relationship is a mean distance plot (70) (“the displacement versus time curve 70” [0057]) obtained by plotting values of the mean distance with respect to the percentage (“it is contemplated for the motion assessment volume 66 to comprise two (or more) volume regions which are separated in space. Said another way, the motion assessment volume 66 may comprise two or more constituent volumes, and in the operation 68 the metric can be different per volume. In such a case, the motion assessment volume 66 comprises two or more constituent volumes and the displacement versus time curve 70 is generated by operations including, for each time interval bin, computing a statistical displacement metric for each constituent volume and combining (e.g. adding together, or averaging) the statistical displacement metrics for the constituent volumes.” [0057]; Fig. 5).
Tsui as modified by Zhao does not teach that the distance is a Euclidean distance, the Euclidean distance being between each of feature vectors corresponding to frames and a reference vector.
However, in the medical image reconstruction field of endeavor, Matsuura discloses apparatus and method for image reconstruction using feature-aware deep learning, which is analogous art. Matsuura teaches that the distance is a Euclidean distance (“Euclidean distance” [0046]), the Euclidean distance being between each of feature vectors (xj) corresponding to frames and a reference phase vector (xl) (“l-2 norm or Euclidean distance, which is given by …can be used the measure of distance between the patches of pixels surrounding pixel j and pixel l,” [0046]).
Therefore, based on Matsuura’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Tsui and Zhao to employ a Euclidean distance, the Euclidean distance being between each of feature vectors and a reference vector, as taught by Matsuura, in order to facilitate image reconstruction by reducing variations/noise in the reconstructed PET images (Matsuura: [0045]-[0046]). In the combined invention of Tsui, Zhao, and Matsuura, the mean distance plot is a Mean Euclidean Distance (MED) plot.
Regarding claim 8, Tsui modified by Zhao and Matsuura teaches the nuclear medicine diagnosis apparatus according to claim 1, wherein Tsui teaches that the processing circuitry is configured to generate mini-frame images by reconstructing the list mode data with respect to each of the mini frames (“to generate a corresponding “bin image”” [0045]) and to generate the feature vectors (“The motion vector images” [0010]; “the displacement metric” [0045]) from the mini-frame images (“A straightforward approach is to reconstruct the emission imaging data of each time interval bin to generate a corresponding “bin image”, and directly measure the displacement of the motion assessment image feature 60 in each bin image. In this case, the displacement metric … is a direct measure of the displacement.” [0045])
Regarding claim 10, Tsui modified by Zhao and Matsuura teaches the nuclear medicine diagnosis apparatus according to claim 1, wherein Tsui teaches the feature vectors corresponding to a quiescent phase (“The illustrative respiration-gated image reconstruction process 40 operates to reconstruct a sub-set of the emission imaging data 22 corresponding to a selected respiratory phase (typically end-exhalation, as this phase is quiescent and of long duration) to generate a reconstructed image with reduced blurring due to respiratory motion.” [0040]; In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval). Typically, the respiratory phase chosen for reconstruction is the end-exhalation phase as this is the longest quiescent phase of the respiratory cycle;” [0058]).
Tsui does not explicitly teach the reference phase vector.
However, in the medical imaging field of endeavor, Zhao discloses systems and methods for image correction, which is analogous art. Zhao teaches the reference phase vector (“a reference sub-phase” [0057]; one of the “motion vectors” for the “reference time point.” [0176]).
Therefore, based on Zhao’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Tsui to employ a reference phase vector, as taught by Zhao, in order to facilitate image reconstruction by taking into account a position change of the target object during imaging. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the processing circuitry that is configured to generate the reference phase vector by averaging the feature vectors corresponding to a quiescent phase, because Tsui discloses the feature vectors corresponding to a quiescent phase (Tsui: [0040]) and because generating the reference phase vector by averaging the feature vectors corresponding to a quiescent phase is a straightforward improvement of the method with a predictable result of providing a more accurate reference phase vector.
Regarding claim 11, Tsui teaches a method (Abstract) comprising:
generating a feature vector (“The motion vector images” [0010]. “Respiratory (e.g. breathing) motion estimation techniques disclosed herein operate by quantifying motion of an organ, anatomical boundary, or other image feature that serves as the motion assessment image feature.” [0026]; “the motion assessment image feature” [0029]) for each of mini frames (“The top row of FIG. 5 shows idealized “bin images” that visualize what would be reconstructed for each time interval bin in the absence of noise (this is not physically realizable due to the low total counts in each time interval bin which would lead to the bin images having low SNR).” [0048]) from list mode data acquired by performing a nuclear medicine scan (“binning the acquired emission list-mode data into time interval bins based on time stamps of the LORs or projections” [0006]; “generating a displacement versus time curve representing motion of the motion assessment image feature from the acquired emission list mode data.” [0007]) and calculating a distance (“Metric” Fig. 5) (“In this example, the displacement metric computed for each time interval bin is the total count of LORs or projections that belong to the time interval bin and intersect the motion assessment volume 66.” [0048]; “the displacement metric” [0050]) between each of feature vectors (“the displacement metric computed for each time interval bin” [0048]) corresponding to frames (“bin images” [0053]; Fig. 5) (The displacement metric for each time interval bin is a statistical metric that serves as a surrogate for determining the actual displacement of the motion assessment image feature in each time interval bin.” [0032]; “A straightforward approach is to reconstruct the emission imaging data of each time interval bin to generate a corresponding “bin image”, and directly measure the displacement of the motion assessment image feature 60 in each bin image. In this case, the displacement metric is not a statistical displacement metric, but rather is a direct measure of the displacement.” [0045]) and a reference phase vector (a reference phase vector corresponds to zero displacement of the “displacement metric” [0048]);
calculating, on a basis of the distances, a mean distance (“averaging… the statistical displacement metrics” [0057]) (“it is contemplated for the motion assessment volume 66 to comprise two (or more) volume regions which are separated in space. Said another way, the motion assessment volume 66 may comprise two or more constituent volumes, and in the operation 68 the metric can be different per volume. In such a case, the motion assessment volume 66 comprises two or more constituent volumes and the displacement versus time curve 70 is generated by operations including, for each time interval bin, computing a statistical displacement metric for each constituent volume and combining (e.g. adding together, or averaging) the statistical displacement metrics for the constituent volumes.” [0057]; Fig. 5) for a plurality of conditions (“it is contemplated for the motion assessment volume 66 to comprise two (or more) volume regions which are separated in space.” [0057]. A plurality of conditions corresponds to a number of volume regions that may be used in the averaging) obtained by varying a percentage of counts (the percentage of counts is defined by counts in the time bins that are a portion, a percentage of total counts of the scan, and it varies because different time bins or time bin combinations and a different number of time bins can be used) to be used in a reconstructing process (50) (84) (“A respiratory motion estimation method (30) includes reconstructing emission imaging data (22) to generate a reconstructed image (50)." Abstract; Figs. 2 and 7; “FIG. 5 diagrammatically illustrates generation of a one-dimensional (1D) displacement versus time curve by computing total counts of emission imaging data in the motion assessment volume of FIG. 4 for successive time interval bins. [0023]. “(The total count metric is a surrogate for the displacement of the lung/diaphragm boundary—hence this curve is also referred to herein as the displacement metric versus time curve 70).” [0048], Fig. 5; “In an operation 84, the phase-specific emission imaging data sub-set is reconstructed. The image reconstruction operation 84 is intended to generate a high-quality reconstructed image suitable for tasks such as clinical diagnosis or clinical evaluation.” [0058]); and
generating a gated image by carrying out an image reconstruction while using the determined percentage as a gating condition (“With returning reference to FIG. 1 and with further reference to FIG. 7, an illustrative embodiment of the respiration-gated image reconstruction process 40 is described. In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval). Typically, the respiratory phase chosen for reconstruction is the end-exhalation phase as this is the longest quiescent phase of the respiratory cycle; however, another phase could be chosen. Note that the selected emission imaging data sub-set will generally include a large number of time interval bins from successive respiratory cycles—for example, the end-exhalation phase for the example of FIG. 5 would include (i.e. combine) the emission imaging data in time interval bins n and n+4 as both of these time bins correspond to end-exhalation.” [0058]).
Tsui does not explicitly teach a reference phase vector and a Euclidean distance.
However, in the medical imaging field of endeavor, Zhao discloses systems and methods for image correction, which is analogous art. Zhao teaches a reference phase vector (“a reference sub-phase” [0057]; one of the “motion vectors” for the “reference time point.” [0176]) (“the determination module 420 may determine multiple motion vector fields of a target object corresponding to multiple motion time points (e.g., multiple sub-phases of the target phase) based on a reference time point (e.g., a reference sub-phase related to the target phase)." [0057]. “As mentioned above, the reference time point may be a time point used to describe relative displacements of the target object during the imaging process (e.g., the tomography). In some embodiments, a relative displacement of the target object at a motion time point may be a position change of the target object between the motion time point and the reference time point.” [0162]; “a motion vector field F.sub.1 of the target object at motion time point T.sub.1 may include motion vectors” [0176]; Fig. 8).
Therefore, based on Zhao’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the invention of Tsui to calculate a distance between each of feature vectors and a reference phase vector, as taught by Zhao, in order to facilitate image reconstruction by taking into account a position change of the target object during imaging.
Tsui as modified by Zhao does not teach that the distance is a Euclidean distance, the Euclidean distance being between each of feature vectors corresponding to frames and a reference vector.
However, in the medical image reconstruction field of endeavor, Matsuura discloses apparatus and method for image reconstruction using feature-aware deep learning, which is analogous art. Matsuura teaches that the distance is a Euclidean distance (“Euclidean distance” [0046]), the Euclidean distance being between each of feature vectors (xj) corresponding to frames and a reference phase vector (xl) (“l-2 norm or Euclidean distance, which is given by …can be used the measure of distance between the patches of pixels surrounding pixel j and pixel l,” [0046]).
Therefore, based on Matsuura’ teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combined invention of Tsui and Zhao to employ a Euclidean distance, the Euclidean distance being between each of feature vectors and a reference vector, as taught by Matsuura, in order to facilitate image reconstruction by reducing variations/noise in the reconstructed PET images (Matsuura: [0045]-[0046]). In the combined invention of Tsui, Zhao, and Matsuura, the Euclidean distance is between each of feature vectors corresponding to frames and a reference phase vector, and a mean distance is a mean Euclidean distance.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Tsui, Zhao, and Matsuura as applied to claim 2, and further in view of Khazen; et al (US 20070211958), hereinafter Khazen.
Regarding claim 3, Tsui modified by Zhao and Matsuura teaches the nuclear medicine diagnosis apparatus according to claim 2, wherein Tsui teaches the mean distance plot (70) (“the displacement versus time curve 70” [0057]). In the combined invention of Tsui, Zhao, and Matsuura, the mean distance plot is the MED plot.
Tsui as modified by Zhao and Matsuura does not teach that the processing circuitry is configured to determine the percentage, on a basis of a MED curve obtained by curve-fitting the MED plot.
However, in the image processing field of endeavor, Khazen discloses a method and means for image processing, which is analogous art. Khazen teaches a curve obtained by curve-fitting the plot (“(FIG. 1 shows an example of the distribution of the values of ln(.alpha.) for a dynamic breast MR measurement (the solid line)…One can identify a cluster corresponding to the small values of a (shown by the fitted Normal Distribution (Gaussian) curve - the dashed line).” [0113]. “The Gauss curve modelling the fat cluster in the feature space is fitted varying mean and standard deviation of the Gauss curve” [0164]).
Therefore, based on Khazen’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined invention of Tsui, Zhao, and Matsuura to employ a curve obtained by curve-fitting the plot, as taught by Khazen, in order to facilitate data analysis by replacing empirical plotted data with smooth differentiable curves. In the combined invention of Tsui, Zhao, Matsuura, and Khazen, the processing circuitry is configured to determine the percentage, on a basis of a MED curve obtained by curve-fitting the MED plot.
Regarding claim 4, Tsui modified by Zhao, Matsuura, and Khazen teaches the nuclear medicine diagnosis apparatus according to claim 3, wherein Tsui teaches that the processing circuitry is configured to set an upper threshold (corresponds to total counts for the scan) and a lower threshold (corresponds to zero counts) with respect to the percentage (the percentage of counts corresponding to the “phase-specific emission imaging data sub-set” [0058]; Fig. 7), and
the processing circuitry is configured to determine the percentage by calculating a weighted average of a value of the mean Euclidean distance at a point corresponding to the upper threshold on the mean distance curve and a value of the mean distance at a point corresponding to the lower threshold on the MED curve (“In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval)… the end-exhalation phase” [0058]. The calculating a weighted average with arbitrary weights and thresholds as claimed amounts to selecting a percentage that corresponds to a portion of total counts, i.e. not all counts are used, which corresponds to the use of the “emission imaging data sub-set” [0058]).
In the combined invention of Tsui, Zhao, Matsuura, and Khazen, the mean distance curve and the mean distance are the MED curve and the mean Euclidean distance.
Regarding claim 5, Tsui modified by Zhao, Matsuura, and Khazen teaches the nuclear medicine diagnosis apparatus according to claim 3, wherein Tsui teaches that the processing circuitry is configured to set an upper threshold (corresponds to total counts for the scan) and a lower threshold (corresponds to zero counts) with respect to the percentage (the percentage of counts corresponding to the “phase-specific emission imaging data sub-set” [0058]; Fig. 7), and
the processing circuitry is configured to determine the percentage by identifying, on the distance curve, a point that maximizes a distance to a straight line passing a point corresponding to the upper threshold on the distance curve and a point corresponding to the lower threshold on the distance curve (“The illustrative respiration-gated image reconstruction process 40 operates to reconstruct a sub-set of the emission imaging data 22 corresponding to a selected respiratory phase (typically end-exhalation, as this phase is quiescent and of long duration) to generate a reconstructed image with reduced blurring due to respiratory motion.” [0040] “In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval)… the end-exhalation phase” [0058]. The identifying with arbitrary thresholds as claimed amounts to selecting a percentage that corresponds to counts of quiescent phase as in [0040]).
In the combined invention of Tsui, Zhao, Matsuura, and Khazen, the mean distance curve is the MED curve.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Tsui, Zhao, Matsuura, and Khazen as applied to claim 3, and further in view of Syeda-Mahmood et al (US 20200185083), hereinafter Syeda-Mahmood.
Regarding claim 6, Tsui modified by Zhao, Matsuura, and Khazen teaches the nuclear medicine diagnosis apparatus according to claim 3, wherein Tsui teaches that the processing circuitry is configured to determine the percentage (the percentage of counts corresponding to the “phase-specific emission imaging data sub-set” [0058]; Fig. 7).
Tsui as modified by Zhao, Matsuura, and Khazen does not teach determining the percentage by calculating a second derivative with respect to the MED curve.
However, in the medical imaging field of endeavor, Syeda-Mahmood discloses automatic summarization of medical imaging studies, which is analogous art. Syeda-Mahmood teaches calculating a second derivative with respect to a curve (“Change points on the curve correspond to inflection points, i.e. places where there are zero-crossings of the second derivative. Salient change points are those that are preserved even after multiple levels of smoothing.” [0039], Fig. 1A).
Therefore, based on Syeda-Mahmood’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined invention of Tsui, Zhao, Matsuura, and Khazen to calculate a second derivative with respect to a curve, as taught by Syeda-Mahmood, in order to facilitate data analysis by making use of analytical properties of smooth differentiable curves. In the combined invention of Tsui, Zhao, Matsuura, Khazen, and Syeda-Mahmood, the processing circuitry is configured to determine the percentage by calculating a second derivative with respect to the MED curve.
Regarding claim 7, Tsui modified by Zhao, Matsuura, and Khazen teaches the nuclear medicine diagnosis apparatus according to claim 3, wherein Tsui teaches that the processing circuitry is configured to set an upper threshold (corresponds to total counts for the scan) and a lower threshold (corresponds to zero counts) with respect to the percentage (the percentage of counts corresponding to the “phase-specific emission imaging data sub-set” [0058]; Fig. 7), and
the processing circuitry is configured to determine a first percentage by identifying, on the distance curve, a point that maximizes a distance to a straight line passing a point corresponding to the upper threshold on the distance curve and a point corresponding to the lower threshold on the distance curve (“The illustrative respiration-gated image reconstruction process 40 operates to reconstruct a sub-set of the emission imaging data 22 corresponding to a selected respiratory phase (typically end-exhalation, as this phase is quiescent and of long duration) to generate a reconstructed image with reduced blurring due to respiratory motion.” [0040] “In an operation 82, an emission imaging data sub-set is selected from the emission imaging data 22 which corresponds to a specific respiratory phase (or more generally, gating interval)… the end-exhalation phase” [0058]. The identifying with arbitrary thresholds as claimed amounts to selecting a percentage that corresponds to counts of quiescent phase as in [0040]). In the combined invention of Tsui, Zhao, Matsuura, and Khazen, the mean distance curve is the MED curve.
Tsui as modified by Zhao and Matsuura does not teach determining a second percentage by calculating a second derivative with respect to the MED curve.
However, in the medical imaging field of endeavor, Syeda-Mahmood discloses automatic summarization of medical imaging studies, which is analogous art. Syeda-Mahmood teaches calculating a second derivative with respect to a curve (“Change points on the curve correspond to inflection points, i.e. places where there are zero-crossings of the second derivative. Salient change points are those that are preserved even after multiple levels of smoothing.” [0039], Fig. 1A).
Therefore, based on Syeda-Mahmood’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined invention of Tsui, Zhao, and Matsuura to calculate a second derivative with respect to a curve, as taught by Syeda-Mahmood, in order to facilitate data analysis by making use of analytical properties of smooth differentiable curves. In the combined invention of Tsui, Zhao, Matsuura, and Syeda-Mahmood, the processing circuitry is configured to determine a second percentage by calculating a second derivative with respect to the MED curve, and the processing circuitry is configured to determine the percentage by calculating a weighted average of the first percentage and the second percentage. One of ordinary skill in the art would want to make image data processing more robust by using not one but several estimates and by combining the estimates to obtain a weighted average as claimed.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Tsui, Zhao, and Matsuura as applied to claim 8, and further in view of Narasimha et al (US 20240260917), hereinafter Narasimha.
Regarding claim 9, Tsui modified by Zhao and Matsuura teaches the nuclear medicine diagnosis apparatus according to claim 8.
Tsui as modified by Zhao and Matsuura does not teach that the feature vectors are latent vectors obtained by encoding the mini-frame images.
However, in the image processing field of endeavor, Narasimha discloses a method and device for generating a vessel imaging sequence, which is analogous art. Narasimha teaches that the feature vectors are latent vectors obtained by encoding the mini-frame images (“encoding, using a first encoder, the input vessel imaging sequence to generate a plurality of vessel latent space vectors, each vessel latent space vector corresponding to an input vessel imaging frame of the input vessel imaging sequence" [0005]; “a method for generating a vessel imaging sequence including a plurality of vessel imaging frames …, including obtaining an input vessel imaging sequence including a plurality of input vessel imaging frames …, encoding, using a first encoder, the input vessel imaging sequence to generate a plurality of vessel latent space vectors, each vessel latent space vector corresponding to an input vessel imaging frame of the input vessel imaging sequence,” [0126]).
Therefore, based on Narasimha’s teachings, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined invention of Tsui, Zhao, and Matsuura to employ the feature vectors that are latent vectors obtained by encoding the mini-frame images, as taught by Narasimha, in order to facilitate image data analysis by reducing the processing time.
Conclusion
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/ALEXEI BYKHOVSKI/
Primary Examiner, Art Unit 3798