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 .
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.
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) 1-3, 7, 10-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over den Bouter et al. (“Deep learning-based single image super-resolution for low-field MR brain images”), hereinafter referred to as den Bouter, in view of Levac et al. (“Super Resolution Enhanced PROPELLER for Retrospective Motion Correction.. With reference to claim 1, den Bouter teaches A method for generating magnetic resonance (MR) image data of an examination object with increased resolution, the method comprising: sampling k-space data and a complementary region of the Cartesian k-space being unsampled (Dataset and training, second paragraph, Fig. 4, 5 and 6, descriptions thereof); reconstructing first MR image data based on the sampled k-space data (Fig. 4, 5 and 6, description thereof); and generating second MR image data with an increased resolution compared to a resolution of the reconstructed first MR image data by applying a supplementing method, using an artificial intelligence (AI)-based model, the supplementing method being adapted to supplement the reconstructed first MR image data with image information which, transformed into the Fourier domain, is associated with the complementary region determined from the k-space sampling (Fig. 4, 5 and 6, description thereof, Results section). However den Bouter is silent with regards to sampling k-space data with a non-rectangular sampling pattern, a non-rectangular sampling region of a Cartesian k-space being sampled. Levac teaches to sampling k-space data with a non-rectangular sampling pattern, a non-rectangular sampling region of a Cartesian k-space being sampled (Methods section, Fig 1, description thereof) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Levac with the method of den Bouter so as to improve motion correction.
With reference to claim 2, den Bouter as combined above further teaches the complementary region has higher sampling frequencies compared to sampling frequencies of the non-rectangular sampling region, the increased resolution being based on supplementing image information, which is associated with the higher sampling frequencies in the k-space, which lie in the complementary region (Fig. 4, 5 and 6, descriptions thereof). With reference to claim 3, den Bouter as combined above further teaches the non-rectangular sampling pattern comprises: elliptical sampling; radial sampling; helical sampling; balanced steady state free precession line acquisition with undersampling (BLADE) sampling; and/or Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) sampling (Levac, Methods section).
With reference to claim 7, den Bouter as combined above further teaches a dedicated model for the sampling pattern type specifically used for the non-rectangular sampling pattern is utilized as the AI-based model (Levac, Methods section). With reference to claim 10, den Bouter as combined above further teaches A non-transitory computer-readable storage medium with an executable program stored thereon, that when executed, instructs a processor to perform the method of claim 1 (Methods section). With reference to claim 11, den Bouter teaches A method for training an artificial intelligence (AI)-based model for a method for supplementing magnetic resonance (MR) image data with image information, comprising: generating labeled training data which has input data and validated results data, wherein: the validated results data comprises reconstructed MR image data from an examination object having been reconstructed based on k-space data obtained by a complete sampling of a Cartesian k-space (Fig. 4, 5, 6, descriptions thereof), and the input data comprises reconstructed MR image data from the examination object, which was reconstructed based on a subset of the k-space data, wherein the subset of the k-space data is associated with a sampling region of k-space, which was generated by setting the k-space data outside of the sampling region to zero (Fig. 4, 5, 6, descriptions thereof); applying the AI-based model to be trained with the method for supplementing magnetic resonance image data with image information to the input data to generate results data; and generating a trained AI-based model by adapting the AI-based model based on the results data and the validated results data (Fig. 4, 5 and 6, description thereof, Results section).
However den Bouter is silent with regards to sampling k-space data with a non-rectangular sampling pattern, a non-rectangular sampling region of a Cartesian k-space being sampled. Levac teaches to sampling k-space data with a non-rectangular sampling pattern, a non-rectangular sampling region of a Cartesian k-space being sampled (Methods section, Fig 1, description thereof) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Levac with the method of den Bouter so as to improve motion correction.
With reference to claim 12, den Bouter as combined above further teaches the trained AI-based model is based on a super resolution network (Methods section). With reference to claim 13, den Bouter as combined above further teaches the input data and/or the results data comprises phase images or complex-valued images (Figs. 4, 5, 6, descriptions thereof). With reference to claim 14, den Bouter as combined above further teaches A non-transitory computer-readable storage medium with an executable program stored thereon, that when executed, instructs a processor to perform the method of claim 11 (Figs. 4, 5, 6, descriptions thereof).
With reference to claim 15, den Bouter teaches An image data-generating device, comprising: an input interface adapted to receive sampled k-space and a complementary region of the Cartesian k-space is unsampled (Figs. 4, 5 and 6, descriptions thereof); a reconstructor adapted to reconstruct first magnetic resonance (MR) image data based on the sampled k-space data (Figs. 4, 5 and 6, descriptions thereof); an image data-generator adapted to generate second MR image data with increased resolution compared to a resolution of the reconstructed first MR image data by applying a supplementing method, which is based on an artificial intelligence (AI)-based model adapted to supplement the reconstructed first MR image data with image information which, transformed into the Fourier domain, is associated with the complementary region determined from the k-space-sampling (Figs. 4, 5 and 6, descriptions thereof, results section).
However den Bouter is silent with regards to sampling k-space data with a non-rectangular sampling pattern, a non-rectangular sampling region of a Cartesian k-space being sampled. Levac teaches to sampling k-space data with a non-rectangular sampling pattern, a non-rectangular sampling region of a Cartesian k-space being sampled (Methods section, Fig 1, description thereof) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Levac with the device of den Bouter so as to improve motion correction.
With reference to claim 16, den Bouter as combined above further teaches a magnetic resonance imaging (MRI) system comprising the image data-generating device as claimed in claim 15 (methods section).
Allowable Subject Matter
Claims 4-6, 8 and 9 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.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art does not disclose or suggest the claimed " generating the second MR image data comprises: generating preliminary second MR image data for generating the data consistency, the preliminary second MR image data being transformed into the Fourier domain, wherein Fourier domain data associated with the preliminary second MR image data is generated; generating modified Fourier domain data, the Fourier domain data associated with the preliminary second MR image data within the non-rectangular sampling region being modified by a projection of Fourier domain data associated with the first MR image data onto the Fourier domain data associated with the preliminary second MR image data; transforming the modified Fourier domain data into the image data domain to generate data-consistent second MR image data" in combination with the remaining claim elements as set forth in claims 4-6.
The prior art does not disclose or suggest the claimed " as a function of geometry of the non-rectangular sampling pattern, a plurality of image portions formed by a subset of the first MR image data is defined and the supplementing method is applied separately to the respective image portions and the second MR image data generated based on the first MR image data of the respective image portions is combined to form an overall image with increased resolution" in combination with the remaining claim elements as set forth in claims 8 and 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lally (US 2023/0113135 A1) teaches a magnetic resonance imaging method and device.
Lazarus et al. (US 11,789,104 B2) teach deep learning techniques for suppressing artefacts in MRI.
Choi et al. (US 10,317,496 B2) teach a MRI apparatus and control method for reconstruction of undersampled data.
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/GREGORY H CURRAN/ Primary Examiner, Art Unit 2852