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公开(公告)号：US11354833B2
公开(公告)日：2022-06-07
申请号：US16805903
申请日：2020-03-02
发明人： Qiaoying Huang , Xiao Chen , Mariappan S. Nadar , Boris Mailhe , Simon Arberet
摘要： For k-space trajectory infidelity correction, a model is machine trained to correct k-space measurements in k-space. K-space trajectory infidelity correction uses deep learning. Trajectory infidelity is corrected from a k-space point of view. Since the image artifacts arise from k-space acquisition distortion, a machine learning model is trained to correct in k-space, either changing values of k-space measurements or estimating the trajectory shifts in k-space.
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公开(公告)号：US20220156938A1
公开(公告)日：2022-05-19
申请号：US17650311
申请日：2022-02-08
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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93.发明授权公开(公告)号：US11062488B2
公开(公告)日：2021-07-13
申请号：US16199392
申请日：2018-11-26
发明人： Simon Arberet , Boris Mailhe , Xiao Chen , Mariappan S. Nadar
摘要： Systems and methods are provided for iterative reconstruction of a magnetic resonance image using magnetic resonance fingerprinting. An image series is estimated according to the following four steps: a gradient step to improve data consistency, fingerprint matching, spatial regularization, and a merging step. The fingerprint matching and spatial regularization steps are performed in parallel.
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公开(公告)号：US10810767B2
公开(公告)日：2020-10-20
申请号：US16150304
申请日：2018-10-03
摘要： For low-complexity to learned reconstruction and/or learned Fourier transform-based operators for reconstruction, a neural network is used for the transform operators. The network architecture is modeled on the Cooley-Tukey fast Fourier transform (FFT) approach. By splitting input data before recursive calls in the network architecture, the network may be trained to perform the transform with similar complexity as FFT. The learned operators may be used in a trained network for reconstruction, such as with a learned iterative framework and image regularizer.
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公开(公告)号：US20200034654A1
公开(公告)日：2020-01-30
申请号：US16440215
申请日：2019-06-13
发明人： Tommaso Mansi , Boris Mailhe , Rui Liao , Shun Miao
摘要： A correspondence between a source image and a reference image is determined. A generative model corresponds to a prior probability distribution of deformation fields, each deformation field corresponding to a respective co-ordinate transformation. A conditional model generates a style transfer probability distribution of reference images, given a source image and a deformation field. The first image data is the source image, and the second image data is the reference image. An initial first deformation field is determined. An update process is iteratively performed until convergence to update the first deformation field, to generate a converged deformation field representing the correspondence between the source image and the reference image. The update process includes: determining a change in one or more characteristics of the first deformation field to increase a posterior probability density associated with the first deformation field, given the source image and reference image; and changing the one or more characteristics in accordance with the determined change.
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公开(公告)号：US20190320934A1
公开(公告)日：2019-10-24
申请号：US16280349
申请日：2019-02-20
摘要： Automated sequence prediction is provided for a medical imaging session including a self-assessment mechanism. An initial scout sequence is performed of a patient or object. The initial scout sequence is validated. An abbreviated acquisition protocol is performed. The abbreviated acquisition protocol is validated. Additional sequences are performed. The sequences may also be configured based on the analysis of the previous scans using deep learning-based reasoning to select the next appropriate settings and procedures.
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公开(公告)号：US20190287292A1
公开(公告)日：2019-09-19
申请号：US16251242
申请日：2019-01-18
摘要： Systems and methods are provided for generating segmented output from input regardless of the resolution of the input. A single trained network is used to provide segmentation for an input regardless of a resolution of the input. The network is recursively trained to learn over large variations in the input data including variations in resolution. During training, the network refines its prediction iteratively in order to produce a fast and accurate segmentation that is robust across resolution differences that are produced by MR protocol variations.
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公开(公告)号：US10387765B2
公开(公告)日：2019-08-20
申请号：US15596124
申请日：2017-05-16
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：US10360697B2
公开(公告)日：2019-07-23
申请号：US15742544
申请日：2015-09-02
发明人： Boris Mailhe , Johannes Flake , Qiu Wang , Mariappan S. Nadar
IPC分类号： G06T11/00
摘要： A method for performing Computed Tomography (CT) reconstruction includes acquiring a sparse measurement matrix using a CT scanner and applying a reconstruction process over a number of iterations to reconstruct image data from the sparse measurement matrix. The reconstruction process performed during each respective iteration includes generating a random view subset and determining a portion of the sparse measurement matrix corresponding to the random view subset. The reconstruction process further includes performing a stochastic gradient descent on the portion of the sparse measurement matrix to yield an image, applying a proximal total variation regularization to the image, and adjusting a step size associated with the stochastic gradient descent.
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公开(公告)号：US20190172230A1
公开(公告)日：2019-06-06
申请号：US15832967
申请日：2017-12-06
发明人： Boris Mailhe , Benjamin L. Odry , Xiao Chen , Mariappan S. Nadar
摘要： Deep reinforcement machine learning is used to control denoising (e.g., image regularizer) in iterative reconstruction for MRI compressed sensing. Rather than requiring different machine-learnt networks for different scan settings (e.g., acceleration of the MR compressed sensing), reinforcement learning creates a policy of actions to provide denoising and data fitting through iterations of the reconstruction given a range of different scan settings. This allows a user to scan as appropriate for the patient, the MR system, the application, and/or preferences while still providing an optimized reconstruction under sampling resulting from the MR compressed sensing.
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