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公开(公告)号：US20240296552A1
公开(公告)日：2024-09-05
申请号：US18117068
申请日：2023-03-03
Inventor： Xiao Chen , Shanhui Sun , Zhang Chen , Yikang Liu , Arun Innanje , Terrence Chen
CPC classification number: G06T7/0012 , G06T7/10 , G16H30/40 , G06T2207/30048
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with cardiac motion tracking and/or analysis. In accordance with embodiments of the disclosure, the motion of a heart such as an anatomical component of the heart may be tracked through multiple medical images and a contour of the anatomical component may be outlined in the medical images and presented to a user. The user may adjust the contour in one or more of the medical images and the adjustment may trigger modifications of motion field(s) associated with the one or more medical images, re-tracking of the contour in the one or more medical images, and/or re-determination of a physiological characteristic (e.g., a myocardial strain) of the heart. The adjustment may be made selectively, for example, to a specific medical image or one or more additional medical images selected by the user, without triggering a modification of all of the medical images.
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公开(公告)号：US12073539B2
公开(公告)日：2024-08-27
申请号：US17564348
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T5/70 , G06N3/045 , G06N3/08 , G06T5/50 , G16H30/40 , G06T2207/10064 , G06T2207/20081 , G06T2207/20084
Abstract: Described herein are systems, methods, and instrumentalities associated with denoising medical images such as fluoroscopic images using deep learning techniques. A first artificial neural network (ANN) is trained to denoise an input medical image in accordance with a provided target noise level. The training of the first ANN is conducted by pairing a noisy input image with target denoised images that include different levels of noise. These target denoised images are generated using a second ANN as intermediate outputs of the second ANN during different training iterations. As such, the first ANN may learn to perform the denoising task in an unsupervised manner without requiring noise-free training images as the ground truth.
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公开(公告)号：US12013452B2
公开(公告)日：2024-06-18
申请号：US17741307
申请日：2022-05-10
Inventor： Xiao Chen , Zhang Chen , Yikang Liu , Shanhui Sun , Terrence Chen , Lin Zhao
IPC: G01R33/56 , G01R33/561 , G06N3/045 , G06N3/08
CPC classification number: G01R33/5608 , G01R33/5611 , G06N3/045 , G06N3/08
Abstract: Described herein are systems, methods, and instrumentalities associated with reconstruction of multi-contrast magnetic resonance imaging (MRI) images. The reconstruction may be performed based on under-sampled MRI data collected for the multiple contrasts using corresponding sampling patterns. The sampling patterns and the reconstruction operations for the multiple contrasts may be jointly optimized using deep learning techniques implemented through one or more neural networks. An end-to-end reconstruction optimizing framework is provided with which information collected while processing one contrast may be stored and used for another contrast. A differentiable sampler is described for obtaining the under-sampled MRI data from a k-space and a novel holistic recurrent neural network is used to reconstruct MRI images based on the under-sampled MRI data.
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公开(公告)号：US20240104721A1
公开(公告)日：2024-03-28
申请号：US17953484
申请日：2022-09-27
Inventor： Arun Innanje , Xiao Chen , Shanhui Sun , Zhanhong Wei , Terrence Chen
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T2207/10088 , G06T2207/30048
Abstract: An anatomy-aware contouring editing method includes receiving an image, wherein the image represents an anatomically recognizable structure; identifying a first image segment representing part of the anatomically recognizable structure; annotating the first image segment to generate a label of the part; drawing a contour along a boundary of the part; receiving a first input from a user device indicative of a region of contour failure, wherein the region of contour failure includes a portion of a contour that requires editing; editing the contour for generating an edited contour based on the first input and anatomical information; and updating another contour of another part of the anatomically recognizable structure based on the edited contour, wherein the another part is anatomically related to the part.
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公开(公告)号：US20240062438A1
公开(公告)日：2024-02-22
申请号：US17891668
申请日：2022-08-19
Inventor： Zhang Chen , Siyuan Dong , Shanhui Sun , Xiao Chen , Yikang Liu , Terrence Chen
CPC classification number: G06T11/008 , G06T5/20 , G06T5/003 , G06T5/002 , G06T5/10 , G06T7/0014 , G06T2207/20084 , G06T2207/10088 , G06T2207/20081 , G06T2207/30008
Abstract: Described herein are systems, methods, and instrumentalities associated with using an invertible neural network to complete various medical imaging tasks. Unlike traditional neural networks that may learn to map input data (e.g., a blurry reconstructed MRI image) to ground truth (e.g., a fully-sampled MRI image), the invertible neural network may be trained to learn a mapping from the ground truth to the input data, and may subsequently apply an inverse of the mapping (e.g., at an inference time) to complete a medical imaging task. The medical imaging task may include, for example, MRI image reconstruction (e.g., to increase the sharpness of a reconstructed MRI image), image denoising, image super-resolution, and/or the like.
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Patent Agency Ranking
公开(公告)号：US20230366964A1
公开(公告)日：2023-11-16
申请号：US17741307
申请日：2022-05-10
Inventor： Xiao Chen , Zhang Chen , Yikang Liu , Shanhui Sun , Terrence Chen , Lin Zhao
IPC: G01R33/56 , G01R33/561
CPC classification number: G01R33/5608 , G01R33/5611 , G06N3/0454
Abstract: Described herein are systems, methods, and instrumentalities associated with reconstruction of multi-contrast magnetic resonance imaging (MRI) images. The reconstruction may be performed based on under-sampled MRI data collected for the multiple contrasts using corresponding sampling patterns. The sampling patterns and the reconstruction operations for the multiple contrasts may be jointly optimized using deep learning techniques implemented through one or more neural networks. An end-to-end reconstruction optimizing framework is provided with which information collected while processing one contrast may be stored and used for another contrast. A differentiable sampler is described for obtaining the under-sampled MRI data from a k-space and a novel holistic recurrent neural network is used to reconstruct MRI images based on the under-sampled MRI data.
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公开(公告)号：US20230342916A1
公开(公告)日：2023-10-26
申请号：US17726383
申请日：2022-04-21
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T7/0012 , G06T5/007 , G06T7/10 , G06T2207/10116 , G06T2207/20084
Abstract: Described herein are systems, methods, and instrumentalities associated with medical image enhancement. The medical image may include an object of interest and the techniques disclosed herein may be used to identify the object and enhance a contrast between the object and its surrounding area by adjusting at least the pixels associated with the object. The object identification may be performed using an image filter, a segmentation mask, and/or a deep neural network trained to separate the medical image into multiple layers that respectively include the object of interest and the surrounding area. Once identified, the pixels of the object may be manipulated in various ways to increase the visibility of the object. These may include, for example, adding a constant value to the pixels of the object, applying a sharpening filter to those pixels, increasing the weight of those pixels, and/or smoothing the edge areas surrounding the object of interest.
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公开(公告)号：US20230214964A1
公开(公告)日：2023-07-06
申请号：US17565714
申请日：2021-12-30
Inventor： Shanhui Sun , Li Chen , Yikang Liu , Xiao Chen , Zhang Chen
CPC classification number: G06T5/002 , G06T5/10 , G06T2207/30052 , G06T2207/20048 , G06T2207/20084 , G06T2207/10121
Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.
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公开(公告)号：US11663727B2
公开(公告)日：2023-05-30
申请号：US17154450
申请日：2021-01-21
Inventor： Xiao Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T7/32 , A61B5/0035 , A61B5/0044 , A61B5/055 , A61B5/318 , A61B5/7267 , G06T7/0012 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.
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公开(公告)号：US20230160986A1
公开(公告)日：2023-05-25
申请号：US17533276
申请日：2021-11-23
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
CPC classification number: G01R33/5608 , G06N3/08 , G06N3/0454
Abstract: In Multiplex MRI image reconstruction, a hardware processor acquires sub-sampled Multiplex MRI data and reconstructs parametric images from the sub-sampled Multiplex MRI data. A machine learning model or deep learning model uses the subsampled Multiplex MRI data as the input and parametric maps calculated from the fully sampled data, or reconstructed fully sample data, as the ground truth. The model learns to reconstruct the parametric maps directly from the subsampled Multiplex MRI data.
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