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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/14/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities: Line 5 recites “the medical image” which Examiner suggests amending to “the first medical image”. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: Line 3 recites “trained using training dataset” which Examiner suggests amending to “trained using a training dataset”. Line 4 recites “image acquired with shortened acquisition time” which Examiner suggests amending to “image acquired with a shortened acquisition time”. Line 5 recites “a standard acquisition time acquisition plane” which Examiner suggests amending to “a standard acquisition time per acquisition plane”. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: Line 3 recites “trained using training dataset” which Examiner suggests amending to “trained using a training dataset”. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: Line 2 recites “comprises co-registered 3D volume” which Examiner suggests amending to “comprises a co-registered 3D volume”. Appropriate correction is required.
Claim 15 is objected to because of the following informalities: Line 7 recites “the medical image” which Examiner suggests amending to “the first medical image”. Appropriate correction is required.
Claim 18 is objected to because of the following informalities: Line 3 recites “trained using training dataset” which Examiner suggests amending to “trained using a training dataset”. Line 4 recites “image acquired with shortened acquisition time” which Examiner suggests amending to “image acquired with a shortened acquisition time”. Lines 5-6 recite “a standard acquisition time acquisition plane” which Examiner suggests amending to “a standard acquisition time per acquisition plane”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 8-10, and 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.
Claim 3 recites the limitation "the number of acquisition planes" in Line 3. There is insufficient antecedent basis for this limitation in the claim as there is no earlier mention of a number of acquisition planes. Examiner suggests amending to “a number of acquisition planes” and has interpreted the limitation as such.
Claim 8 recites the limitation "the number of acquisition planes" in Line 2. There is insufficient antecedent basis for this limitation in the claim as there is no earlier mention of a number of acquisition planes. Examiner suggests amending to “a number of acquisition planes” and has interpreted the limitation as such.
Claims 9 and 10 are dependent on claim 8 and thus are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite.
Claim 17 recites the limitation "the number of acquisition planes" in Lines 3-4. There is insufficient antecedent basis for this limitation in the claim as there is no earlier mention of a number of acquisition planes. Examiner suggests amending to “a number of acquisition planes” and has interpreted the limitation as such.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-7 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pan et al. (Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept).
With regards to claim 1, Pan et al. discloses a computer-implemented method for improving image quality comprising:
(a) receiving a first medical image of a subject, wherein the first medical image is acquired with an acceleration scheme using single-photon emission computed tomography (SPECT) (Subjects and image acquisition: Para. 1 lines 4-7, "fast scan" "1/7 SPECT");
(b) combining the medical image with a second medical image acquired using computed tomography (CT) to generate an input image (Subjects and image acquisition: Para. 1 lines 10-12, Image preprocess: Para. 1 lines 1-10, "combine 1/7 SPECT image and CT image"); and
(c) applying a deep learning network model to the input image and outputting an enhanced medical image, wherein the deep learning network model is selected based at least in part on the acceleration scheme (Background: Para. 4 lines 1-3, Image preprocess: Para. 1 lines 4-10, Residual U-block and U2-Net: Para. 1 lines 7-12, Conclusion: Para. 1 lines 1-5, Abstract: Lines 1-2, "U2-Net").
With regards to claim 2, Pan et al. discloses the computer-implemented method of claim 1, wherein the enhanced medical image has an image quality same as a SPECT image acquired with an acquisition time longer than the acquisition time of the acceleration scheme or has an image quality improved over the first medical image (Clinic evaluation: Para. 1 lines 6-7, "same general image quality" "standard SPECT").
With regards to claim 3, Pan et al. discloses the computer-implemented method of claim 1, wherein the acceleration scheme comprises at least a first parameter indicating a shortened acquisition time per acquisition plane or a second parameter indicating a reduction of the number of acquisition planes (Subjects and image acquisition: Para. 1 lines 4-7, "fast scan" "1/7 SPECT").
With regards to claim 4, Pan et al. discloses the computer-implemented method of claim 1, wherein the acceleration scheme comprises a first parameter indicating a shortened acquisition time per acquisition plane, and wherein the deep learning network model is trained using training dataset comprising a SPECT image acquired with shortened acquisition time per acquisition plane, a corresponding CT image and a SPECT image acquired using a standard acquisition time acquisition plane (Materials and methods: Subjects and image acquisition: Para. 1 lines 4-7 and 10-12 and 19-21, Fig. 1, "1/7 SPECT" "standard SPECT" "CT images" "training").
With regards to claim 5, Pan et al. discloses the computer-implemented method of claim 4, wherein the input image to the deep learning network model comprises a plurality of image slices and wherein the deep learning network model comprises a 2D convolutional layer (Image preprocess: Par. 1 lines 4-10, Residual U-block and U2-Net: Para. 1 lines 12-15, Fig. 2, "slices" "U2-Net" "convolutional layer").
With regards to claim 6, Pan et al. discloses the computer-implemented method of claim 4, wherein the deep learning network model is trained using a loss function to enhance an accuracy in a region of interest (Background: Para. 4 lines 3-4, Lesion attention loss and deep supervision: Para. 1 lines 1-5, "loss function" "distinguishability of the structure").
With regards to claim 7, Pan et al. discloses the computer-implemented method of claim 4, wherein the deep learning network model is trained using an attention mask (Lesion attention loss and deep supervision: Para. 2 lines 1-6, "lesion attention masks").
With regards to claim 13, Pan et al. discloses the computer-implemented method of claim 1, wherein the first medical image and the second medical image are acquired simultaneously (Image preprocess: Para. 1 lines 1-2, "simultaneous SPECT and CT acquisition modes").
With regards to claim 14, Pan et al. discloses the computer-implemented method of claim 13, wherein the second medical image is acquired without acceleration (Subjects and image acquisition: Para. 1 lines 10-12, Conclusion: Para. 1 lines 2-3, "CT image").
With regards to claim 15, Pan et al. discloses a non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations (Implementation details: Para. 1 lines 1-5, "NVIDIA GEFORCE 3090 (24GB)") comprising:
(a) receiving a first medical image of a subject, wherein the first medical image is acquired with an acceleration scheme using single-photon emission computed tomography (SPECT) (Subjects and image acquisition: Para. 1 lines 4-7, "fast scan" "1/7 SPECT");
(b) combining the medical image with a second medical image acquired using computed tomography (CT) to generate an input image (Subjects and image acquisition: Para. 1 lines 10-12, Image preprocess: Para. 1 lines 1-10, "combine 1/7 SPECT image and CT image"); and
(c) applying a deep learning network model to the input image and outputting an enhanced medical image, wherein the deep learning network model is selected based at least in part on the acceleration scheme (Background: Para. 4 lines 1-3, Image preprocess: Para. 1 lines 4-10, Residual U-block and U2-Net: Para. 1 lines 7-12, Conclusion: Para. 1 lines 1-5, Abstract: Lines 1-2, "U2-Net").
With regards to claim 16, Pan et al. discloses the non-transitory computer-readable storage medium of claim 15, wherein the enhanced medical image has an image quality same as a SPECT image acquired with an acquisition time longer than the acquisition time of the acceleration scheme or has an image quality improved over the first medical image (Clinic evaluation: Para. 1 lines 6-7, "same general image quality" "standard SPECT").
With regards to claim 17, Pan et al. discloses the non-transitory computer-readable storage medium of claim 16, wherein the acceleration scheme comprises at least a first parameter indicating a shortened acquisition time per acquisition plane or a second parameter indicating a reduction of the number of acquisition planes (Subjects and image acquisition: Para. 1 lines 4-7, "fast scan" "1/7 SPECT").
With regards to claim 18, Pan et al. discloses the non-transitory computer-readable storage medium of claim 15, wherein the acceleration scheme comprises a first parameter indicating a shortened acquisition time per acquisition plane, and wherein the deep learning network model is trained using training dataset comprising a SPECT image acquired with shortened acquisition time per acquisition plane, a corresponding CT image and a SPECT image acquired using a standard acquisition time acquisition plane (Materials and methods: Subjects and image acquisition: Para. 1 lines 4-7 and 10-12 and 19-21, Fig. 1, "1/7 SPECT" "standard SPECT" "CT images" "training").
With regards to claim 19, Pan et al. discloses the non-transitory computer-readable storage medium of claim 18, wherein the input image to the deep learning network model comprises a plurality of image slices and wherein the deep learning network model comprises a 2D convolutional layer (Image preprocess: Par. 1 lines 4-10, Residual U-block and U2-Net: Para. 1 lines 12-15, Fig. 2, "slices" "U2-Net" "convolutional layer").
With regards to claim 20, Pan et al. discloses the non-transitory computer-readable storage medium of claim 18, wherein the deep learning network model is trained using a loss function to enhance an accuracy in a region of interest (Background: Para. 4 lines 3-4, Lesion attention loss and deep supervision: Para. 1 lines 1-5, "loss function" "distinguishability of the structure").
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.
Claim(s) 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. (Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept) in view of Sahbaee Bagherzadeh et al. (US 2021/0015438).
With regards to claim 8, Pan et al. discloses the computer-implemented method of claim 1, wherein the deep learning network model is trained using training dataset comprising a SPECT image acquired with the acceleration scheme, a corresponding CT image and a SPECT image acquired using a standard number of acquisition planes (Materials and methods: Subjects and image acquisition: Para. 1 lines 4-7 and 10-12 and 19-21, Fig. 1, "1/7 SPECT" "standard SPECT" "CT images" "training").
Pan et al. does not explicitly teach wherein the acceleration scheme comprises a second parameter indicative of a reduction of the number of acquisition planes.
However, Sahbaee Bagherzadeh et al. discloses the concept of an acceleration scheme comprising reducing the number of acquisition planes in order to lower radiation dosage (Para. 0019 lines 9-14, 0029 lines 1-5, 0030 lines 1-5, 0033 lines 1-17, 0034 lines 20-25, "reduced number" "lower radiation dosage").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the concept of an acceleration scheme comprising reducing the number of acquisition planes as taught by Sahbaee Bagherzadeh et al. into the computer-implemented method of Pan et al. The motivation for this would be to lower the radiation dosage as accelerated SPECT images are acquired.
With regards to claim 9, the combination of Pan et al. and Sahbaee Bagherzadeh et al. discloses computer-implemented method of claim 8, wherein the input image to the deep learning network model comprises co-registered 3D volume of the first medical image and the second medical image (Pan et al.: Image preprocess: Para. 1 lines 1-10, "combine 1/7 SPECT image and CT image" "difference of voxel values" "concatenate in the first dimension").
Allowable Subject Matter
Claims 11 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
With regards to claim 11, Pan et al. (Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept) discloses applying a deep learning network model to the input image and outputting an enhanced medical image, wherein the deep learning network model is selected based at least in part on the acceleration scheme, however, there is no mention of specifically where the deep learning network model is selected from a plurality of trained models, and where the plurality of trained models correspond to different types of artifacts or different acceleration schemes. Li et al. (US 2019/0244399) discloses determining one or more trained machine learning models by training one or more preliminary machine learning models based on artifacts, however, there is no specific mention of where the deep learning network model is selected from a plurality of trained models, and where the plurality of trained models correspond to different types of artifacts or different acceleration schemes. Thus, while different prior arts disclose parts of the claim, none of the prior arts disclose or have reasonable motivation to combine to disclose all of the limitations of the claim as a whole.
With regards to claim 12, Pan et al. (Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept) discloses synthesizing one or more projection planes in the process of enhancing the medical images, however, there is no mention of processing the first medical image by a convolutional neural network to synthesize one or more projection planes prior to applying the deep learning network model. Thus, while different prior arts disclose parts of the claim, none of the prior arts disclose or have reasonable motivation to combine to disclose all of the limitations of the claim as a whole.
Claim 10 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
With regards to claim 10, Pan et al. (Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept) discloses a U2-net as the deep learning network model, which comprises a 2D convolutional layer and not a 3D convolutional layer. Song et al. (Low-dose Cardiac-Gated SPECT Studies Using a Residual Convolutional Neural Network) discloses enhancing SPECT images by using a 3D residual convolutional neural network, which comprises a 3D convolutional layer, however, there is no mention of the rest of the limitations of the claim and there is no reasonable motivation to combine with Pan et al. which discloses a deep learning network model using a 2D convolutional layer. Thus, while different prior arts disclose parts of the claim, none of the prior arts disclose or have reasonable motivation to combine to disclose all of the limitations of the claim as a whole.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAROL W CHAN whose telephone number is (571)272-5766. The examiner can normally be reached 9:30-3:30 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached at (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAROL W CHAN/Primary Examiner, Art Unit 2672
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