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 .
Response to Amendment
Claims 1, 7, 13, and 16 are amended. Claim 10 is canceled. Claims 1-9, 11, and 13-16 are pending in the application.
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.
Claim(s) 1-9, 11, and 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramani et al., US 2021/0012463 in view of Souza et al., US 2022/0240879.
Regarding claim 1, Ramani discloses a computer-implemented method for denoising medical image data (fig. 3; para 0031 and 0042; methods and systems to reduce noise and artifacts in images acquired in multi-energy CT imaging (e.g., spectral CT imaging), comprising:
obtaining an input dataset (figs. 2a-2b; para 0039; multi-spectral projections and fig. 3, element 62; para 0040; acquiring multi-energy scan datasets) comprising:
first basis image data of a region of interest of a subject (fig. 1, element 24; para 0033; a subject), comprising image domain data responsive to a first type of material in the region of interest (fig. 2b; para 0039; basis material images) and/or spectra of energy passing through or generated in the region of interest (fig. 2a; para 0039; basis material projections); and
second basis image data of the region of interest of the subject comprising image domain data responsive to a second type of material in the region of interest (fig. 2b; para 0039; basis material images) and/or spectra of energy passing through or generated in the region of interest (fig. 2a; para 0039; basis material projections), wherein the first and second basis image data are generated by a computed tomography scanner (fig. 1; para 0004 and 0032; a CT scanner); and
processing the input dataset (figs. 2a-2b; para 0039; proposed method) using a machine-learning algorithm to generate a first denoised basis image responsive to the first type of material and/or spectra of energy, and a second denoised basis image responsive to the second type of material and/or spectra of energy (para 0042; the method 58 further includes performing denoising (e.g., via joint and individual treatment or processing) of the images 64 in the spectral and/or spatial domains utilizing a deep learning-based denoising network to generate a plurality of denoised arbitrary basis images or vectors of the denoised arbitrary basis images 68).
Ramani further discloses the X-ray controller 38 may be configured to provide fast-kVp switching of the X-ray source 12 so as to rapidly switch the source 12 to emit X-rays at the respective polychromatic energy spectra in succession during an image acquisition session. For example, in a dual-energy imaging context, the X-ray controller 38 may operate the X-ray source 12 so that the X-ray source 12 alternately emits X-rays at the two polychromatic energy spectra of interest, such that adjacent projections are acquired at different energies (i.e., a first projection is acquired at high energy, the second projection is acquired at low energy (para 0035).
Ramani discloses claim 1 as enumerated above, but Ramani does not explicitly disclose the CT scanner operating in a low dosage mode as claimed.
However, Souza discloses X-ray based imaging devices may be configured to selectively operate in a normal-dose mode and a low-dose mode. The low-dose mode may expose the patient to a lower amount of radiation than the normal-dose mode. Embodiments of the present disclosure may utilize low-dose imaging that exposes the patient to 75% or less of the amount of radiation to which a patient would be exposed in a normal-dose mode (para 0060).
Therefore, taking the combined disclosures of Ramani and Souza as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate X-ray based imaging devices may be configured to selectively operate in a normal-dose mode and a low-dose mode as taught by Souza into the invention of Ramani for the benefit of achieving the reduction in radiation exposure (Souza: para 0060).
Regarding claim 2, the computer-implemented method of claim 1, Ramani in the combination further disclose wherein the input dataset further comprises combined image data of the region of interest of the subject, wherein the combined image data is generated, by the computed tomography scanner, using projection data obtained from all detection elements of the computed tomography scanner (para 0033).
Regarding claim 3, the computer-implemented method of claim 1, Ramani in the combination further disclose wherein the machine-learning algorithm is an artificial neural network formed of a plurality of layers (para 0028).
Regarding claim 4, the computer-implemented method of claim 3, Ramani in the combination further disclose wherein processing the input dataset using the machine-learning algorithm comprises setting values of the first layer of the artificial neural network to include all values of the first basis image data and all values of the second basis image data (fig. 4; para 0045).
Regarding claim 5, the computer-implemented method of claim 1, Ramani in the combination further disclose wherein the first basis image data comprises a single first basis image, and the second basis image data comprises a single second basis image (fig. 4; para 0045).
Regarding claim 6, the computer-implemented method of claim 1, Ramani in the combination further disclose wherein the first basis image data comprises a plurality of first basis images, each first basis image being two-dimensional, and the second basis image data comprises a plurality of second basis images, each second basis image being two-dimensional (para 0044 and 0050).
Regarding claim 7, the computer-implemented method of claim 6, Ramani in the combination further disclose wherein the plurality of first basis images comprises fewer than 10 first basis images, and the plurality of second basis images comprises fewer than 10 second basis images (figs. 2a-2b; para 0039; Ramani discloses obtaining multiple basis material images; therefore it is within the capability of Ramani to obtain fewer than 10 basis material images).
Regarding claim 8, the computer-implemented method of claim 1, Ramani in the combination further disclose wherein the first basis image data only comprises at least one photoelectric image, and the second basis image data only comprises at least one Compton-scatter image (para 0032 and 0035; low energy polychromatic emission spectra (i.e., photoelectric image) and high energy polychromatic emission spectra (i.e., Compton-scatter image)).
Regarding claim 9, the computer-implemented method according to claim 1, Ramani in the combination further disclose wherein the machine-learning algorithm is a residual machine-learning algorithm that produces noise imaging data (para 0055).
Regarding claim 11, the computer-implemented method of claim 1, Ramani in the combination further disclose wherein:
the input dataset comprises one or more sets of additional basis image data of the region of interest of the subject, each set of additional basis image data comprising image domain data responsive to a respective different type of material in the region of interest and/or spectra of energy passing through or generated in the region of interest (para 0032); and
processing the input dataset using a machine-learning algorithm further generates, for each set of additional basis image data, a respective additional denoised basis image, being a denoised image responsive to the respective type of material and/or spectra of energy of the corresponding set of additional basis image data (para 0032 and 0042).
Regarding claim 13, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 14, this claim recites substantially the same limitations that are performed by claim 2 above, and it is rejected for the same reasons.
Regarding claim 15, this claim recites substantially the same limitations that are performed by claim 3 above, and it is rejected for the same reasons.
Regarding claim 16, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Response to Arguments
Applicant's arguments with respect to claims 1-9, 11, and 13-16 have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VAN D HUYNH whose telephone number is (571)270-1937. The examiner can normally be reached 8AM-6PM.
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, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VAN D HUYNH/Primary Examiner, Art Unit 2665
Prosecution Insights