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公开(公告)号：US20190146048A1
公开(公告)日：2019-05-16
申请号：US16189430
申请日：2018-11-13
IPC分类号： G01R33/56
摘要： In a method and apparatus for noise decorrelation of magnetic resonance (MR) measurement signals acquired by multiple detectors of an MR apparatus, which are disturbed by additive noise, noise signals and reference signals of the multiple detectors are used to determine an improved noise decorrelation matrix, which removes a noise correlation in the MR measurement signals of the multiple detectors.
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公开(公告)号：US20190041480A1
公开(公告)日：2019-02-07
申请号：US16045022
申请日：2018-07-25
发明人： Simon Arberet , Xiao Chen , Boris Mailhe , Mariappan S. Nadar , Peter Speier
摘要： Systems and methods are provided for iterative reconstruction of a magnetic resonance image using Magnetic Resonance Fingerprinting (MRF). An image series is estimated according to the following three steps: a gradient step to improve data consistency, fingerprint matching, and a spatial regularization. Singular Value Decomposition (SVD) compression may be used along the time dimension to accelerate both the matching and the spatial regularization that operates in the compressed domain as well as to enforce low-rank regularization.
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公开(公告)号：US10175321B2
公开(公告)日：2019-01-08
申请号：US15332336
申请日：2016-10-24
发明人： Xiao Chen , Mariappan S. Nadar , Christopher Cline , Boris Mailhe , Qiu Wang
IPC分类号： G01R33/48 , G01R33/50 , G01R33/561
摘要： Disclosed herein is a method obtaining a magnetic resonance image of an object, comprising obtaining a first time evolution signal from a magnetic resonance signal from the object; performing a search of a compressed dictionary of magnetic resonance fingerprints to select a magnetic resonance fingerprint representative of the first time evolution signal, wherein the selected magnetic resonance fingerprint is an exact or approximate nearest neighbor match to the first time evolution signal; obtaining a magnetic resonance parameter associated with the selected fingerprint; generating the magnetic resonance image of the object from the obtained magnetic resonance parameter; and performing a second search of the compressed dictionary using the magnetic resonance image.
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公开(公告)号：US10145924B2
公开(公告)日：2018-12-04
申请号：US15405449
申请日：2017-01-13
发明人： Julia Traechtler , Qiu Wang , Boris Mailhe , Xiao Chen , Marcel Dominik Nickel , Mariappan S. Nadar
IPC分类号： G01R33/561 , G01R33/48
摘要： A method for magnetic resonance (MR) imaging is provided. A first sampling mask is provided for sampling along a first set of parallel lines extending in a first direction in k-space. A second sampling mask is provided for sampling along a second set of parallel lines extending in a second direction in k-space. The second direction is orthogonal to the first direction. A first set of MR k-space data is sampled using an MR scanner, by scanning a subject in the first direction using the first sampling mask. A second set of MR k-space data is sampled using the MR scanner, by scanning the subject in the second direction using the second sampling mask. An MR image is reconstructed from a combined set of MR k-space data including the first set of MR k-space data and the second set of MR k-space data.
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105.发明申请公开(公告)号：US20180240219A1
公开(公告)日：2018-08-23
申请号：US15893891
申请日：2018-02-12
发明人： Katrin Mentl , Boris Mailhe , Mariappan S. Nadar
摘要： The present embodiments relate to denoising medical images. By way of introduction, the present embodiments described below include apparatuses and methods for machine learning sparse image representations with deep unfolding and deploying the machine learnt network to denoise medical images. Iterative thresholding is performed using a deep neural network by training each layer of the network as an iteration of an iterative shrinkage algorithm. The deep neural network is randomly initialized and trained independently with a patch-based approach to learn sparse image representations for denoising image data. The different layers of the deep neural network are unfolded into a feed-forward network trained end-to-end.
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公开(公告)号：US20180203085A1
公开(公告)日：2018-07-19
申请号：US15405449
申请日：2017-01-13
发明人： Julia Traechtler , Qiu Wang , Boris Mailhe , Xiao Chen , Marcel Dominik Nickel , Mariappan S. Nadar
IPC分类号： G01R33/561 , G01R33/48
CPC分类号： G01R33/5612 , G01R33/4822 , G01R33/5611
摘要： A method for magnetic resonance (MR) imaging is provided. A first sampling mask is provided for sampling along a first set of parallel lines extending in a first direction in k-space. A second sampling mask is provided for sampling along a second set of parallel lines extending in a second direction in k-space. The second direction is orthogonal to the first direction. A first set of MR k-space data is sampled using an MR scanner, by scanning a subject in the first direction using the first sampling mask. A second set of MR k-space data is sampled using the MR scanner, by scanning the subject in the second direction using the second sampling mask. An MR image is reconstructed from a combined set of MR k-space data including the first set of MR k-space data and the second set of MR k-space data.
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公开(公告)号：US10012717B2
公开(公告)日：2018-07-03
申请号：US14685635
申请日：2015-04-14
IPC分类号： G01R33/561 , G01R33/48 , G01R33/483
CPC分类号： G01R33/5611 , G01R33/4822 , G01R33/4826 , G01R33/4835
摘要： A method for performing a magnetic resonance image reconstruction with spatially varying coil compression includes using a non-Cartesian acquisition scheme to acquire a multi-coil k-space dataset fully sampled along a fully sampled direction and decoupling the multi-coil k-space dataset along the fully sampled direction to yield a plurality of uncompressed coil data matrices. The plurality of uncompressed coil data matrices are compressed to yield a plurality of virtual coil data matrices which are aligned along the fully sampled direction to yield a plurality of aligned virtual coil data matrices. The aligned virtual coil data matrices are coupled along the fully sampled direction to yield a compressed multi-coil k-space dataset. Intensity values in the plurality of aligned virtual coil data matrices are normalized based on the plurality of uncompressed coil data matrices and an image is reconstructed using the compressed multi-coil k-space dataset.
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公开(公告)号：US20170372193A1
公开(公告)日：2017-12-28
申请号：US15596124
申请日：2017-05-16
CPC分类号： G06N3/02 , G06N3/0445 , G06N3/0454 , G06N3/0472 , G06N3/084 , G06N3/088 , G06N5/003 , G06N7/005 , G06N20/10 , G06N20/20 , G06T5/001 , G06T5/002 , G06T5/003 , G06T5/005 , G06T7/0002 , G06T2207/10072 , G06T2207/10132 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
摘要： For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.
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公开(公告)号：US20170371017A1
公开(公告)日：2017-12-28
申请号：US15629779
申请日：2017-06-22
IPC分类号： G01R33/56
摘要： A system and method including receiving magnetic resonance (MR) imaging data from a first MR scanner device, the MR imaging data including data for a plurality of MR scans of different structural or anatomical regions; generating, based on the MR imaging data, normalized reference data including statistical information for each MR scan; learning a transformation, based on the normalized reference data, to correlate a set of input MR imaging data to the normalized reference data; and storing a record of the transformed imaging data.
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公开(公告)号：US20170357844A1
公开(公告)日：2017-12-14
申请号：US15584393
申请日：2017-05-02
发明人： Dorin Comaniciu , Ali Kamen , David Liu , Boris Mailhe , Tommaso Mansi
CPC分类号： G06K9/00127 , G01N2800/7028 , G06F19/00 , G06F19/24 , G06F19/26 , G06F19/30 , G06K9/00536 , G06K9/00885 , G16H30/00
摘要： Machine training and application of machine-trained classifier are used for image-based tumor phenotyping in a medical system. To create a training database with known phenotype information, synthetic medical images are created. A computational tumor model creates various examples of tumors in tissue. Using the computational tumor model allows one to create examples not available from actual patients, increasing the number and variance of examples used for machine-learning to predict tumor phenotype. A model of an imaging system generates synthetic images from the examples. The machine-trained classifier is applied to images from actual patients to predict tumor phenotype for that patient based on the knowledge learned from the synthetic images.
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