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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Paragraph [0045] recites, “FIG. 1 is a system diagram illustrating an operating environment 100”, however, the reference number ‘100’ is not shown in Fig. 1.
Paragraph [0045] recites, “FIG. 1 is a system diagram illustrating an operating environment 100”, however, the reference number ‘100’ is not shown in Fig. 1.
Paragraph [0045] recites “the system comprising a trained neural network (shown as 102a)”, however, there reference number ‘102a’ is not shown in Fig. 1. Instead, Fig. 1 displays a ‘training neural network’ labeled as ‘102b’.
Paragraph [0045] recites “displacement encoded data 106”, however, there reference number ‘106’ is not shown in Fig. 1. Instead, Fig. 1 displays ‘displacement data’ labeled as ‘106’.
Paragraph [0045] recites “biomedical images 104”, however, there reference number ‘104’ is not shown in Fig. 1.
Paragraph [0045] recites “strain analysis 107”, however, there reference number ‘107’ is not shown in Fig. 1.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 12 is objected to because of the following informalities:
Claim 12 recites “The system of Claim 12”, thus leading to dependency issues. The Examiner suggests amending Claim 12 to recite “The system of Claim 11” to resolve dependency issues.
Claim 2 recites five steps, wherein each step is labeled vi, vii, viii, ix, and x, respectively. However, Claim 13 recites the same steps which are labeled, i, ii, iii, iv, and v. The Examiner suggests amending Claim 2 to recite ‘steps i, ii, iii, iv, and v’ to promote clarity.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 10 11 and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1, 7, 15, and 19 of U.S. Patent No. 12,694,518 (corresponding to US Pub No 2024/0046464) in view of Hsiao et al. (US Pub No 2023/0147286), hereinafter Hsiao.
As to Claim 1, U.S. Pat No 12,694,518 teaches a [[computer implemented]] method of measuring intramyocardial tissue displacement with image data, the method comprising (see Claim 1, lines 1-2, “A method of determining intramyocardial motion and/or measurand in medical image scans”, wherein motion is similar to ‘displacement’)
retrieving a medical image of a subject [[with a magnetic resonance imaging (MRI) system]], having at least one processor, (see Claim 1, line 3, “retrieving, by a processor, a medical image scan of a subject” ),
[[wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence]];
and determining, by the processor, intramyocardial tissue displacement within the medical image by using a neural network (see Claim 1, lines 4-5 “determining, by the processor, intramyocardial motion data in the medical image scan, in part, using a trained neural network”),
wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data (see Claim 1, lines 5-9“wherein the trained neural network has been trained by:(i) generating a set of contour motion images or data from Displacement-ENcoding with Stimulated Echoes (DENSE) magnitude images…a set of training displacement map images or data from DENSE phase images or data”).
The instant application is directed towards a method of measuring tissue displacement, while the U.S. Patent is directed towards intramyocardial motion. However, the Examiner has found no patentable distinction between ‘motion’ and displacement. The U.S. Patent states the neural network is capable of outputting a ‘displacement map image’ (see Claim 1, line 12 “applying the set of contour motion images or data to the input to generate an output displacement map image”). Thus, it is clear that the invention claimed in the U.S. Patent is capable of determining intramyocardial tissue displacement. The instant application is also directed towards a ‘computer implemented method’, while U.S. Patent does not explicitly state so. However, it is clear that a computer is needed to implement the neural network claimed in the U.S. Patent. Thus, it is clear that the instant Claim 1 is merely broadening the scope of the previously patented Claim 1 by not reciting the specific training steps recited in Claim 1 of the U.S. Patent.
The U.S. Patent does not teach that the medical image is retrieved with a magnetic resonance imaging (MRI) system, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence . However, in an analogous art Hsiao teaches a method of determining intramyocardial tissue displacement with image data (see paragraph [0016], “The method may further include post-processing to compute a spatial gradient of the myocardial velocity field to determine a myocardial strain rate”) the method comprising,
retrieving a medical image of a subject with a magnetic resonance imaging (MRI) system (see paragraph [0066], “Referring to FIG. 6 , we retrospectively collected a convenience sample of 139 cardiac MRI exams”),
wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence (see paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the MRI image retrieval taught by Hsiao with the method taught by the U.S. Patent. The motivation for using a bSSFP pulse sequence would be to comply with current standards for cardiac measurement. Hsiao teaches in paragraph [0006], “An important area in need of improvement in medical image analysis is cardiac MRI, a versatile imaging technique for assessment of anatomy and function. Cine balanced steady-state free precession (SSFP) forms the backbone of cardiac MRI and is the standard for quantification of cardiac function and morphology”). Thus, it would have been obvious to combine the imaging retrieval system taught by Hsiao with the teachings of the U.S. Patent in order to obtain the invention as claimed in Claim 1.
As to Claim 10, the U.S. Patent teaches using a 3D U-Net neural network as the convolutional neural network (see Claim 7, “wherein the neural network comprises a 3D UNet neural network).
As to Claim 11, the U.S Patent teaches a system comprising: a processor (see Claim 15, lines 1-2, “A system comprising: a processor”);
and a memory having instructions stored thereon to calculate intramyocardial displacement in medical image scans, wherein execution of the instructions by the processor causes the processor to (see Claim 15, lines 3-4, “and a memory having instructions stored thereon to determine intramyocardial motion and/or measurand in medical image scans, wherein execution of the instructions by the processor causes the processor to”):
retrieve a medical image of a subject (see Claim 15, line 6, “retrieve medical image scan of a subject”),
[[with a magnetic resonance imaging (MRI) system in communication with the processor, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence]];
and determining, by the processor, intramyocardial tissue displacement within the medical image by using a neural network (see Claim 15, lines 7-8, “determine intramyocardial motion data in the medical image scan, in part, using a trained neural network”),
wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data (see Claim 15, lines 8-11, “wherein the trained neural network has been trained by: (i) generating a set of contour motion images or data from Displacement-ENcoding with Stimulated Echoes (DENSE) magnitude images or data”).
As stated earlier, instant application is directed towards a method of measuring tissue displacement, while the U.S. Patent is directed towards intramyocardial motion. However, the Examiner has found no patentable distinction between ‘motion’ and displacement. The U.S. Patent states the neural network is capable of outputting a ‘displacement map image’ (see Claim 15, line 12 “applying the set of contour motion images or data to the input to generate an output displacement map image”). Thus, it is clear that the invention claimed in the U.S. Patent is capable of determining intramyocardial tissue displacement.
As stated earlier, the U.S. Patent does not teach that the medical image is retrieved with a magnetic resonance imaging (MRI) system, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence . However, in an analogous art Hsiao teaches retrieving a medical image of a subject with a magnetic resonance imaging (MRI) system (see paragraph [0066], “Referring to FIG. 6 , we retrospectively collected a convenience sample of 139 cardiac MRI exams”),
wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence (see paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the MRI image retrieval taught by Hsiao with the method taught by the U.S. Patent. The motivation for using a bSSFP pulse sequence would be to comply with current standards for cardiac measurement (see Hsiao, paragraph [0006]). Thus, it would have been obvious to combine the imaging retrieval system taught by Hsiao with the teachings of the U.S. Patent in order to obtain the invention as claimed in Claim 11.
As to Claim 14, the U.S. Patent teaches a non-transitory computer readable medium having instructions stored thereon to calculate intramyocardial displacement in medical image scans (see Claim 19, lines 1-2“A non-transitory computer-readable medium having instructions stored thereon to determine intramyocardial motion and/or measurand in medical image scans”),
wherein execution of the instructions by a computer with a processor causes the [[computer]] to (see Claim 19, line 3, “wherein execution of the instructions by the processor causes the processor to”):
retrieve a medical image of a subject (see Claim 19, line 4, “retrieve medical image scan of a subject”)
[[with a magnetic resonance imaging (MRI) system in communication with the processor, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence]];
and determining, by the processor, intramyocardial tissue displacement within the medical image by using a neural network (see Claim 19, lines 5-6, “determine intramyocardial motion data in the medical image scan, in part, using a trained neural network”),
wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data (see Claim 19, lines 7-10, “wherein the trained neural network has been trained by: (i) generating a set of contour motion images or data from Displacement-ENcoding with Stimulated Echoes (DENSE) magnitude images or data”).
As stated earlier, instant application is directed towards a method of measuring tissue displacement, while the U.S. Patent is directed towards intramyocardial motion. However, the Examiner has found no patentable distinction between ‘motion’ and displacement. The U.S. Patent states the neural network is capable of outputting a ‘displacement map image’ (see Claim 19, line 12 “applying the set of contour motion images or data to the input to generate an output displacement map image”). Thus, it is clear that the invention claimed in the U.S. Patent is capable of determining intramyocardial tissue displacement.
As stated earlier, the U.S. Patent does not teach that the medical image is retrieved with a magnetic resonance imaging (MRI) system, wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence . However, in an analogous art Hsiao teaches retrieving a medical image of a subject with a magnetic resonance imaging (MRI) system (see paragraph [0066], “Referring to FIG. 6 , we retrospectively collected a convenience sample of 139 cardiac MRI exams”),
wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence (see paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the MRI image retrieval taught by Hsiao with the method taught by the U.S. Patent. The motivation for using a bSSFP pulse sequence would be to comply with current standards for cardiac measurement (see Hsiao, paragraph [0006]). Thus, it would have been obvious to combine the imaging retrieval system taught by Hsiao with the teachings of the U.S. Patent in order to obtain the invention as claimed in Claim 14.
Thus, for all the reasons cited above, Claims 1, 10, 11 and 14 of the instant application is not patentably distinct from Claims 1, 7, 15, and 19 of the US. Pat No 12,694,518 in view of Hsiao.
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, 3-6, 9-12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao et al. (US Pub No 20230147286), hereinafter Hsiao, in view of Ghadimi et al. (US Pub No 20210267455), hereinafter Ghadimi.
As to Claim 1, Hsiao teaches a computer implemented method of measuring intramyocardial tissue displacement with image data (see paragraph [0016], “The method may further include post-processing to compute a spatial gradient of the myocardial velocity field to determine a myocardial strain rate”), where the Examiner notes that myocardial strain rate is a measure of displacement of the intramyocardial tissue (see paragraph [0097], “Myocardial strain is defined as the changes in length of the cardiac wall in the axial, radial, and circumferential directions after a fixed interval” ), the method comprising:
retrieving a medical image of a subject with a magnetic resonance imaging (MRI) system (see paragraph [0066], “Referring to FIG. 6 , we retrospectively collected a convenience sample of 139 cardiac MRI exams”),
having at least one processor (see paragraph [0014], “In another aspect of the invention, a method for analysis of time series images includes: receiving the time series images in a computer processor”),
wherein the MRI system retrieves the medical image with a balanced steady-state free precession (bSSFP) pulse sequence (see paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series”); and
determining, by the processor (see paragraph [0014]), intramyocardial tissue displacement within the medical image by using a neural network (see paragraph [0100], “FIG. 12 shows the results of training the Triton-Net hybrid CNN to simultaneously segment cardiac chambers, locate anatomical landmarks, and synthesize myocardial velocity fields. From these outputs, the myocardial strain rate can be computed ”, where CNN stands for convolutional neural network).
Hsiao fails to explicitly teach that the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data. However, in an analogous art, Ghadimi et al. teaches a method for measuring cardiac displacement (see paragraph [0006], “In one aspect, the present disclosure relates to a method of strain analysis of a cardiac region of interest of a subject from displacement encoded magnetic resonance image (MRI) data”
by using a neural network (see paragraph [0006], “The method includes training a convolutional neural network (CNN)”)
wherein the neural network has been trained with training data calculated from Displacement-ENcoding with Stimulated Echoes (DENSE) image data (see paragraph [0101], “Short-axis cine DENSE MRI data from 38 heart-disease patients and 70 healthy volunteers were used for network training and testing of a non-limiting example of the present disclosure”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training data taught by Ghadimi with the method taught by Hsiao. The motivation for doing so would be to use the DENSE training data to improve clinical performance. Ghadimi teaches in paragraph [0003], “Among various strain imaging methods, cine displacement encoding with stimulated echoes (DENSE)2-4 magnetic resonance imaging (MRI) uniquely measures heart motion by encoding myocardial displacement into the signal phase, which can facilitate high measurement accuracy5, high reproducibility of global and segmental strain6,7, and rapid computation of displacement and strain5,8. These properties translate to benefits in clinical performance”. Thus, it would have been obvious to combine the training date taught by Ghadimi with the teachings of Hsiao in order to obtain the invention as claimed in Claim 1.
As to Claim 3, Hsiao in view of Ghadimi teaches retrieving a medical image of a subject comprises retrieving a two dimensional (2D) bSSFP medical image of the subject (see Hsiao, paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series”, and see 2D image data depicted in Fig. 2A and Fig. 2B).
As to Claim 4, Hsiao in view of Ghadimi teaches retrieving the medical image comprises retrieving cine image data with the MRI system using the bSSFP pulse (see Hsiao, paragraph [0066], “Referring to FIG. 6 , we retrospectively collected a convenience sample of 139 cardiac MRI exams”, and see paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series”).
As to Claim 5, Hsiao in fails to teach using the processor to generate frames of test image magnitude data from the cine image data and segment the test image magnitude data to track pixels of the medical image corresponding to endocardial contours and epicardial contours of a myocardium of the subject.
Ghadimi teaches generating frames of test image magnitude data generate frames of test image magnitude data from the cine image data (see paragraph [0021], “The method further includes training the convolutional neural network with augmented test data”, and see paragraph [0101], “Short-axis cine DENSE MRI data from 38 heart-disease patients and 70 healthy volunteers were used for network training and testing”)
and segment the test image magnitude data to track pixels of the medical image corresponding to endocardial contours and epicardial contours of a myocardium of the subject (see paragraph [0089], “To automatically segment the LV from DENSE magnitude images, one U-Net was trained to extract the epicardial border, and another to extract the endocardial border, and the myocardial pixels can be identified”).
Thus, it would have been obvious to combine the segmentation taught by Ghadimi with the teachings of Hisao. The motivation for doing so would be to automate segmentation. Ghadimi teaches in paragraph [0004], “Currently, LV segmentation of DENSE is typically performed using motion-guided segmentation8, which can require manual segmentation of the LV epicardial and endocardial borders at a single cardiac phase, followed by automated propagation of these borders to all other phases (guided by the measured myocardial displacements). User intervention is sometimes needed to adjust the segmentation results”. Thus, it would have been obvious to combine the segmentation taught by Ghadimi with the teachings of Hsiao in order to obtain the invention as claimed in Claim 5.
As to Claim 6, Hsiao teaches segmenting bSSFP image data to obtain binary masks to obtain contour motion data (see paragraph [0012], “In an exemplary implementation of the neural network architecture, the time series images comprise a cine balanced steady-state free precession (SSFP) cardiac series, the multiple sub-convolutional neural network output prongs comprise three prongs, and wherein image segmentation comprises delineating edges of cardiac chambers,”, and see Fig. 8 binary image 408). However, Hsiao fails to teach applying morphological dilation to a bSSFP binary mask to binarize segmented test image magnitude data.
However, Ghadimi teaches a method of segmentation which comprises applying morphological dilations to binarize segment test image data (see paragraph [0089], “To automatically segment the LV from DENSE magnitude images, one U-Net was trained to extract the epicardial border, and another to extract the endocardial border, and the myocardial pixels can be identified by performing a logical XOR between the two masks. Specifically, in the contracting path, each encoding block can contain two consecutive sets of dilated convolutional layers with filter size 3×3 and dilation rate 2, a batch normalization layer and a rectified linear activation layer.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the dilation taught by Ghadimi to the binary masks taught by Hsiao. The motivation for doing so would be to improve model performance. Ghadimi teaches in paragraph [0089], “Compared with traditional convolutions, dilated convolutions can increase the receptive field size without increasing the number of parameters and showed improved performance in our experiments.” Thus, it would have been obvious to combine the dilation taught by Ghadimi with the teachings of Hsiao in order to obtain the invention as claimed in Claim 6.
As to Claim 9, Hsiao in view of Ghadimi teaches using a convolutional neural network as the neural network (see Hsiao, paragraph [0014], “receiving the time series images in a computer processor configured for executing a trained 3D-UNet convolutional neural network (CNN)”).
As to Claim 10, Hsiao in view of Ghadimi teaches using a 3D U-Net neural network as the convolutional neural network (see paragraph [0014], “receiving the time series images in a computer processor configured for executing a trained 3D-UNet convolutional neural network (CNN)”).
As to Claim 11, Hsiao in view of Ghadimi teaches a system (see Ghadimi, Fig. 14)
comprising: a processor (see Ghadimi, Fig. 14, processing unit 302);
and a memory (see Ghadimi, Fig. 14, memory 304); having instructions stored thereon to calculate intramyocardial displacement in medical image scans (see Ghadimi, paragraph [0027], “In one aspect, the present disclosure relates to a non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause one or more computing devices to perform functions for strain analysis of a cardiac region of interest of a subject”), wherein execution of the instructions by the processor causes the processor to perform the same steps recited in Claim 1. Therefore, the rejection and rationale are analogous to that of Claim 1.
As to Claim 12, Hsiao in view of Ghadimi teaches that the instructions in the memory implement a neural network to calculate the intramyocardial displacement (see Hsiao, paragraph [0014]), intramyocardial tissue displacement within the medical image by using a neural network (see paragraph [0100], “FIG. 12 shows the results of training the Triton-Net hybrid CNN to simultaneously segment cardiac chambers, locate anatomical landmarks, and synthesize myocardial velocity fields. From these outputs, the myocardial strain rate can be computed ”, where CNN stands for convolutional neural network).
As to Claim 14¸ Hsiao in view of Wang teaches a non-transitory computer readable medium having instructions stored thereon to calculate intramyocardial displacement in medical image scans see Ghadimi, paragraph [0027], “In one aspect, the present disclosure relates to a non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause one or more computing devices to perform functions for strain analysis of a cardiac region of interest of a subject”),
wherein execution of the instructions by a computer with a processor (see Ghadimi, Fig. 14, computer 300, processing unit 302, memory 304) causes the computer to perform the same steps recited in Claim 1. Therefore, the rejection and rationale are analogous to that of Claim 1.
Claims 2, 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hsiao et al. (US Pub No 2023/0147286), hereinafter Hsiao, in view of Ghadimi et al. (US Pub No 20210267455), and hereinafter Ghadimi, and further in view of Wang et al. (Wang, Yu, et al., “Cardiac MRI Feature Tracking by Deep Learning from DENSE Data”, ISMRM 2021, presented May 15-20, 2021), hereinafter Wang.
As to Claim 2, Hsiao teaches calculating error data and updating parameters of the neural network with the error data (see paragraph [0081], “Loss Function Weighing: For training, we scaled each component loss function such that their values were of similar scale. We weighed segmentation loss, localization loss, flow synthesis loss, and peak speed loss by factors of 10, 10, 0.1, and 0.1, respectively”).
Hsiao fails to teach training the neural network comprises using a training computer to perform computerized steps comprising:(vi) generating a set of contour motion image data from magnitude data from the DENSE image data; (vii) using the neural network to calculate estimated displacement image data from the contour motion image data; (viii) generating a set of ground truth displacement image data with phase data from the DENSE image data; (ix) calculating error data by comparing the estimated displacement image data with the ground truth displacement image data; and (x) updating parameters of the neural network with the error data.
However, in an analogous art, Wang teaches:
vi) generating a set of contour motion image data from magnitude data from the DENSE image data (see Methods section, “To train DENSE-trained FlowNet2 (DT-FlowNet2), the input was the endocardial and epicardial contours in two image frames”);
(vii) using the neural network to calculate estimated displacement image data from the contour motion image data (see Introduction, paragraph 1, “ As DENSE images provide both myocardial contours and intramyocardial displacement data, we investigated the use of DENSE data to train deep networks to predict intramyocardial motion from contour motion”);
(viii) generating a set of ground truth displacement image data with phase data from the DENSE image data (see Introduction, paragraph 1, “DENSE directly measures intramyocardial tissue displacement”, and see Results, paragraph 1, “DT-FlowNet2 showed better agreement with the ground truth”, and see ground truth data generated DENSE phase data shown in Figure 2);
(ix) calculating error data by comparing the estimated displacement image data with the ground truth displacement image data (see Results, paragraph 1, “DT-FlowNet2 showed better agreement with the ground truth than unmodified FlowNet2…End-point-error (EPE), which represents the difference of two displacement vectors, was used for error calculation and comparisons”).
Thus, it would have been obvious to combine the motion contour data taught by Wang with eh teachings of Hsiao and Ghadimi. The motivation for doing so would be to improve the accuracy of the measured intramyocardial displacement (see Introduction, paragraph 1, “This deep learning (DL) approach may provide more accurate intramyocardial displacements (the precursor to strain) than optical-flow-based methods when applied to myocardial contour data”). Thus, it would have been obvious to combine the motion data taught by Wang with the teachings of Hsia and Ghadimi in order to obtain the invention as claimed in Claim 2.
As to Claim 8, Hsiao in view of Ghadimi fails to explicitly teach using the test contour motion data as an input to the neural network to calculate intramyocardial tissue displacement from the cine image data.
However, Wang teaches using contour motion data as an input to the neural network to calculate intramyocardial tissue displacement from the cine image data see Introduction, paragraph 1, “ As DENSE images provide both myocardial contours and intramyocardial displacement data, we investigated the use of DENSE data to train deep networks to predict intramyocardial motion from contour motion”, and see Methods, paragraph 3, “Our training data is from 108 subjects, with each subject including three short-axis slices of cine DENSE images”).
Thus, it would have been obvious to combine the motion contour data taught by Wang with eh teachings of Hsiao and Wang. The motivation for doing so would be to improve the accuracy of the measured intramyocardial displacement (see Introduction, paragraph 1).Thus, it would have been obvious to combine the motion data taught by Wang with the teachings of Hsia and Ghadimi in order to obtain the invention as claimed in Claim 8.
As to Claim 13, Claim 13 claims the same limitation claimed as Claim 2 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are similar to that of Claim 2.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hsiao et al. (US Pub No 2023/0147286), hereinafter Hsiao, in view of Ghadimi et al. (US Pub No 20210267455), and hereinafter Ghadimi, and further in view of Chen et al. (US Pub No 20150285889), hereinafter Chen
As to Claim 7, both Hsiao and Ghadimi fail to teach under sampling the segmented test image magnitude data to generate the test contour motion data with spatial resolution matching between the DENSE image data and the test contour motion data.
However, Chen teaches obtaining DENSE image data (see paragraph [0058], “In some embodiments, image reconstruction uses a combination of BLOSM and SENSE in which certain techniques used for reconstructed accelerated two-dimensional (2D) cine DENSE imaging”)
and under sampling the received data to generate image data with spatial resolution matching (see paragraph [00068], “The fully-sampled datasets provided reference images, and retrospective under sampling of these datasets was used to evaluate the new methodologies. Prospectively acquired under sampled cine DENSE datasets demonstrated true acceleration. Imaging parameters included field of view (FOV) 280-320×280-320 mm2, spatial resolution 1.8-2.2×1.8-2.2×8 mm3”)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the under sampling taught by Chen with the teachings of Hsiao and Chen . The motivation for doing so would be to obtain the true acceleration of motion image data see paragraph [0068], “Prospectively acquired under sampled cine DENSE datasets demonstrated true acceleration”). Thus, it would have been obvious to combine the under sampling taught by Chen with the teachings of Hsiao and Ghadimi in order to obtain the invention as claimed in Claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Østvik et al. (US Pub No 20200074625) hereinafter Østvik teaches a method for determining intramyocardial displacement by using multiple convolutional neural networks.
Loecher et al. (US Pub No 20210219862) teaches a method for using a convolutional neural network for determining the motion of intramyocardial tissue
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/S.T./ Examiner, Art Unit 2664
/CHARLOTTE M BAKER/ Primary Examiner, Art Unit 2664
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