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 .
Response to Amendment/Arguments
This action is in response to the Applicant’s amendment and arguments dated 3/16/2026.
The Applicant’s amendments/arguments are fully considered regarding rejection under 35 USC 103 however they are not persuasive with respect to the art rejection and moot due to new grounds of rejection and Applicant's amendment necessitated the new ground(s) of rejection. Please see the updated 35 USC 103 rejection
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.
Claims 1-18 are rejected under 35 USC 103 as being unpatentable over Li (Eliminating CT radiation for PET examination using deep learning, European Journal of Radiology, 23 June 2022, 072-048X@2022 Elsevier, pages 1-9, Year: 2022 http/doi.org/10.1016/ejrad.2022.110422) in view of Shi (US 20220207791).
Regarding claims 1, 7 and 13 Li disclose system, a storage device and processing unit (Li Fig. 1, page 2, paragraphs 2-3. Methos-Data., left thru right column);
reconstruct a three-dimensional image based on nuclear imaging data/ system (Li Fig. 1, page 2, paragraph 3. Data, right-column, lines 11-16 note: PET image and voxels);
input the three-dimensional image to a model to generate a linear attenuation map (Li ABSTRACT, page 1, section Introduction left column, Fig 1 shows inputting PET [NAC] image which is obviously 3D image input to model , as disclosed page 2, section 3, first paragraph PET [voxels], attenuation and inverse attenuation map which is attenuation correction map shown in Fig. 1, ABSTRACT and page 1, section Introduction left column generating linear attenuation map of PET and inverse linear attenuation correction map and page 6, section Discussion, which is obviously linear attenuation correction map).
generate a simulated computed tomography image based on the linear attenuation correction map (Li Fig. 1, ABSTRACT, paragraph Introduction disclose pseudo or synthetic CT image is generated from linear inverse attenuation map which is obviously linear attenuation correction map as described in section Introduction, page 1 and page 6, section Discussion) and
apply a segmentation algorithm to the simulated computed tomography image to generate a segmentation map (Li Figs. 4, 6 show segmented parts of body and Fig. 10 page 8, shows segmentation of regions of interest [ROIs RO1 1, ROI 2 and RO1 3] of CT synthetic image it is obvious that Li system apply a segmentation algorithm to the simulated computed tomography image is generate a segmentation map, also note: page 4, section 5.2, right-column, last paragraph, page 5, last paragraph and page 6, section 5.2, left-column thru right column).
Li has not explicitly discloses input the three-dimensional image to a trained convolutional network and receive a linear attenuation correction map output from the trained convolutional network in response to the input of the three-dimensional image
In the same field endeavor Shi disclose input the three-dimensional image to a trained convolutional network and receive a linear attenuation correction map output from the trained convolutional network in response to the input of the three-dimensional image (Shi paragraph 0043 3D SPECT image voxels and paragraph 0038 Shi disclose “estimating attenuation coefficients from only SPECT emission data uses a deep learning-based model for estimating attenuation maps directly from SPECT emission data. Briefly, estimating attenuation coefficients from only SPECT emission data uses a deep learning-based model for estimating attenuation maps directly from SPECT emission data. Briefly, 3D (three-dimensional) models are developed using a generator network 10, which in accordance with the present invention is a deep convolutional neural network (CNN) with Generative Adversarial Network (GAN) training, to estimate attenuation maps for SPECT directly and solely from the SPECT emission data 12a, 12b. As demonstrated below, qualitative and quantitative analysis demonstrates that the present method and system is capable of generating accurate attenuation maps. Evaluations on real human data showed that the present method produces attenuation maps that are consistent with CT-based attenuation maps, and provides accurate attenuation correction for SPECT images. The attenuation maps produced in accordance with the present invention are then used to correct raw SPECT data or SPECT images reconstructed. As demonstrated below, qualitative and quantitative analysis demonstrates that the present method and system is capable of generating accurate attenuation maps. Evaluations on real human data showed that the present method produces attenuation maps that are consistent with CT-based attenuation maps, and provides accurate attenuation correction for SPECT images. The attenuation maps produced in accordance with the present invention are then used to correct raw SPECT data or SPECT images reconstructed. This disclosure of Shi obviously corresponds to input the three-dimensional image to a trained convolutional network and receive a linear attenuation correction map output from the trained convolutional network in response to the input of the three-dimensional image).
Therefore it would have been obvious to one of ordinary skill in the art, before the claimed invention was filed to input the three-dimensional image to a trained convolutional network and receive a linear attenuation correction map output from the trained convolutional network in response to the input of the three-dimensional image as disclosed by Shi in the system Li because such a system provides and produces attenuation maps that are consistent with CT-based attenuation maps, and provides accurate attenuation correction for SPECT images as disclosed by Shi paragraph 0038.
Regarding claims 2, 8 and 14 Li disclose the three-dimensional image is a non-attenuation corrected image, reconstruct an attenuation-corrected three-dimensional image based on the nuclear imaging data and the linear attenuation correction map (LI in Fig. 1, the three-dimensional image is a non-attenuation corrected image, reconstruct an attenuation-corrected three-dimensional image based on the nuclear imaging data and the linear attenuation correction map i.e. NAC PET and s AC PET ).
Furthermore Shi disclose the three-dimensional image is a non-attenuation corrected image, reconstruct an attenuation-corrected three-dimensional image based on the nuclear imaging data and the linear attenuation correction map (Shi paragraph 0038 Shi Shi disclose “estimating attenuation coefficients from only SPECT emission data uses a deep learning-based model for estimating attenuation maps directly from SPECT emission data. Briefly, estimating attenuation coefficients from only SPECT emission data uses a deep learning-based model for estimating attenuation maps directly from SPECT emission data. Briefly, 3D (three-dimensional) models are developed using a generator network 10, which in accordance with the present invention is a deep convolutional neural network (CNN) with Generative Adversarial Network (GAN) training, to estimate attenuation maps for SPECT directly and solely from the SPECT emission data 12a, 12b. As demonstrated below, qualitative and quantitative analysis demonstrates that the present method and system is capable of generating accurate attenuation maps. Evaluations on real human data showed that the present method produces attenuation maps that are consistent with CT-based attenuation maps, and provides accurate attenuation correction for SPECT images).
Regarding claims 3, 9 and 15 LI discloses display the segmentation map and the attenuation-corrected three-dimensional image (Li in page 2, Fig. 1 shows attenuation correction of the image and page 8, segmentation map of organ in ROIs for attenuation corrected PET and synthetic CT image. Therefore it is obvious that Li system include display the segmentation map and the attenuation-corrected three-dimensional image also note: page 4, section 5.2, right-column, last paragraph, page 5, last paragraph and page 6, section 5.2, left-column thru right column) .
Regarding claim 4, 10 and 16 Li disclose display of the segmentation map and the attenuation corrected three-dimensional image comprise generation of a composite image based on the segmentation map and the attenuation- corrected three-dimensional image and display of the composite image (Li discloses Fig. 1 page 2, disclose attenuation corrected PET image and Fig. 10, page 8 disclose PET attenuation corrected image with segmentation map of ROIs. Also note: note: page 4, section 5.2, right-column, last paragraph, page 5, last paragraph and page 6, section 5.2, left-column thru right column. Therefore it is obvious that Li system include display of the segmentation map and the attenuation corrected three-dimensional image comprise generation of a synthesis i.e. composite image based on the segmentation map and the attenuation- corrected three-dimensional image and display of the composite image).
Regarding claim 5, 11 and 17 Li disclose generate a composite image based on the segmentation map and the three-dimensional image and display the composite image ( Li Fig. 1 page 2, disclose attenuation corrected PET image/non-attenuation corrected PET and Fig. 10, page 8, disclose PET attenuation corrected/non-attenuation corrected image with segmentation map of ROIs. Also note: note: page 4, section 5.2, right-column, last paragraph, page 5, last paragraph and page 6, section 5.2, left-column thru right column).
Regarding claim 6, 12 and 18 Li disclose generation of an initial simulated computed tomography image based on the linear attenuation correction map (Li, ABSTRACT, Fig. 1, page 2, synthetic CT i.e., simulated image ) and up-sampling of the initial simulated computed tomography image to generate the simulated computed tomography image (Li, Fig. 1, simulated CT image, e page 2 section 2.1, right column last paragraph scaling the CT image and Fig. 10 shows zoomed in ROIs CT image and note page 5, section 5.2, right column, last paragraph. Therefore it is obvious that the Li system include generation of an initial simulated computed tomography image based on the linear attenuation correction map and up-sampling of the initial simulated computed tomography image to generate the simulated computed tomography image.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/ISHRAT I SHERALI/Primary Examiner, Art Unit 2667
ISHRAT I. SHERALI
Examiner
Art Unit 2667
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