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公开(公告)号：US11846692B2
公开(公告)日：2023-12-19
申请号：US17733967
申请日：2022-04-29
Applicant: University of Virginia Patent Foundation
Inventor： Quan Dou , Zhixing Wang , Xue Feng , John P. Mugler, III , Craig H. Meyer
IPC: G01R33/56 , G01R33/565 , G06T7/262
CPC classification number: G01R33/5608 , G01R33/56509 , G06T7/262 , G06T2207/10088 , G06T2207/20084
Abstract: Training a neural network to correct motion-induced artifacts in magnetic resonance images includes acquiring motion-free magnetic resonance image (MRI) data of a target object and applying a spatial transformation matrix to the motion-free MRI data. Multiple frames of MRI data are produced having respective motion states. A Non-uniform Fast Fourier Transform (NUFFT) can be applied to generate respective k-space data sets corresponding to each of the multiple frames of MRI; the respective k-space data sets can be combined to produce a motion-corrupted k-space data set and an adjoint NUFFT can be applied to the motion-corrupted k-space data set. Updated frames of motion-corrupted MRI data can be formed. Using the updated frames of motion corrupted MRI data, a neural network can be trained that generates output frames of motion free MRI data; and the neural network can be saved.
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12.发明申请公开(公告)号：US20220373627A1
公开(公告)日：2022-11-24
申请号：US17732181
申请日：2022-04-28
Applicant: University of Virginia Patent Foundation
Inventor： Zhixing Wang , Steven P. Allen , Xue Feng , John P. Mugler, III , Craig H. Meyer
IPC: G01R33/48 , G01R33/561 , G01R33/56 , G01R33/54 , G01R33/565
Abstract: Methods, computing devices, and magnetic resonance imaging systems that improve image quality in turbo spiral echo (TSE) imaging are disclosed. With this technology, a TSE pulse sequence is generated that includes a series of radio frequency (RF) refocusing pulses to produce a corresponding series of nuclear magnetic resonance (NMR) spin echo signals. A gradient waveform including a plurality of segments is generated. The plurality of segments collectively comprise a spiral ring retraced in-out trajectory. During an interval adjacent to each of the series of RF refocusing pulses, a first gradient pulse is generated according to the gradient waveform. The first gradient pulses encode the NMR spin echo signals. An image is then constructed from digitized samples of the NMR spin echo signals obtained based at least in part on the encoding.
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13.发明申请公开(公告)号：US20200249306A1
公开(公告)日：2020-08-06
申请号：US16783144
申请日：2020-02-05
Applicant: University of Virginia Patent Foundation
Inventor： Mohammad Abdishektaei , Xue Feng , Xiaoying Cai , Craig H. Meyer , Frederick H. Epstein
IPC: G01R33/565 , G06N20/00 , G01R33/48 , G06N3/08
Abstract: Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.
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公开(公告)号：US09651645B2
公开(公告)日：2017-05-16
申请号：US15078790
申请日：2016-03-23
Applicant: UNIVERSITY OF VIRGINIA PATENT FOUNDATION
Inventor： Samuel W. Fielden , Craig H. Meyer , Xue Feng
IPC: G01V3/00 , G01R33/565 , G01R33/48
CPC classification number: G01R33/565 , G01R33/4824
Abstract: Systems, methods of reducing off-resonance blurring in acquired magnetic resonance imaging data. The method includes acquiring a first set of spiral interleaf data for each of one or more spiral-in/out interleaves by performing a first sampling each of one or more locations in k-space along a first redundant spiral-in/out trajectory, and acquiring a second set of spiral interleaf data for each of the one or more spiral-in/out interleaves by performing a second sampling of each of the one or more locations in the k-space along a second redundant spiral-in/out trajectory, wherein the second redundant spiral-in/out trajectory corresponds to a time-reversed trajectory of the first redundant spiral-in/out trajectory. The method may yet further include combining the first set of spiral interleaf data and the second set of spiral interleaf data with an averaging operation such as to reduce artifacts.
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15.发明申请公开(公告)号：US20160202335A1
公开(公告)日：2016-07-14
申请号：US15078790
申请日：2016-03-23
Applicant: UNIVERSITY OF VIRGINIA PATENT FOUNDATION
Inventor： Samuel W. Fielden , Craig H. Meyer , Xue Feng
IPC: G01R33/48 , G01R33/565
CPC classification number: G01R33/565 , G01R33/4824
Abstract: Systems, methods of reducing off-resonance blurring in acquired magnetic resonance imaging data. The method includes acquiring a first set of spiral interleaf data for each of one or more spiral-in/out interleaves by performing a first sampling each of one or more locations in k-space along a first redundant spiral-in/out trajectory, and acquiring a second set of spiral interleaf data for each of the one or more spiral-in/out interleaves by performing a second sampling of each of the one or more locations in the k-space along a second redundant spiral-in/out trajectory, wherein the second redundant spiral-in/out trajectory corresponds to a time-reversed trajectory of the first redundant spiral-in/out trajectory. The method may yet further include combining the first set of spiral interleaf data and the second set of spiral interleaf data with an averaging operation such as to reduce artifacts.
Abstract translation: 系统，减少获取的磁共振成像数据中的非共振模糊的方法。 该方法包括：通过沿着第一冗余螺旋进/出轨迹对k空间中的一个或多个位置执行第一采样，获取一个或多个螺旋输入/输出交错中的每一个的第一组螺旋插入数据，以及 通过沿着第二冗余螺旋进/出轨迹执行所述k空间中的所述一个或多个位置中的每个位置的第二采样来获取所述一个或多个螺旋输入/输出交错中的每一个的第二组螺旋插入数据， 其中所述第二冗余螺旋进/出轨迹对应于所述第一冗余螺旋进/出轨迹的时间反转轨迹。 该方法还可以包括将第一组螺旋插入数据和第二组螺旋插值数据与平均化操作组合，以减少伪影。
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Patent Agency Ranking
公开(公告)号：US20240394844A1
公开(公告)日：2024-11-28
申请号：US18642776
申请日：2024-04-22
Applicant: University of Virginia Patent Foundation
Inventor： Quan Dou , Xue Feng , Craig H. Meyer
Abstract: A computer implemented method of training a deep learning convolutional neural network (CNN) to correct output magnetic resonance images includes acquiring magnetic resonance image (MRI) data for a region of interest of a subject and saving the MRI data in frames of k-space data. The method includes calculating ground truth image data from the frames k-space data. The method includes corrupting the k-space data with real noise additions into the lines of the k-space data and saving in computer memory, training pairs a ground truth frame and a corrupted frame with real noise additions. By applying the training pairs to a U-Net convolutional neural network, the method trains the U-Net to adjust output images by correcting the output images for the real noise additions.
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17.发明公开公开(公告)号：US20230342886A1
公开(公告)日：2023-10-26
申请号：US18181520
申请日：2023-03-09
Applicant: University of Virginia Patent Foundation
Inventor： Craig H. Meyer , Quan Dou , Zhixing Wang , Xue Feng , John P. Mugler, III
CPC classification number: G06T5/002 , G06T5/50 , G06T11/008 , G06T2207/20081 , G06T2207/20084 , G06T2207/10088 , G06T2207/20224 , G06T2211/424
Abstract: MR image data can be improved by using a complex de-noising convolutional neural network such as a non-blind C-DnCNN, a network for MRI denoising that leverages complex-valued data with phase information and noise level information to improve denoising performance in various settings. The proposed method achieved superior performance on both simulated and in vivo testing data compared to other algorithms. The utilization of complex-valued operations allows the network to better exploit the complex-valued MRI data and preserve the phase information. The MR image data is subject to complex de-noising operations directly and simultaneously on both real and imaginary parts of the image data. Complex and real values are also utilized for block normalization and rectified linear units applied to the noisy image data. A residual image is predicted by the C-DnCNN and a clean MR image is available for extraction.
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公开(公告)号：US20220349970A1
公开(公告)日：2022-11-03
申请号：US17733970
申请日：2022-04-29
Applicant: University of Virginia Patent Foundation
Inventor： Zhixing Wang , Xue Feng , John P. Mugler, III , Michael Salerno , Adrienne E. Campbell-Washburn , Craig H. Meyer
IPC: G01R33/48 , G01R33/561 , G01R33/54 , G01R33/565 , G01R33/44
Abstract: Systems and methods for performing ungated magnetic resonance imaging are disclosed herein. A method includes producing magnetic resonance image MRI data by scanning a target in a low magnetic field with a pulse sequence having a spiral trajectory; sampling k-space data from respective scans in the low magnetic field and receiving at least one field map data acquisition and a series of MRI data acquisitions from the respective scans; forming a field map and multiple sensitivity maps in image space from the field map data acquisition; forming target k-space data with the series of MRI data acquisitions; forming initial magnetic resonance images in the image domain by applying a Non-Uniform Fast Fourier Transform to the target k-space data; and forming reconstructed images with a low rank plus sparse (L+S) reconstruction algorithm applied to the initial magnetic resonance images.
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19.发明授权公开(公告)号：US11294015B2
公开(公告)日：2022-04-05
申请号：US16783144
申请日：2020-02-05
Applicant: University of Virginia Patent Foundation
Inventor： Mohammad Abdishektaei , Xue Feng , Xiaoying Cai , Craig H. Meyer , Frederick H. Epstein
IPC: G01R33/565 , G06N20/00 , G06N3/08 , G01R33/48
Abstract: Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.
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公开(公告)号：US20210267455A1
公开(公告)日：2021-09-02
申请号：US17166604
申请日：2021-02-03
Applicant: University of Virginia Patent Foundation
Inventor： Sona Ghadimi , Changyu Sun , Xue Feng , Craig H. Meyer , Frederick H. Epstein
Abstract: A method of cardiac strain analysis uses displacement encoded magnetic resonance image (MRI) data of a heart of the subject and includes generating a phase image for each frame of the displacement encoded MRI data. Phase images include potentially phase-wrapped measured phase values corresponding to pixels of the frame. A convolutional neural network CNN computes a wrapping label map for the phase image, and the wrapping label map includes a respective number of phase wrap cycles present at each pixel in the phase image. Computing an unwrapped phase image includes adding a respective phase correction to each of the potentially-wrapped measured phase values of the phase image, and the phase correction is based on the number of phase wrap cycles present at each pixel. Computing myocardial strain follows by using the unwrapped phase image for strain analysis of the subject.
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