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 Rejections - 35 USC § 103
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.
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.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over An et al. (US 2018/0204358; hereinafter An) in view of Amit et al. (US 2020/0163641; hereinafter Amit) and Li et al. (US 2016/0104279; hereinafter Li).
An shows a method for reconstructing a set of one or more magnetic resonance imaging (MRI) images from one or more segmented acquisitions of MRI raw data, each of the one or more segmented acquisitions including a plurality of shots ([0089], [0094]), the method comprising: for each respective shot of the plurality of shots capturing a part of the MRI raw data of the respective MRI images, capturing and reconstructing a low-resolution navigation image of a region of interest, from additional MRI raw data acquired adjacent to the part of the MRI raw data in time ([0096]); clustering navigation images from the plurality of shots within each MRI image of the one or more MRI images according to a characteristic of the navigation images ([0098], [0153]); excluding a navigation image of the navigation images and a corresponding part of the MRI raw data from further processing when the navigation image is identified ([0126]-[0127], [0151], [0157]-[0158], [0171]); estimating motion values of the object of interest from each remaining navigation image with respect to a reference navigation image within the respective cluster ([0129]-[0134]); normalizing the motion values for all data clusters across the one or more MRI images within the acquired set of one or more MRI images ([0135]); and performing a final reconstruction of the set of one or more MRI images using the motion values ([0158]).
An also shows wherein each shot of the plurality of shots is captured at a corresponding point in a cardiac cycle ([0093]-[0094], [0159]); wherein the characteristic for clustering the navigation images is a time of capture within a cardiac cycle ([0096], [0131]-[0133]); wherein the characteristic for clustering the navigation images is a characteristic of a navigation image signal ([0095], [0098]); wherein clusters of the navigation images are identified as outliers ([0126]-[0127], [0151], [0157]-[0158], [0171]); wherein the reference navigation image for estimating motion values of the object of interest is identified as having a value of the characteristic corresponding to a center of values of the navigation images of the respective cluster ([0099]); wherein the normalizing of motion values between clusters within and across at least one MRI image of the one or more MRI images comprises, for each cluster in each MRI image of the at least one MRI image, subtracting a motion value representing an average motion position from all the motion values in the respective cluster ([0135]); wherein the set of one or more MRI images includes a single MRI image for which a single non-outlier cluster is identified ([0126]-[0127], [0151], [0157]-[0158], [0171]).
An further shows wherein the normalizing of motion values between clusters within and across at least one MRI image of the one or more MRI images comprises, for each cluster in each MRI image of the at least one MRI image, subtracting a motion value representing an average motion position from all the motion values in the respective cluster ([0135]). An states that “In various other aspects, the movement distances may be normalized using any known method of normalization without limitation. Non-limiting examples of suitable normalization methods include: subtracting the average of all movement distances from each movement distance, dividing all the movement distances by a maximum movement distance, and any combination thereof” ([0135]).
An fails to show identifying an outlier during the clustering and excluding the outlier identified during the clustering.
An fails to explicitly state wherein normalizing the motion values between clusters within and across at least on MRI image of the one or more MRI images comprises, for each cluster in each MRI image of the at least one MRI image, subtracting a motion value representing an end-expiratory motion position from all motion values in the respective cluster.
An fails to show excluding…before motion values of the object of interest are estimated. An also fails to show wherein the characteristic of the navigation image signal for clustering the navigation images is a normalized cross-correlation; wherein estimating motion values of the object of interest is performed using a normalized cross-correlation of image signals of regions of interest within the navigation images.
Amit discloses systems and methods for medical image data analysis. Amit teaches identifying an outlier during the clustering and excluding the outlier identified during the clustering ([0003], [0013], [0020], [0035]-[0039]).
Li discloses methods and systems for MRI examination. Li teaches excluding…before motion values of the object of interest are estimated (processed respiratory pattern with outliers discarded results in a more uniform binning result that allows subsequent accurate motion estimation; [0018], [0072]-[0079]). Li teaches wherein the characteristic of the navigation image signal for clustering the navigation images is a normalized cross-correlation ([0064]; wherein estimating motion values of the object of interest is performed using a normalized cross-correlation of image signals of regions of interest within the navigation images ([0064]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of An to identify an outlier during the clustering as taught by Amit, as Amit teaches that clustering techniques may be utilized efficiently to identify outliers, as clustering groups of data having similar properties will highlight data points which stand out from the majority of the clustered groups, and thereby improve the overall accuracy of the image analysis techniques ([0038]). Additionally, it would be an obvious design choice to one of ordinary skill in the art, without undue experimentation, to select from known image analysis techniques such as clustering to identify outliers, or other known image analysis techniques, in order to improve the quality and accuracy of the obtained medical images and ultimately an improved medical diagnosis.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of An to utilize other types of normalization methods as recognized by An ([0135)), such as by for each cluster in each MRI image of the at least one MRI image, subtracting a motion value representing an end-expiratory motion position from all motion values in the respective cluster. A variety of different normalization techniques using different motion related variables are known by one of ordinary skill in the art as recognized by An ([0135]). It would be within the level of ordinary skill in the art to utilize an end-expiratory motion position, as this motion position is representative of an end state of the respiratory cycle. For example, An recognizes that characteristic values shift to higher values during an expiration phase versus during an inspiration phase, indicating movements of organ positions at various respiration phases that may be distinguished using the navigator motion resolution method ([0130]). It would be a design choice to utilize other normalization techniques involving key features of the respiratory cycle as recognized by An ([0135]) and would be accomplished by one of ordinary skill in the art without undue experimentation.
It would have been 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 An and Amit to exclude before motion values of the object of interest are estimated as taught by Li, as Li teaches that the processed respiratory pattern with outliers discarded results in a more uniform binning result that allows subsequent accurate motion estimation ([0018]-[0019]). It would have been 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 An and Amit to utilize normalized cross-correlation techniques as taught by Li, in order to aid in detecting the respiratory motion of the patient by applying known signal analysis techniques to increase the accuracy of the motion detection.
Response to Arguments
Applicant's arguments filed 12/26/25 have been fully considered but they are not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant’s arguments regarding Li, the examiner notes that Li is relied upon to teach excluding before motion values of the object of interest are estimated. Li teaches that discarding outliers results in a more uniform binning that allows subsequent accurate motion estimation ([0018]-[0019]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHAN CWERN/ Primary Examiner, Art Unit 3797