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公开(公告)号：US20220392018A1
公开(公告)日：2022-12-08
申请号：US17340635
申请日：2021-06-07
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.
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公开(公告)号：US20230019733A1
公开(公告)日：2023-01-19
申请号：US17378448
申请日：2021-07-16
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
IPC: G06T5/00 , G06T7/00 , G01R33/48 , G01R33/565
Abstract: Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.
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公开(公告)号：US12045958B2
公开(公告)日：2024-07-23
申请号：US17378448
申请日：2021-07-16
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
IPC: G06T5/70 , G01R33/48 , G01R33/565 , G06T7/00
CPC classification number: G06T5/70 , G01R33/4818 , G01R33/56509 , G06T7/0014 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084
Abstract: Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.
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公开(公告)号：US11948288B2
公开(公告)日：2024-04-02
申请号：US17340635
申请日：2021-06-07
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T5/70 , G06N3/08 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/70 , G06T2207/10088 , G06T2207/20056 , G06T2207/20081 , G06T2207/20084 , G16H50/50
Abstract: Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.
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