公开(公告)号：US20230184860A1

公开(公告)日：2023-06-15

申请号：US17550667

申请日：2021-12-14

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

Inventor： Xiao Chen ,  Lin Zhao ,  Zhang Chen ,  Yikang Liu ,  Shanhui Sun ,  Terrence Chen

IPC: G01R33/56 ,  G06T7/00

CPC classification number: G01R33/5601 ,  G01R33/5608 ,  G06T7/0012 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Described herein are systems, methods, and instrumentalities associated with generating multi-contrast MRI images associated with an MRI study. The systems, methods, and instrumentalities utilize an artificial neural network (ANN) trained to jointly determine MRI data sampling patterns for the multiple contrasts based on predetermined quality criteria associated with the MRI study and reconstruct MRI images with the multiple contrasts based on under-sampled MRI data acquired using the sampling patterns. The training of the ANN may be conducted with an objective to improve the quality of the whole MRI study rather than individual contrasts. As such, the ANN may learn to allocate resources among the multiple contrasts in a manner that optimizes the performance of the whole MRI study.

公开(公告)号：US20250025054A1

公开(公告)日：2025-01-23

申请号：US18223414

申请日：2023-07-18

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

Inventor： Yikang Liu ,  Dehong Fang ,  Lin Zhao ,  Zhang Chen ,  Xiao Chen ,  Shanhui Sun ,  Terrence Chen

IPC: A61B5/02 ,  G16H30/20

Abstract: Described herein are systems, methods, and instrumentalities associated with automatic determination of hemodynamic characteristics. An apparatus as described may implement a first artificial neural network (ANN) and a second ANN. The first ANN may model a mapping from a set of 3D points associated with one or more blood vessels to a set of hemodynamic characteristics of the one or more blood vessels, while the second ANN may generate, based on a geometric relationship of the set of points in a 3D space, parameters for controlling the mapping. The apparatus may obtain a 3D anatomical model representing at least one blood vessel of a patient based on one or more medical images of the patient, and determine, based on the first ANN and the second ANN, a hemodynamic characteristic of the at least one blood vessel of the patient at a target location of the 3D anatomical model.

公开(公告)号：US12013452B2

公开(公告)日：2024-06-18

申请号：US17741307

申请日：2022-05-10

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

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.

公开(公告)号：US20230366964A1

公开(公告)日：2023-11-16

申请号：US17741307

申请日：2022-05-10

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

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.

公开(公告)号：US20250029720A1

公开(公告)日：2025-01-23

申请号：US18225009

申请日：2023-07-21

Applicant: Shanghai United Imaging Intelligence Co., Ltd.

Inventor： Shanhui Sun ,  Zhang Chen ,  Xiao Chen ,  Yikang Liu ,  Lin Zhao ,  Terrence Chen ,  Arun Innanje ,  Abhishek Sharma ,  Wenzhe Cui ,  Xiao Fan

IPC: G16H50/20 ,  G16H50/70

Abstract: Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.