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公开(公告)号：US10753997B2
公开(公告)日：2020-08-25
申请号：US16054319
申请日：2018-08-03
摘要： Systems and methods are provided for synthesizing protocol independent magnetic resonance images. A patient is scanned by a magnetic resonance imaging system to acquire magnetic resonance data. The magnetic resonance data is input to a machine learnt generator network trained to extract features from input magnetic resonance data and synthesize protocol independent images using the extracted features. The machine learnt generator network generates a protocol independent segmented magnetic resonance image from the input magnetic resonance data. The protocol independent magnetic resonance image is displayed.
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22.发明授权公开(公告)号：US10698063B2
公开(公告)日：2020-06-30
申请号：US16008086
申请日：2018-06-14
发明人： Sandro Braun , Boris Mailhe , Xiao Chen , Benjamin L. Odry , Pascal Ceccaldi , Mariappan S. Nadar
IPC分类号： G01R33/565 , G06T5/00 , G06N5/04 , G06T7/20 , G06N3/08
摘要： Systems and methods are provided for correcting motion artifacts in magnetic resonance images. An image-to-image neural network is used to generate motion corrected magnetic resonance data given motion corrupted magnetic resonance data. The image-to-image neural network is coupled within an adversarial network to help refine the generated magnetic resonance data. The adversarial network includes a generator network (the image-to-image neural network) and a discriminator network. The generator network is trained to minimize a loss function based on a Wasserstein distance when generating MR data. The discriminator network is trained to differentiate the motion corrected MR data from motion artifact free MR data.
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公开(公告)号：US20200042873A1
公开(公告)日：2020-02-06
申请号：US16394507
申请日：2019-04-25
发明人： Guillaume Daval Frerot , Xiao Chen , Simon Arberet , Boris Mailhe , Mariappan S. Nadar , Peter Speier , Mathias Nittka
摘要： For machine training and application of a trained complex-valued machine learning model, an activation function of the machine learning model, such as a neural network, includes a learnable parameter that is complex or defined in a complex domain with two dimensions, such as real and imaginary or magnitude and phase dimensions. The complex learnable parameter is trained for any of various applications, such as MR fingerprinting, other medical imaging, or non-medical uses.
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公开(公告)号：US20190377047A1
公开(公告)日：2019-12-12
申请号：US16002447
申请日：2018-06-07
IPC分类号： G01R33/56 , G01R33/565 , G01R33/48 , G06T11/00
摘要： For artifact reduction in a magnetic resonance imaging system, deep learning trains an image-to-image neural network to generate an image with reduced artifact from input, artifacted MR data. For application, the image-to-image network may be applied in real time with a lower computational burden than typical post-processing methods. To handle a range of different imaging situations, the image-to-image network may (a) use an auxiliary map as an input with the MR data from the patient, (b) use sequence metadata as a controller of the encoder of the image-to-image network, and/or (c) be trained to generate contrast invariant features in the encoder using a discriminator that receives encoder features.
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公开(公告)号：US20190094322A1
公开(公告)日：2019-03-28
申请号：US16143865
申请日：2018-09-27
摘要： For determining a configuration for a MR scanner using MRF, MRF is optimized using a model of acquisition and reconstruction together. The effects of under sampling and reconstruction are included in the model being optimized with the sequence. A spatial distribution of response is used in the MRF optimization. An added transformation may be included to convert the fingerprint into a domain for noise removal and/or more direct parameter estimation without the dictionary. Artificial intelligence may be used to alter and/or select the type of optimization or optimization settings and/or to optimize.
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公开(公告)号：US20190049540A1
公开(公告)日：2019-02-14
申请号：US16054319
申请日：2018-08-03
CPC分类号： G01R33/5608 , G01R33/543 , G06N3/0454 , G06N3/0472 , G06N3/084
摘要： Systems and methods are provided for synthesizing protocol independent magnetic resonance images. A patient is scanned by a magnetic resonance imaging system to acquire magnetic resonance data. The magnetic resonance data is input to a machine learnt generator network trained to extract features from input magnetic resonance data and synthesize protocol independent images using the extracted features. The machine learnt generator network generates a protocol independent segmented magnetic resonance image from the input magnetic resonance data. The protocol independent magnetic resonance image is displayed.
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公开(公告)号：US20180285695A1
公开(公告)日：2018-10-04
申请号：US15471079
申请日：2017-03-28
发明人： Yi Guo , Boris Mailhe , Xiao Chen , Mariappan S. Nadar
CPC分类号： G06K9/66 , A61B5/055 , A61B5/7267 , G01R33/5608 , G06T7/0012 , G06T11/008 , G06T2207/10088
摘要： A method for training a system for reconstructing a magnetic resonance image includes: under-sampling image data from each of a plurality of fully-sampled images; and inputting the under-sampled image data to a multi-scale neural network comprising sequentially connected layers. Each layer has an input for receiving input image data and an output for outputting reconstructed image data. Each layer performs a process comprising: decomposing the array of input image data; applying a thresholding function to the decomposed image data, to form a shrunk data, the thresholding function outputting a value asymptotically approaching one when the thresholding function receives an input having a magnitude greater than a first value, reconstructing the shrunk data for combining with a reconstructed image data output by another one of the layers to form updated reconstructed image data, and machine-learning at least one parameter of the decomposing, the thresholding function, or the reconstructing.
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公开(公告)号：US10043243B2
公开(公告)日：2018-08-07
申请号：US15176216
申请日：2016-06-08
发明人： Yevgen Matviychuk , Boris Mailhe , Xiao Chen , Qiu Wang , Mariappan S. Nadar
摘要： A computer-implemented method for denoising image data includes a computer system receiving an input image comprising noisy image data and denoising the input image using a deep multi-scale network comprising a plurality of multi-scale networks sequentially connected. Each respective multi-scale network performs a denoising process which includes dividing the input image into a plurality of image patches and denoising those image patches over multiple levels of decomposition using a threshold-based denoising process. The threshold-based denoising process denoises each respective image patch using a threshold which is scaled according to an estimation of noise present in the respective image patch. The noising process further comprises the assembly of a denoised image by averaging over the image patches.
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公开(公告)号：US20180081018A1
公开(公告)日：2018-03-22
申请号：US15272867
申请日：2016-09-22
IPC分类号： G01R33/563 , G01R33/56 , G01R33/567
CPC分类号： G01R33/56341 , G01R33/5608 , G01R33/5673
摘要： A computer-implemented method for sparse recovery of fiber orientations using a multidimensional Prony method for use in tractography applications includes performing magnetic resonance imaging to acquire a plurality of sparse signal measurements using a q-space sampling scheme which enforces a lattice structure with a predetermined number of collinear samples. Next, for each voxel included in the plurality of sparse signal measurements, a computer system is used to perform a parameter estimation process. This process includes translating a portion of the sparse signal measurements corresponding to the voxel into a plurality of Sparse Approximate Prony Method (SAPM) input parameters, and applying a SAPM process to the SAPM input parameters to recover a number of fiber populations, a plurality of orientation vectors, and a plurality of amplitude scalars. Then, one or more tractograms are generated using the number of fiber populations, the orientation vectors, and the amplitude scalars recovered for each voxel.
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公开(公告)号：US20170160363A1
公开(公告)日：2017-06-08
申请号：US15372445
申请日：2016-12-08
发明人： Xiao Chen , Boris Mailhe , Qiu Wang , Shaohua Kevin Zhou , Yefeng Zheng , Xiaoguang Lu , Puneet Sharma , Benjamin L. Odry , Bogdan Georgescu , Mariappan S. Nadar
CPC分类号： G01R33/5608 , G01R33/50 , G06N3/08
摘要： A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.
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