-
公开(公告)号：US11748921B2
公开(公告)日：2023-09-05
申请号：US17097060
申请日：2020-11-13
CPC分类号： G06T11/003 , G06N20/00 , G06T7/0012 , G06T2207/20081 , G06T2207/20084
摘要： For reconstruction in medical imaging, such as reconstruction in MR imaging, the number of iterations in deep learning-based reconstruction may be reduced by including a learnable extrapolation in one or more iterations. Regularization may be provided in fewer than all of the iterations of the reconstruction. The result of either approach alone or both together is better quality reconstruction and/or less computationally expensive reconstruction.
-
公开(公告)号：US20230084413A1
公开(公告)日：2023-03-16
申请号：US17473229
申请日：2021-09-13
摘要： For reconstruction, a machine-learned model is adapted to allow for reconstruction based on the repetitions available in some scanning. The reconstruction for one or more subsets is performed during the scanning. The machine-learned model is trained to reconstruction separately or independently for each repetition or to use information from previous repetitions without requiring waiting for completion of scanning. The reconstructed image may be displayed much more rapidly after completion of the acquisition since the reconstruction begins during the reconstruction.
-
公开(公告)号：US20220334204A1
公开(公告)日：2022-10-20
申请号：US17699619
申请日：2022-03-21
发明人： Birgi Tamersoy , Boris Mailhe , Vivek Singh , Ankur Kapoor , Mariappan S. Nadar
IPC分类号： G01R33/3875 , G01R33/56 , G01R33/44 , G06N20/00
摘要： Object specific in-homogeneities in an MRI system are corrected. Prescan information available at the MR imaging system is determined. The prescan information includes at least object specific information of an object located in the MR imaging system from which an MR image is to be generated. The prescan information does not include a B1 map of the MRI system with the object being present in the MR imaging system. The prescan information is applied to a trained machine learning module provided at the MRI system. The trained machine learning module determines and generates shimming information as output. The shimming information is applied to a shimming module of the MR imaging system, wherein the shimming module uses the shimming information to generate a corrected magnetic field B0.
-
公开(公告)号：US11422217B2
公开(公告)日：2022-08-23
申请号：US16897391
申请日：2020-06-10
摘要： For reconstruction in medical imaging, such as reconstruction in MR imaging, a high-resolution image is reconstructed using a generator of a progressive generative adversarial network (PGAN or progressive GAN). In machine training the network, both the generator and discriminator of the GAN are grown progressively: starting from a low resolution, new layers are added that model finer details as training progresses. The resulting generator may be better able to handle high-resolution information than a generator of a GAN.
-
公开(公告)号：US20220252683A1
公开(公告)日：2022-08-11
申请号：US17577148
申请日：2022-01-17
IPC分类号： G01R33/24 , G01R33/56 , G01R33/561
摘要： A system is provided for MRI coil sensitivity estimation and reconstruction At least two cascades of regularization networks are serially connected such that the output of a cascade is used as input of a following cascade, at least two deepsets coil sensitivity map networks are serially connected such that the output of a deepsets coil sensitivity map network is used as input of a following deepsets coil sensitivity map network (CR), and wherein the outputs of the deepsets coil sensitivity map networks are also used as inputs for the cascades.
-
公开(公告)号：US11346911B2
公开(公告)日：2022-05-31
申请号：US16238554
申请日：2019-01-03
发明人： Guillaume Daval Frerot , Xiao Chen , Mariappan S. Nadar , Peter Speier , Mathias Nittka , Boris Mailhe , Simon Arberet
摘要： Machine training a network for and use of the machine-trained network are provided for tissue parameter estimation for a magnetic scanner using magnetic resonance fingerprinting. The machine-trained network is trained to both reconstruct a fingerprint image or fingerprint and to estimate values for multiple tissue parameters in magnetic resonance fingerprinting. The reconstruction of the fingerprint image or fingerprint may reduce noise, such as aliasing, allowing for more accurate estimation of the values of the multiple tissue parameters from the under sampled magnetic resonance fingerprinting information.
-
公开(公告)号：US20210272335A1
公开(公告)日：2021-09-02
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.
-
8.发明授权公开(公告)号：US11035919B2
公开(公告)日：2021-06-15
申请号：US16532917
申请日：2019-08-06
摘要： Magnetic resonance compressed sensing data may be acquired and reconstructed into an image. Noise-like aliasing present in the compressed sensing data may be modeled. The reconstruction may include denoising the compressed sensing data based on a noise level of the compressed sensing data, a sampling density of the compressed sensing data, and the model of the noise-like aliasing. A result of the denoising is denoised image data. The reconstruction may further include generating updated image data based on the compressed sensing data and the denoised image data. An output image based on the updated image may be output.
-
公开(公告)号：US10991092B2
公开(公告)日：2021-04-27
申请号：US16214339
申请日：2018-12-10
发明人： Sandro Braun , Boris Mailhe , Xiao Chen , Benjamin L. Odry , Mariappan S. Nadar
摘要： For classifying magnetic resonance image quality or training to classify magnetic resonance image quality, deep learning is used to learn features distinguishing between corrupt images base on simulation and measured similarity. The deep learning uses synthetic data without quality annotation, allowing a large set of training data. The deep-learned features are then used as input features for training a classifier using training data annotated with ground truth quality. A smaller training data set may be needed to train the classifier due to the use of features learned without the quality annotation.
-
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.
-
-
-
-
-
-
-
-
-
搜索结果
111 条记录 (耗时 0.025 秒)
