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 § 102
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- 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jun et al. (“Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI”), hereinafter referred to as Jun.
Jun teaches an image processing method, performed by an electronic device, the method comprising: obtaining, through a plurality of radio frequency coils, a plurality of pieces of undersampled frequency-domain data respectively, a radio frequency coil being configured to obtain one piece of undersampled frequency-domain data (Fig. 1, Input, Introduction); performing, by using a plurality of image processing networks that are cascaded, an information supplement operation respectively on the plurality of pieces of frequency-domain data to obtain a plurality of corresponding target restored images, a piece of frequency-domain data being configured for obtaining one target restored image, and an image processing network comprising an image restoring network (Fig. 1, “CNN image De-aliasing”), a frequency-domain complement network (Fig. 1, “CNN k-space interpolation), and a susceptibility estimation network (Fig. 1, “CNN-based Coil Sensitivity Maps Reconstruction”); and determining a target reconstructed image based on the plurality of target restored images (Fig. 1, “Output”). With reference to claim 2, Jun further teaches performing the information supplement operation respectively on the plurality of pieces of frequency-domain data to obtain the plurality of corresponding target restored images comprises: performing, for a piece of frequency-domain data, following operations in sequence according to a cascading order of the plurality of image processing networks: performing, for the first image processing network by an image restoring network in a current cascade, image-domain information supplement on the frequency-domain data, and inputting an obtained restored image in the current cascade to a frequency-domain complement network in a next cascade for frequency-domain information supplement; and performing, by a frequency-domain complement network in the current cascade, frequency-domain information supplement on the frequency-domain data, and inputting obtained frequency-domain complement data in the current cascade to a susceptibility estimation network in the next cascade for susceptibility supplement; and performing, for a non-first image processing network by an image restoring network in a current cascade, image-domain information supplement on frequency-domain complement data output by a frequency-domain complement network in a previous cascade and coil susceptibility output by a susceptibility estimation network in the previous cascade to obtain a restored image in the current cascade, and using the restored image in the current cascade output by an image processing network in the last cascade as the target reconstructed image (Section 3.4).
With reference to claim 3, Jun further teaches performing, by the image restoring network in the current cascade, the image-domain information supplement on the frequency-domain data, and inputting the obtained restored image in the current cascade to the frequency-domain complement network in the next cascade for frequency-domain information supplement comprises: performing an inverse Fourier transform on the frequency-domain data to obtain an initial time-domain image; and performing, by the image restoring network in the current cascade, the image-domain information supplement on the initial time-domain image, and inputting the obtained restored image in the current cascade to the frequency-domain complement network in the next cascade for the frequency-domain information supplement (Section 3.2, last paragraph). With reference to claim 4, Jun further teaches performing, by the image restoring network in the current cascade, the image-domain information supplement on the initial time-domain image comprises: performing, by the image restoring network in the current cascade, following processing on the initial time-domain image: performing a pooling operation on the initial time-domain image to obtain a pooled feature map; and performing upsampling on a downsampled feature map to obtain an upsampled feature map, and using the upsampling feature map as the obtained restored image in the current cascade (Section 4.1). With reference to claim 5, Jun further teaches performing following operations for the first image processing network: selecting target data within a preset frequency range from the frequency-domain data, and performing an inverse Fourier transform on the target data to obtain initial coil susceptibility; and performing, by a susceptibility estimation network in the current cascade, susceptibility supplement on the initial coil susceptibility, and inputting obtained coil susceptibility in the current cascade to the frequency-domain complement network in the next cascade for frequency-domain information supplement (Section 3.2 last paragraph). With reference to claim 6, Jun further teaches performing, by the image restoring network in the current cascade, image-domain information supplement on frequency-domain complement data output by the frequency-domain complement network in the previous cascade and the coil susceptibility output by the susceptibility estimation network in the previous cascade to obtain the restored image in the current cascade comprises: performing an inverse Fourier transform and a shrinking operation on the frequency-domain complement data output by the frequency-domain complement network in the previous cascade to obtain a time-domain image; and performing, by the image restoring network in the current cascade, the image-domain information supplement on the time-domain image and the coil susceptibility output by the susceptibility estimation network in the previous cascade to obtain the restored image in the current cascade (Section 3.2, last paragraph). With reference to claim 7, Jun further teaches performing following operations for the non-first image processing network: performing, by a frequency-domain complement network in the current cascade, frequency-domain information supplement on a restored image output by an image restoring network in the previous cascade to obtain frequency-domain complement data in the current cascade; and performing, by a susceptibility estimation network in the current cascade, susceptibility supplement on the frequency-domain complement data output by the frequency-domain complement network in the previous cascade to obtain coil susceptibility in the current cascade (Section 2.2). With reference to claim 8, Jun further teaches performing, by the frequency-domain complement network in the current cascade, the frequency-domain information supplement on a restored image output by the image restoring network in the previous cascade to obtain frequency-domain complement data in the current cascade comprises: performing a Fourier transform and an extended operation on the restored image output by the image restoring network in the previous cascade to obtain corresponding to-be-complemented frequency-domain data; and performing, by the frequency-domain complement network in the current cascade, the frequency-domain information supplement on the to-be-complemented frequency-domain data to obtain the frequency-domain complement data in the current cascade (Section 2.2). With reference to claim 9, Jun further teaches determining the target reconstructed image based on the plurality of target restored images comprises: performing a residual sum of square operation on the obtained plurality of target restored images to obtain the target reconstructed image (Section 4.1). With reference to claim 10, Jun further teaches performing joint training by using the following manners to obtain the plurality of image processing networks: performing, based on an undersampled sample data set, joint iteration training on a plurality of to-be-trained processing networks that are cascaded to obtain the plurality of image processing networks, the following operations being performed in each iteration training: performing, by the plurality of to-be-trained processing networks, an information supplement operation respectively on a plurality of pieces of sample data selected from the sample data set to obtain a plurality of corresponding prediction restored images and a plurality of pieces of corresponding prediction frequency-domain complement data, and determining a prediction reconstructed image based on the plurality of prediction restored images; and determining a target loss function based on the prediction reconstructed image and the plurality of pieces of prediction frequency-domain complement data, and performing parameter adjustment by using the target loss function (Section 3.2). With reference to claim 11, Jun further teaches determining the target loss function based on the prediction reconstructed image and the plurality of pieces of prediction frequency-domain complement data comprises: determining a first loss function based on the plurality of pieces of prediction frequency-domain complement data and fully-sampled sample data corresponding to the plurality of pieces of sample data; determining a second loss function based on the prediction reconstructed image and a corresponding reference reconstructed image, the reference reconstructed image being constructed based on the fully-sampled sample data; and determining the target loss function based on the first loss function and the second loss function (Section 3.2). With reference to claim 12, Jun teaches a computer device, comprising a memory, at least one processor, and a computer program stored in the memory and executable on the at least one processor for performing: obtaining, through a plurality of radio frequency coils, a plurality of pieces of undersampled frequency-domain data respectively, a radio frequency coil being configured to obtain one piece of undersampled frequency-domain data (Fig. 1, Input, Introduction); performing, by using a plurality of image processing networks that are cascaded, an information supplement operation respectively on the plurality of pieces of frequency-domain data to obtain a plurality of corresponding target restored images, a piece of frequency-domain data being configured for obtaining one target restored image, and an image processing network comprising an image restoring network (Fig. 1, “CNN image De-aliasing”), a frequency-domain complement network (Fig. 1, “CNN k-space interpolation), and a susceptibility estimation network (Fig. 1, “CNN-based Coil Sensitivity Maps Reconstruction”); and determining a target reconstructed image based on the plurality of target restored images (Fig. 1, “Output”).
With reference to claim 13, Jun further teaches the at least one processor is further configured to perform: performing, for a piece of frequency-domain data, following operations in sequence according to a cascading order of the plurality of image processing networks: performing, for the first image processing network by an image restoring network in a current cascade, image-domain information supplement on the frequency-domain data, and inputting an obtained restored image in the current cascade to a frequency-domain complement network in a next cascade for frequency-domain information supplement; and performing, by a frequency-domain complement network in the current cascade, frequency-domain information supplement on the frequency-domain data, and inputting obtained frequency-domain complement data in the current cascade to a susceptibility estimation network in the next cascade for susceptibility supplement; and performing, for a non-first image processing network by an image restoring network in a current cascade, image-domain information supplement on frequency-domain complement data output by a frequency-domain complement network in a previous cascade and coil susceptibility output by a susceptibility estimation network in the previous cascade to obtain a restored image in the current cascade, and using the restored image in the current cascade output by an image processing network in the last cascade as the target reconstructed image (Section 3.4).
With reference to claim 14, Jun further teaches the at least one processor is further configured to perform: performing an inverse Fourier transform on the frequency-domain data to obtain an initial time-domain image; and performing, by the image restoring network in the current cascade, the image-domain information supplement on the initial time-domain image, and inputting the obtained restored image in the current cascade to the frequency-domain complement network in the next cascade for the frequency-domain information supplement (Section 3.4).
With reference to claim 15, Jun further teaches the at least one processor is further configured to perform: performing, by the image restoring network in the current cascade, following processing on the initial time-domain image: performing a pooling operation on the initial time-domain image to obtain a pooled feature map; and performing upsampling on a downsampled feature map to obtain an upsampled feature map, and using the upsampling feature map as the obtained restored image in the current cascade (Section 4.1).
With reference to claim 16, Jun further teaches the at least one processor is further configured to perform: performing following operations for the first image processing network: selecting target data within a preset frequency range from the frequency-domain data, and performing an inverse Fourier transform on the target data to obtain initial coil susceptibility; and performing, by a susceptibility estimation network in the current cascade, susceptibility supplement on the initial coil susceptibility, and inputting obtained coil susceptibility in the current cascade to the frequency-domain complement network in the next cascade for frequency-domain information supplement (Section 3.2). With reference to claim 17, Jun further teaches the at least one processor is further configured to perform: performing an inverse Fourier transform and a shrinking operation on the frequency-domain complement data output by the frequency-domain complement network in the previous cascade to obtain a time-domain image; and performing, by the image restoring network in the current cascade, the image-domain information supplement on the time-domain image and the coil susceptibility output by the susceptibility estimation network in the previous cascade to obtain the restored image in the current cascade (Section 3.2). With reference to claim 18, Jun further teaches the at least one processor is further configured to perform: performing following operations for the non-first image processing network: performing, by a frequency-domain complement network in the current cascade, frequency-domain information supplement on a restored image output by an image restoring network in the previous cascade to obtain frequency-domain complement data in the current cascade; and performing, by a susceptibility estimation network in the current cascade, susceptibility supplement on the frequency-domain complement data output by the frequency-domain complement network in the previous cascade to obtain coil susceptibility in the current cascade (Section 2.2).
With reference to claim 19, Jun further teaches the at least one processor is further configured to perform: performing a Fourier transform and an extended operation on the restored image output by the image restoring network in the previous cascade to obtain corresponding to-be-complemented frequency-domain data; and performing, by the frequency-domain complement network in the current cascade, the frequency-domain information supplement on the to-be-complemented frequency-domain data to obtain the frequency-domain complement data in the current cascade (Section 2.2).
With reference to claim 20, Jun teaches A non-transitory computer-readable storage medium, containing a computer program that, when being executed, causes a computer device to perform: obtaining, through a plurality of radio frequency coils, a plurality of pieces of undersampled frequency-domain data respectively, a radio frequency coil being configured to obtain one piece of undersampled frequency-domain data (Fig. 1, Input, Introduction); performing, by using a plurality of image processing networks that are cascaded, an information supplement operation respectively on the plurality of pieces of frequency-domain data to obtain a plurality of corresponding target restored images, a piece of frequency-domain data being configured for obtaining one target restored image, and an image processing network comprising an image restoring network (Fig. 1, “CNN image De-aliasing”), a frequency-domain complement network (Fig. 1, “CNN k-space interpolation), and a susceptibility estimation network (Fig. 1, “CNN-based Coil Sensitivity Maps Reconstruction”); and determining a target reconstructed image based on the plurality of target restored images (Fig. 1, “Output”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mailhe et al. (US 12,086,908 B2) teach reconstruction with MR compressed sensing.
Chatterjee et al. (US 11,808,832 B2) teach a system and method for deep learning-based generation of truer contrast images utilizing synthetic MRI data.
Schlemper et al. (US 11,185,249 B2) teach self ensembling techniques for generating MR images from spatial frequency data.
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/GREGORY H CURRAN/Primary Examiner, Art Unit 2852
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