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公开(公告)号：US12045695B2
公开(公告)日：2024-07-23
申请号：US16804907
申请日：2020-02-28
Inventor： Srikrishna Karanam , Ziyan Wu , Abhishek Sharma , Arun Innanje , Terrence Chen
Abstract: Data samples are transmitted from a central server to at least one local server apparatus. The central server receives a set of predictions from the at least one local server apparatus that are based on the transmitted set of data samples. The central server trains a central model based on the received set of predictions. The central model, or a portion of the central model corresponding to a task of interest, can then be sent to the at least one local server apparatus. Neither local data from local sites nor trained models from the local sites are transmitted to the central server. This ensures protection and security of data at the local sites.
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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：US20240135737A1
公开(公告)日：2024-04-25
申请号：US18128290
申请日：2023-03-29
Inventor： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC classification number: G06V20/70 , G06V10/235
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.
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公开(公告)号：US20240135684A1
公开(公告)日：2024-04-25
申请号：US17969876
申请日：2022-10-19
Inventor： Meng Zheng , Srikrishna Karanam , Ziyan Wu , Arun Innanje , Terrence Chen
IPC: G06V10/774 , G06T7/00
CPC classification number: G06V10/774 , G06T7/0012 , G06T2207/20081 , G06T2207/20108
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D annotation provided by an annotator and by propagating the 2D annotation through multiple images of a sequence of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets based on similarities between the first 3D image dataset and the other 3D image datasets. The automatic annotation of the first 3D image dataset and/or the other 3D image datasets may be conducted based on one or more machine-learning models trained for performing those tasks.
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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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Patent Agency Ranking
106.发明公开公开(公告)号：US20240077562A1
公开(公告)日：2024-03-07
申请号：US17939251
申请日：2022-09-07
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Terrence Chen
CPC classification number: G01R33/5608 , A61B5/055 , A61N5/1049 , A61N2005/1055
Abstract: A power management apparatus for a workflow to enable low power MR patient positioning on edge devices is disclosed. The power management apparatus changes an operational mode of an edge device from a first power mode to a second power mode after a defined time-interval. The power management apparatus further controls the edge device to capture a first image of a first scene. The power management apparatus further determines a trigger point based on a detection of a plurality of objects in the captured first image. The power management apparatus further changes the operational mode of the edge device from the second power mode to a third power mode to control a consumption of electric power while a set of operations is executed at the edge device. The operational mode of the edge device may be changed at the determined trigger point.
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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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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：US11710244B2
公开(公告)日：2023-07-25
申请号：US16673817
申请日：2019-11-04
Inventor： Shanhui Sun , Zhang Chen , Terrence Chen , Ziyan Wu
IPC: G06N3/08 , G06T7/246 , A61B5/11 , A61B5/107 , G06T7/20 , G06T7/62 , G06T7/215 , G06T7/00 , G06T11/00 , G06F18/2132 , G06F18/214 , G06F18/21 , G06V10/25 , G06V10/764 , G06V10/774 , A61B90/00
CPC classification number: G06T7/248 , A61B5/1076 , A61B5/1107 , A61B5/1128 , G06F18/2132 , G06F18/2155 , G06F18/2178 , G06N3/08 , G06T7/0016 , G06T7/20 , G06T7/215 , G06T7/251 , G06T7/62 , G06T11/005 , G06V10/25 , G06V10/764 , G06V10/7753 , A61B2090/061 , G06T2207/10088 , G06T2207/20081 , G06T2207/30048 , G06T2207/30061
Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.
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