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公开(公告)号：US20230079353A1
公开(公告)日：2023-03-16
申请号：US17474363
申请日：2021-09-14
摘要： For correction of an image from an imaging system, an inverse solution uses an imaging prior as a regularizer and a physics model of the imaging system. An invertible network is used as the deep-learnt generative model in the regularizer of the inverse solution with the physics model of the degradation behavior of the imaging system. The prior model based on the invertible network provides a closed-form expression of the prior probability, resulting in a more versatile or accurate probability prediction.
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公开(公告)号：US20220215600A1
公开(公告)日：2022-07-07
申请号：US17457948
申请日：2021-12-07
摘要： A computer-implemented method includes, based on an input dataset defining an input image, determining a reconstructed image using a reconstruction algorithm, and executing a data-consistency operation for enforcing consistency between the input image and the reconstructed image. The data-consistency operation determines, for multiple K-space positions at which the input dataset comprises respective source data, a contribution of respective K-space values associated with the input dataset to a K-space representation of the reconstructed image.
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公开(公告)号：US11288806B2
公开(公告)日：2022-03-29
申请号：US15929430
申请日：2020-05-01
摘要： For medical imaging such as MRI, machine training is used to train a network for segmentation using both the imaging data and protocol data (e.g., meta-data). The network is trained to segment based, in part, on the configuration and/or scanner, not just the imaging data, allowing the trained network to adapt to the way each image is acquired. In one embodiment, the network architecture includes one or more blocks that receive both types of data as input and output both types of data, preserving relevant features for adaptation through at least part of the trained network.
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公开(公告)号：US20210150783A1
公开(公告)日：2021-05-20
申请号：US16688170
申请日：2019-11-19
发明人： Simon Arberet , Boris Mailhe , Xiao Chen , Mariappan S. Nadar
摘要： For magnetic resonance imaging reconstruction, using a cost function independent of the ground truth and many samples of k-space measurements, machine learning is used to train a model with unsupervised learning. Due to use of the cost function with the many samples in training, ground truth is not needed. The training results in weights or values for learnable variables, which weights or values are fixed for later application. The machine-learned model is applied to k-space measurements from different patients to output magnetic resonance reconstructions for the different patients. The weights and/or values used are the same for different patients.
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公开(公告)号：US10663549B2
公开(公告)日：2020-05-26
申请号：US14552539
申请日：2014-11-25
发明人： Boris Mailhe , Mariappan S. Nadar , Aurélien Stalder , Qiu Wang , Michael Zenge
IPC分类号： G01R33/56 , G01R33/48 , G01R33/561 , G01R33/483
摘要： A method for acquiring a three-dimensional image volume using a magnetic resonance imaging device includes performing a multi-slice or multi-slab acquisition process to acquire a plurality of slices or three-dimensional slabs corresponding to an imaged object. Each respective slice or three-dimensional slab included in the plurality of slices or three-dimensional slabs comprises k-space data. An iterative compressed-sensing reconstruction process is applied to jointly reconstruct the plurality of three-dimensional slabs as a single consistent volume. The iterative compressed-sensing reconstruction process solves a cost function comprising a summation of individual data fidelity terms corresponding to the plurality of three-dimensional slabs.
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公开(公告)号：US10282588B2
公开(公告)日：2019-05-07
申请号：US15584393
申请日：2017-05-02
发明人： Dorin Comaniciu , Ali Kamen , David Liu , Boris Mailhe , Tommaso Mansi
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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7.发明申请公开(公告)号：US20190128989A1
公开(公告)日：2019-05-02
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 , G06N3/08 , G06N5/04 , G06T7/20
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：US20190046068A1
公开(公告)日：2019-02-14
申请号：US16055546
申请日：2018-08-06
摘要： Systems and methods are provided for generating a protocol independent image. A deep learning generative framework learns to recognize the boundaries and classification of tissues in an MRI image. The deep learning generative framework includes an encoder, a decoder, and a discriminator network. The encoder is trained using the discriminator network to generate a latent space that is invariant to protocol and the decoder is trained to generate the best output possible for brain and/or tissue extraction.
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公开(公告)号：US10043088B2
公开(公告)日：2018-08-07
申请号：US15606069
申请日：2017-05-26
摘要： For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.
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10.发明授权公开(公告)号：US09858689B1
公开(公告)日：2018-01-02
申请号：US15266425
申请日：2016-09-15
发明人： Boris Mailhe , Alexander Ruppel , Qiu Wang , Mariappan S. Nadar
摘要： A computer-implemented method of performing image reconstruction with sequential cycle-spinning includes a computer system acquiring an input signal comprising k-space data using a magnetic resonance imaging (MRI) device and initializing an estimate of a sparse signal associated with the input signal. The computer system selects one or more orthogonal wavelet transforms corresponding to a wavelet family and performs an iterative reconstruction process to update the estimate of the sparse signal over a plurality of iterations. During each iteration, one or more orthogonal wavelet transforms are applied to the estimate of the sparse signal to yield one or more orthogonal domain signals, the estimate of the sparse signal is updated by applying a non-convex shrinkage function to the one or more orthogonal domain signals, and a shift to the orthogonal wavelet transforms. Following the iterative reconstruction process, the computer system generates an image based on the estimate of the sparse signal.
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