DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 27, 2026, has been entered.
Response to Amendment
Claims 1, 10, and 14 have been amended. Claims 2, 3, 5-9, and 11-13 have been canceled. Claims 15-17 are new. Claims 1, 4, 10, and 14-17 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant's arguments filed February 27, 2026, have been fully considered but they are not persuasive. A response is provided below in bold where appropriate.
Applicant argues 35 USC §101 Rejection, starting pg. 6 of Remarks:
CLAIM REJECTIONS UNDER 35 USC 101
Claims 1, 2, 4, 5, 10 and 14 are rejected under 35 USC §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In response, the Examiner's rejections are respectfully traversed.
Turning to the analysis of the claims under the legal framework which was described in full detail previously, under Step 1 the Examiner admits that the claims fall into at least one of the four categories recited in 35 USC § 101.
Under Step 2A (Prong One), contrary to the Examiner's statement, the Applicant's computer-implemented method that comprises such actions as acquire, segment and register a medical image can't be considered mental processes under BRI, because they can only be performed by a computer, not by a human mind. That is, regarding the recited features of "segmenting the medical image using a neural network and registering the medical image with an atlas," it is respectfully submitted that humans simply cannot perform those operations, despite the Examiner's remarks in the Office Action. Under the Guidance and Example 47, once the claim recites a concrete machine-learning pipeline that improves image-quality assessment, the recited steps are not reasonably understood as mental steps, and the claim no longer recites a judicial exception (Step 2A-Prong One ends eligibility).
Respectfully, segmenting using a neural network is recited at a high level of generality, and a person can segment an image.
From Applicant’s specification on atlas…
“The atlas may include a statistically averaged anatomical map of one or more body portions. At least a portion of the atlas may be indicative of or may represent a two-dimensional or three-dimensional shape of a portion of the body, in particular an anatomical portion of the body. By way of example, at least a portion of the atlas may be indicative of or may represent a three-dimensional outer surface of one or more anatomical or functional portions of the body. By way of example, the anatomical portion of the body may be one or more bones and/or one or more portions of bones.” (pg. 6, lines 26-32)
A person can with pen can write on the image and the atlas marks that link or register the segmented image to the atlas. Registering is not defined by the specification and could be just about anything that associates an image with an atlas.
The cases that relate to mental processes are directed to using categories to organize, store and transmit information, as in Cyberfone Systems, LLC v. CNN Interactive Grp, Inc., 558 F. App'x 988 (Fed. Cir. 2014), data recognition and storage, as in Content Extraction and Transmission LLC v. Wells Fargo Bank, National Ass'n, 776 F.3d 1343, (Fed. Cir. 2014), and organizing information through mathematical correlation, as in Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014). It is not clear how those cases are pertinent to the Applicant's claims directed to assessing diagnostic quality of medical image data.
Mental Processes are not case specific but one of three abstract groupings provided by the Office and reflect activities that can be performed in the mind of a person or with pen and paper.
Thus, Applicant's claims contain patent eligible subject matter under Step 2A, Prong One.
Even if we consider a hypothetical analysis under which the Examiner's assertion is correct under Step 2A, the claims recite additional elements that integrate the judicial exception into a practical application. The claims recite a system and method for analyzing medical image data. Considering the claims as a whole, interaction of all the steps using the processor applies the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole are more than a drafting effort designed to monopolize the exception. The Applicant's invention is directed to an improved system and method for generating medical images and assessing their diagnostic quality in assisting medical professionals. Thus, the abstract idea is integrated into a practical application.
The improvement cannot be to a judicial exception. There is no indication that computer technology itself is improved.
In addition, the examiner's attention is directed to MPEP 2111.02 on the effect of the preamble. "If the claim preamble is necessary to give life, meaning and vitality to the claim, then the claim preamble should be construed as if in the balance of the claims" See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165-66 (Fed. Cir. 1999). "Any terminology in the preamble that limits the structure of the claimed invention must be treated as a claim limitation." See, e.g., Corning Glass Works v. Sumitomo Elec. U.S.A., Inc., 868 F.2d 1251, 1257, 9 USPQ2d 1962, 1966 (Fed. Cir. 1989).
In the present invention, the recited method must be considered in the context of a computer-implemented method for assessing diagnostic quality of medical image data. It is not a generic method for processing an image, but a specific one that is applicable to a specific field in the preamble. Clearly, the entire recited feature in the preamble is necessary to give meaning to Applicant's claim 1. The preamble also limits the structure of the claimed invention.
Specificity is not an additional element. An additional element is something that is not abstract. Computers have been shown not to be enough to make abstract claims statutory.
Under the Guidance and Example 47, once the claim recites a concrete machine-learning pipeline that improves image-quality assessment, the exception is integrated into a practical application (specific technical improvement to medical-imaging QC).
Example 47 had claims that were both statutory and non-statutory. Claim 1 was a specific neural network physical architecture and Claim 3 improved the technical field of network intrusion for improved network security. Applicant is not improving a physical architecture or network security.
As a result, Applicant's claims contain patent eligible subject matter under Step 2A, Prong Two.
Based on the above response, the rejection is respectfully maintained but modified for the claim amendments.
Applicant argues 35 USC §103 Rejection, starting pg. 8 of Remarks:
Applicant’s arguments are moot as new prior art is cited to address the claim amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4, 10, and 14-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 4, 10, and 14-17 are directed to a system, method, or product, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 10 as the claim that represents the claimed invention for analysis and is similar to system Claim 1 and product Claim 14.
Claim 10 recites the limitations of:
A computer-implemented method for assessing diagnostic quality of medical image data, comprising:
acquiring the medical image data, which represents a two-dimensional or three-dimensional medical image;
segmenting the medical image using a neural network;
registering the medical image with an atlas;
determining, for the medical image, a plurality of image quality metrics; and
determining a combined quality metric based on the plurality of image quality metrics, the combined quality metric being indicative of whether the medical image is diagnostic or non-diagnostic;
wherein the combined quality metric is based on an interaction among the image quality metrics of the plurality;
wherein the interaction among the image quality metrics of the plurality is such that a change in the combined quality metric, which is caused by a change of one image quality metric of the plurality, is compensable by a change of another image quality metric of the plurality;
wherein each of the image quality metrics of the plurality is associated with a respective optimum value or a predefined optimum range; and
wherein the interaction among the image quality metrics of the plurality is such that an increased deviation from the optimum range or the optimum value of a first metric or the plurality is compensable by a decreased deviation from the optimum range or the optimum value of another image quality metric of the plurality.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. The claim recites elements, in non-bold above, which covers performance of the limitation that can be concepts performed in the mind of a person or with pen and paper. Acquiring medical image data (person can read and comprehend in their mind image data), registering the image data with an atlas (person with pen and paper can register the image with an atlas), determining a plurality of image quality metrics (a person can determine in their mind quality metrics), a person can determine combined quality metrics (perform calculations/analysis in their mind of combined metrics) and determine whether the image is diagnostic or non-diagnostic, person determine increase deviation from a first optimum range is compensable by decreased deviation of optimal range of another metric. Further, a person can segment a medical image in their mind or using pen and paper. It has been shown that collecting (acquiring), analyzing (determining), and providing a result are mental processes (see MPEP 2106.04(a)(2) III A and Electric Power Group v. Alstom). Further, it’s been determined that using a generic computer, while not claimed, to perform a judicial exception can also be abstract, see MPEP 2106.04(a)(2) III C. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 14 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
The claims also recite acquire a medical image, segment the image using a neural network, register the medical image with an atlas, determine a plurality of image quality metrics, and determine a combined quality metric indicative of the medical image is diagnostic or non-diagnostic. The claims are therefore abstract as managing personal behavior by teaching (determine if medical image is diagnostic or non-diagnostic). See pg. 1, lines 8-17 and pg. 18, lines 1-16 where radiographer and radiologist are performing steps of acquiring image data and determining quality, and Fig. 1, ref. 2 of patient. For example, a change of a first metric is compensable by a change of one or more remaining metrics is providing teaching/instructions for acquiring a medical image to compensate for a change (see pg. 16, lines 4-9 of the instant disclosure which requires ankle movement, therefore, interaction of a metric is interaction of a physical movement of a person). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 14 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite: memory, processor, neural network (Claim 1); computer, neural network (Claim 10); non-transitory computer readable medium, processor, neural network (Claim 14). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The neural network is applied at a high level of generality. Registering an image with an atlas is insignificant extra solution activity as nothing is done with the atlas. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 10, and 14 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as acquiring (receiving) are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 10, and 14 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 4 and 15-17 further define the abstract idea that is present in their independent claims 1, 10, and 14 and thus correspond to Mental Processes and Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claim 4 recites an imager, which appears to be a generic imager recited at a high level of generality. Claims 15-17 express an image quality as a scalar value, which is converting an expression into a number which also abstract as a mathematical concept. Claims 4 and 15-17 also further limit abstract claim elements or are themselves abstract. Therefore, the claims 4 and 15-17 are directed to an abstract idea. Thus, the claims 1, 4, 10, and 14-17 are not patent-eligible.
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, 4, 10, and 14-17 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The terms “optimum value” or “optimum range” in claim 1 are relative terms which renders the claim indefinite. The term “optimum value” or “optimum range” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes, optimum value or optimum range are interpreted values or ranges based on an object at a fixed distance from an imaging device, the object at the focal plane, and the imaging device taking a single image, the imaging lens parallel to the focal plane. Therefore, an optimal value or range could be rotation or movement of an object within the focal plane. Claims 10 and 14-17 have a similar problem.
Claims 4 and 15-17 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
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 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.
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.
Claims 1, 4, 10, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2015/0265220 to Ernst et al. in view of Pub. No. US 2021/0049757 to Zhu et al. and in view of Pub. No. US 2021/0183055 to Rao et al. and in view of Maes et al. (Maes et al, “Medical Image Registration Using Mutual Information,” October, 2003, Proceedings of the IEEE Vol. 92, No. 10, pp. 1699-1722).
Regarding claims 1, 10, and 14
(claim 1) A system for assessing diagnostic quality of medical image data, comprising:
Ernst et al. teaches:
Comparing images (assessing) and save the better (quality) imaging data…
“In some embodiments, the system displays the motion-corrected and de-corrected images with a label indicating which set of imaging data is being shown. For example, the de-corrected images could be displayed with a label that states it is de-corrected imaging data. When comparing the images, the operator or doctor or other use may be enabled to save the better imaging data (or to indicate a preference for the motion-corrected or de-corrected data). In some embodiments, the doctor saves the entire 2D or 3D representation of the scanned area; however, in some embodiments, the doctor can also save slices individually to generate composite data, wherein the best image quality is used from each individual slice. In such embodiments, each individual slice may be labelled such that the individual slices of the composite scanned images are identified as motion-corrected or de-corrected. For example, the system may be configured to enable a user to generate a composite or merged set of scanned data or images that is created by interlacing or merging motion corrected and motion de-corrected images or slices into the same set of data (which may be created as a new set of data, or may be an edited version of existing data). In some embodiments, the system is configured to change the stored data by removing and/or adding individual slices. In some embodiments, the system is configured to generate a new set of data that comprises at least one of a motion corrected or de-corrected slice or image for each point in time of the subject scan. In some embodiments, the system is configured to generate or update header information for the dataset and/or individual images or slices, such as to indicate that a composite data set is being used, whether each slice or image has been motion corrected, and/or the like.” [0063]
2D or 3D data (image)…
“In block 604, the scanner acquires raw imaging data. The acquired data is compensated based on the tracked position of the subject by the scanner controller. Therefore, 2D or 3D data is generated with the position stored with respect to the position within the patient or subject instead of just to the position of the scanner. The raw imaging data is stored in a raw imaging database 605. The processes performed in blocks 610, 614, 616, and 618 are repeated by the system for each iteration of the scanner acquiring raw imaging data in block 604. For example, after outputting the position and/or motion data in block 616, and the scanning controller adjust the scanner in block 618, the system determines if the imaging scan is complete in block 620. If the scan is complete, the system moves on to block 622. If the scan is not complete, the motion compensation system repeats the processes of acquiring and analyzing the movement and/or position data of the subject.” [0068]
a memory that stores a plurality of instructions; and
Computer with memory…
“In certain embodiments, a computer implemented-method for generating motion de-corrected images in conjunction with a biomedical imaging scan comprises: tracking, by a computer system, motion of an object being scanned by a scanner; generating, by the computer system, motion tracking data indicating the position of the object being scanned; adjusting, by the computer system, a biomedical imaging scanner, using the motion tracking data, to compensate in real time for object motion, such that raw image data generated by the scanner can be reconstructed into motion-corrected images; inverting, by the computer system, the motion tracking data to generated inverted motion tracking data; and applying, by the computer system, the inverted motion tracking data to the raw image data to generate de-corrected image data representative of what the scanner would produce had the scanner not compensated for motion, wherein the de-corrected image data can be reconstructed into de-corrected images, wherein the computer system comprises an electronic memory and a computer processor.” [0011]
a processor that couples to the memory and is configured to execute the plurality of instructions to:
Memory and computer processor…
“In certain embodiments, a computer implemented-method for generating motion de-corrected images in conjunction with a biomedical imaging scan comprises: tracking, by a computer system, motion of an object being scanned by a scanner; generating, by the computer system, motion tracking data indicating the position of the object being scanned; adjusting, by the computer system, a biomedical imaging scanner, using the motion tracking data, to compensate in real time for object motion, such that raw image data generated by the scanner can be reconstructed into motion-corrected images; inverting, by the computer system, the motion tracking data to generated inverted motion tracking data; and applying, by the computer system, the inverted motion tracking data to the raw image data to generate de-corrected image data representative of what the scanner would produce had the scanner not compensated for motion, wherein the de-corrected image data can be reconstructed into de-corrected images, wherein the computer system comprises an electronic memory and a computer processor.” [0011]
Software (instructions) and module storage…
“In general, the word "module," as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, COBOL, CICS, Java, Lua, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software instructions may be embedded in firmware, such as an EPROM. It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays or processors. The modules described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage.” [0086]
acquire the medical image data, which represents a two-dimensional or three-dimensional medical image;
Doctor saves (acquire) 2D or 3D representation of scanned area (medical image)…
“In some embodiments, the system displays the motion-corrected and de-corrected images with a label indicating which set of imaging data is being shown. For example, the de-corrected images could be displayed with a label that states it is de-corrected imaging data. When comparing the images, the operator or doctor or other use may be enabled to save the better imaging data (or to indicate a preference for the motion-corrected or de-corrected data). In some embodiments, the doctor saves the entire 2D or 3D representation of the scanned area; however, in some embodiments, the doctor can also save slices individually to generate composite data, wherein the best image quality is used from each individual slice. In such embodiments, each individual slice may be labelled such that the individual slices of the composite scanned images are identified as motion-corrected or de-corrected. For example, the system may be configured to enable a user to generate a composite or merged set of scanned data or images that is created by interlacing or merging motion corrected and motion de-corrected images or slices into the same set of data (which may be created as a new set of data, or may be an edited version of existing data). In some embodiments, the system is configured to change the stored data by removing and/or adding individual slices. In some embodiments, the system is configured to generate a new set of data that comprises at least one of a motion corrected or de-corrected slice or image for each point in time of the subject scan. In some embodiments, the system is configured to generate or update header information for the dataset and/or individual images or slices, such as to indicate that a composite data set is being used, whether each slice or image has been motion corrected, and/or the like.” [0063]
Store 2D or 3D image data in database 605…
“In block 604, the scanner acquires raw imaging data. The acquired data is compensated based on the tracked position of the subject by the scanner controller. Therefore, 2D or 3D data is generated with the position stored with respect to the position within the patient or subject instead of just to the position of the scanner. The raw imaging data is stored in a raw imaging database 605. The processes performed in blocks 610, 614, 616, and 618 are repeated by the system for each iteration of the scanner acquiring raw imaging data in block 604. For example, after outputting the position and/or motion data in block 616, and the scanning controller adjust the scanner in block 618, the system determines if the imaging scan is complete in block 620. If the scan is complete, the system moves on to block 622. If the scan is not complete, the motion compensation system repeats the processes of acquiring and analyzing the movement and/or position data of the subject.” [0068]
Fig. 6A, ref. 622 teaches acquiring image data…
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segment the medical image using a neural network;
See Neural Network and Atlas below.
register the medical image with an atlas;
See Neural Network and Atlas below.
determine, for the medical image, a plurality of image quality metrics; and
Medical image scans (plurality of image metrics)…
“The disclosure herein provides systems, methods, and devices for removing prospective motion correction from medical imaging scans. Medical imaging scans are required for various medical treatments and/or diagnoses. A variety of biomedical imaging scan technologies are available, such as magnetic resonance imaging (MRI), computed tomography (CT), and the like. Some medical imaging scan technologies require that a subject be scanned for an extended period of time. During a scan, any motion of the patient or object being scanned can introduce motion artifacts and other errors into the resulting images. For example, if a patient is having their brain scanned in an MRI machine, the resulting images may have motion artifacts and/or be blurry if the user or patient moves his or her head during scan. Motion compensation systems provide techniques for removing or preventing motion artifacts from showing up in a scan.” [0024]
Determine which sets of data (metrics) is better (quality metric)…
“Although motion compensation systems can be beneficial in removing motion artifacts from medical imaging scans, some deficiencies in motion tracking and compensation systems exist. Because of potential deficiencies in compensation systems, some users of a medical imaging scanner, such as an MRI scanner operator, may not trust that the motion-compensated images are better than what would have been produced without motion compensation. Further, in some instances, such as, for example, when a patient did not move at all during a scan, a motion-compensated image may actually be of worse quality than if motion correction had not been applied. Accordingly, it can be beneficial to generate two or more sets of image data, some sets being motion-corrected, and other sets not being motion-corrected, so that a user can view the two or more sets of data to determine which is better to use and/or to instill a level of comfort that the motion-corrected data is better than the non-motion corrected data.” [0025]
determine a combined quality metric based on the plurality of image quality metrics, the combined quality metric being indicative of whether the medical image is diagnostic or non-diagnostic;
Medical imaging required for diagnosis…
“The disclosure herein provides systems, methods, and devices for removing prospective motion correction from medical imaging scans. Medical imaging scans are required for various medical treatments and/or diagnoses. A variety of biomedical imaging scan technologies are available, such as magnetic resonance imaging (MRI), computed tomography (CT), and the like. Some medical imaging scan technologies require that a subject be scanned for an extended period of time. During a scan, any motion of the patient or object being scanned can introduce motion artifacts and other errors into the resulting images. For example, if a patient is having their brain scanned in an MRI machine, the resulting images may have motion artifacts and/or be blurry if the user or patient moves his or her head during scan. Motion compensation systems provide techniques for removing or preventing motion artifacts from showing up in a scan.” [0024]
Determine which sets of data (metrics) is better (quality metric)…
“Although motion compensation systems can be beneficial in removing motion artifacts from medical imaging scans, some deficiencies in motion tracking and compensation systems exist. Because of potential deficiencies in compensation systems, some users of a medical imaging scanner, such as an MRI scanner operator, may not trust that the motion-compensated images are better than what would have been produced without motion compensation. Further, in some instances, such as, for example, when a patient did not move at all during a scan, a motion-compensated image may actually be of worse quality than if motion correction had not been applied. Accordingly, it can be beneficial to generate two or more sets of image data, some sets being motion-corrected, and other sets not being motion-corrected, so that a user can view the two or more sets of data to determine which is better to use and/or to instill a level of comfort that the motion-corrected data is better than the non-motion corrected data.” [0025]
Combining data sets…
“In some embodiments, tracking motion of an object being scanned comprises combining two or more sets of tracking data to generate a position of the object being scanned. In some embodiments, compensating in real time for object motion further comprises updating geometric parameters of the scanner based on an updated position of the object being scanned. In some embodiments, the scanner generates raw image data using a process comprising: exciting, by the biological image scanner, nuclei within the object being scanned; applying, by the biomedical image scanner, a magnetic field gradient across the object being scanned; and receiving, at a receiver coil, radiofrequency signals indicating one or more features of the object being scanned.” [0012]
Generate or update images and indicate if composite (combined) data set (metric) for images is being used…
“…In some embodiments, the system is configured to change the stored data by removing and/or adding individual slices. In some embodiments, the system is configured to generate a new set of data that comprises at least one of a motion corrected or de-corrected slice or image for each point in time of the subject scan. In some embodiments, the system is configured to generate or update header information for the dataset and/or individual images or slices, such as to indicate that a composite data set is being used, whether each slice or image has been motion corrected, and/or the like.” [0063]
See Diagnostic and Optimum Value below.
wherein the combined quality metric is based on an interaction among the image quality metrics of the plurality;
Various (plurality) tracking of position and orientation (image metrics) and movement (interactions)…
“Several possible tracking controllers or filters 202, as shown in FIG. 2, either in isolation or in combination, can be configured to track the object of interest. One embodiment of a tracking controller or filter 202, for example Tracking Controller 1 shown in FIG. 2, is configured to track the position and orientation of anatomical features or "landmarks" during subject movement, and uses this information to derive the object of interest's (for example, the subject's head) movement. For example, when tracking a subject's head, if the position of the subject's two eyes and the position of the tip of the subject's nose are known in detector coordinates, then the three translations and three rotations of the subject's head can be derived by means of triangulation or other methods. In general, accuracy of such a tracking controller or filter 202 can be improved by tracking a greater number of anatomical features. For example, if the position of a subject's nostrils and/or the bridge of the nose are tracked in addition to the nose tip and the eyes, then tracking of the subject's head can be generally more accurate. Tracking accuracy can also be improved by utilizing a greater number of detectors 102 and/or positioning the detectors 102 to view the subject's head from a variety of angles. Furthermore, in some embodiments, a single tracking controller or filter can be configured to provide data for less than all six degrees of freedom, i.e. less than three translations and three rotations, in which case information from one or more other tracking controllers or filters may be used to complete the tracking of all six degrees of freedom.” [0043]
wherein the interaction among the image quality metrics of the plurality is such that a change in the combined quality metric, which is caused by a change of one image quality metric of the plurality, is compensable by a change of another image quality metric of the plurality; and
Example of best quality image from each slice and generate (change in) composite or merged (combined) scanned data or images based on motion corrected (one image metric) and de-corrected images (another image metric)…
“In some embodiments, the system displays the motion-corrected and de-corrected images with a label indicating which set of imaging data is being shown. For example, the de-corrected images could be displayed with a label that states it is de-corrected imaging data. When comparing the images, the operator or doctor or other use may be enabled to save the better imaging data (or to indicate a preference for the motion-corrected or de-corrected data). In some embodiments, the doctor saves the entire 2D or 3D representation of the scanned area; however, in some embodiments, the doctor can also save slices individually to generate composite data, wherein the best image quality is used from each individual slice. In such embodiments, each individual slice may be labelled such that the individual slices of the composite scanned images are identified as motion-corrected or de-corrected. For example, the system may be configured to enable a user to generate a composite or merged set of scanned data or images that is created by interlacing or merging motion corrected and motion de-corrected images or slices into the same set of data (which may be created as a new set of data, or may be an edited version of existing data). In some embodiments, the system is configured to change the stored data by removing and/or adding individual slices. In some embodiments, the system is configured to generate a new set of data that comprises at least one of a motion corrected or de-corrected slice or image for each point in time of the subject scan. In some embodiments, the system is configured to generate or update header information for the dataset and/or individual images or slices, such as to indicate that a composite data set is being used, whether each slice or image has been motion corrected, and/or the like.” [0063]
wherein each of the image quality metrics of the plurality is associated with a respective optimum value or a predefined optimum range; and
See Optimum below.
wherein the interaction among the image quality metrics of the plurality is such that an increased deviation from the optimum range or the optimum value of a first metric or the plurality is compensable by a decreased deviation from the optimum range or the optimum value of another image quality metric of the plurality.
See Optimum below.
Neural Network and Atlas
Ernst et al. teaches medical images. They do not teach neural network and atlas.
Zhu et al. also in the business of medical images teaches:
Neural network with register atlas and image segmentation…
“In at least one embodiment, registration-based segmentation by multi-task learning (MTL) uses a dual registration scheme using a registration simulator. In at least one embodiment, a trained neural network can register a moving/atlas image M with an arbitrary image I° from a registration simulator as well as an arbitrary image selected from a dataset of images. In at least one embodiment, in a segmentation scenario, a registration simulator can act as a data augmentation tool, which can be useful with medical image segmentation where medical image datasets are small and dense annotation of segmentation is expensive.” [0066]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Ernst et al. the ability to segment images using neural networks and register an image to an atlas as taught by Zhu et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Zhu et al. who teaches the advantages of using neural network for registering and segmenting images and Ernst benefits as they also deal with segmented images.
Diagnostic
The combined references teach metrics. They do not specifically teach quality metrics as diagnostic.
Rao et al. also in the business of metrics teaches:
Determines if quality metric has diagnostic quality (whether the image is diagnostic or non-diagnostic)….
“At 308, method 300 determines if the quality metric(s) indicate the image has insufficient diagnostic quality. For example, each quality metric may be compared to a respective quality threshold, and if any of the quality metrics is below the corresponding quality threshold, insufficient diagnostic quality may be indicated. For example, the first quality metric may be the output of the anatomy model, which may be a value in the range of 1-100. If the anatomy model output is less than a threshold value (e.g., 70), the image may be determined to be of insufficient diagnostic quality. In some examples, each quality metric may be compared to the same quality threshold. In other examples, two or more of the quality metrics may be compared to different quality thresholds. For example, the first quality metric (e.g., the output of the anatomy model) may be compared to a first quality threshold and a second quality metric (e.g., the output of the exposure model) may be compared to a second quality threshold that is different (e.g., lower) than the first quality threshold. In doing so, different quality metrics/model outputs may be given different importance/weight in determining whether an image is of insufficient diagnostic quality.” [0042]
Quality metric as a value in range 1-100 (scalar) and compared to threshold (stored optimum value or range)…
“At 308, method 300 determines if the quality metric(s) indicate the image has insufficient diagnostic quality. For example, each quality metric may be compared to a respective quality threshold, and if any of the quality metrics is below the corresponding quality threshold, insufficient diagnostic quality may be indicated. For example, the first quality metric may be the output of the anatomy model, which may be a value in the range of 1-100. If the anatomy model output is less than a threshold value (e.g., 70), the image may be determined to be of insufficient diagnostic quality. In some examples, each quality metric may be compared to the same quality threshold. In other examples, two or more of the quality metrics may be compared to different quality thresholds. For example, the first quality metric (e.g., the output of the anatomy model) may be compared to a first quality threshold and a second quality metric (e.g., the output of the exposure model) may be compared to a second quality threshold that is different (e.g., lower) than the first quality threshold. In doing so, different quality metrics/model outputs may be given different importance/weight in determining whether an image is of insufficient diagnostic quality.” [0042]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use metrics for diagnostic purposes as taught by Rao et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Rao et al. who teaches the advantages of using quality metrics for diagnostic purposes.
Optimum
The combined references teach imaging. They do not teach optimum.
Maes et al. also in the business of imaging teaches:
See Maes, Reference U-1:
Range with translation and rotation (movement) and optimum, and where translation could be positive and rotation negative (MI – Mutual Information)…
“ Fig. 3 shows traces of MI of the images of Fig. 1 for translation and rotation around the registered position. These illustrate the robustness and reliability of the MI criterion: within a large range of -25 to +25 mm translation and to -25 to +25 rotation around the registered position there is only a single strong optimum, which coincides with the correct registration solution.” (pg. 1704, col. 1, para. 3) Inherent with the range is the ability to have a positive range offset by a negative range.
See Maes, Reference U-2:
Optimize in-plan translation and rotation parameters, then out-of-plane parameters…
“ For brain images, for instance, the horizontal translation and the rotation around the vertical axis are more constrained by the shape of the head than the pitching rotation around the left to right horizontal axis. Therefore, it is suggested in [59] to first align the images in the transversal plane by optimizing the in-plane translation and rotation parameters first, in order to facilitate the optimization of the out-of-plane parameters. As the optimization proceeds, the Powell algorithm may introduce other optimization directions and change the order in which these are considered [88].” (pg. 1706, col. 2, para. 3)
See Maes, Reference U-3:
“Nonrigid registration or matching of 3-D images involves finding a 3-D field of 3-D deformation vectors that maps each point in one image onto the corresponding point in the other image. Nonrigid image to image registration warps one image toward a second one such that all objects in the warped image precisely coincide with the corresponding objects in the target image. In contrast with affine registration, which only allows for translation, rotation, scaling or skew of one image relative to another in order to establish global alignment of both images all over the image domain as illustrated in Section IV, nonrigid registration allows displacement of individual voxels such that local, regional distortions between both images can be corrected for up to voxel scale.” (pg. 1715, col. 2, para. 2)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to look for optimal position as taught by Maes et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Maes et al. who the benefits of capturing images that in the in plane (focal plane).
Regarding claim 4
The system of claim 1, wherein the processor is configured to determine, for one or more of the images based on the combined quality metric, a parameter or a state variable representing a change of imaging conditions for using the imager; and
Ernst et al. teaches:
Position of subject to generate geometric updates of scanner (state parameter)…
“In block 512, the system accesses the motion or position data of the subject corresponding to the scanning process. For example, the position of the subject used to generate geometric updates for use by the scanner may be stored in a matrix with position data corresponding to the data acquired by the scanner at the same time. The position and/or motion data may indicate the position of the subject in up to six degrees of freedom. The corresponding image data may be stored correlated with the motion or position or tracking data that was used to adjust the scanner during data acquisition.” [0058]
wherein the changed imaging conditions are configured to change one or more of the image quality metrics of the plurality.
Example of inverting positions or motions (changed imaging conditions) and de-corrects imaging data (change image metric)…
“In block 514, the system de-corrects the raw imaging data to remove the effect of the prospective motion control. In some embodiments, the system may de-correct the data, by inverting the positions and/or motions of the subject during the scanning process. For example, if the position of the subject during one step of scanning indicates that the subject was rotated 5 degrees to the left, the system may invert the position to rotate the position of the data 5 degrees to the right. Similar corrections can be made for rotations, translations, or other motions of the subject. For example, if the patient moved 5 cm to the left and rotated 5 degrees to the right, the system may de-correct the corresponding imaging data by updating the position 5 cm to the right and rotating it 5 degrees to the left.” [0059]
Regarding claims 15, 16, and 17
(claim 15) The system of claim 1, wherein each of the image quality metrics of the plurality is expressed as a scalar value that is directly comparable with a respective stored optimum value or range.
The combined references teach imaging. They do not teach optimum.
Maes also in the business of optimum teaches:
See Maes, Reference U-1:
Maes also in the business of imaging teaches:
-25 to +25 (scalar values)…
“ Fig. 3 shows traces of MI of the images of Fig. 1 for translation and rotation around the registered position. These illustrate the robustness and reliability of the MI criterion: within a large range of -25 to +25 mm translation and to -25 to +25 rotation around the registered position there is only a single strong optimum, which coincides with the correct registration solution.” (pg. 1704, col. 1, para. 3)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to look for optimal position as taught by Maes et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Maes et al. who the benefits of capturing images that in the in plane (focal plane).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following prior art teaches imaging and focal plane:
US-20080285711-A1
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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