Terrence Yufeng Chen

US Patent:

20220366535, Nov 17, 2022

Filed:

Apr 28, 2021

Appl. No.:

17/242473

Inventors:

- Shanghai, CN
Zhang Chen - Cambridge MA, US
Xiao Chen - Cambridge MA, US
Shanhui Sun - Cambridge MA, US
Terrence Chen - Cambridge MA, US

Assignee:

Shanghai United Imaging Intelligence Co., LTD. - Shanghai

International Classification:

G06T 3/40
G06T 11/00
G16H 30/40
G06N 3/08

Abstract:

An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.

US Patent:

20220338816, Oct 27, 2022

Filed:

Apr 21, 2021

Appl. No.:

17/236173

Inventors:

- Shanghai, CN
Abhishek Sharma - Cambridge MA, US
Terrence Chen - Cambridge MA, US
Shanhui Sun - Cambridge MA, US

Assignee:

Shanghai United Imaging Intelligence Co., LTD. - Shanghai

International Classification:

A61B 5/00
G06N 3/04
G06N 3/08
G16H 30/40
G16H 50/30

Abstract:

A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.

US Patent:

20230079164, Mar 16, 2023

Filed:

Sep 15, 2021

Appl. No.:

17/475534

Inventors:

- Shanghai, CN
Zhang Chen - Brookline MA, US
Xiao Chen - Lexington MA, US
Terrence Chen - Lexington MA, US
Junshen Xu - Cambridge MA, US

Assignee:

Shanghai United Imaging Intelligence Co., Ltd. - Shanghai

International Classification:

G06T 7/30
G06N 3/02

Abstract:

Deep learning based systems, methods, and instrumentalities are described herein for registering images from a same imaging modality and different imaging modalities. Transformation parameters associated with the image registration task are determined using a neural ordinary differential equation (ODE) network that comprises multiple layers, each configured to determine a respective gradient update for the transformation parameters based on a current state of the transformation parameters received by the layer. The gradient updates determined by the multiple ODE layers are then integrated and applied to initial values of the transformation parameters to obtain final parameters for completing the image registration task. The operations of the ODE network may be facilitated by a feature extraction network pre-trained to determine content features shared by the input images. The input images may be resampled into different scales, which are then processed by the ODE network iteratively to improve the efficiency of the ODE operations.

US Patent:

20230014745, Jan 19, 2023

Filed:

Jul 16, 2021

Appl. No.:

17/378465

Inventors:

- Shanghai, CN
Shanhui Sun - Lexington MA, US
Xiao Chen - Lexington MA, US
Terrence Chen - Lexington MA, US

Assignee:

Shanghai United Imaging Intelligence Co., Ltd. - Shanghai

International Classification:

G06T 11/00
G06T 5/50
G06K 9/62
G06N 3/04
G06N 3/08
A61B 5/055
A61B 5/00

Abstract:

Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on under-sampled MR data. The MR data include 2D or 3D information, and may encompass multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which may accelerate the scan and/or image generation process. Challenges imposed by the large quantity of the MR data and hardware limitations are overcome by separately reconstructing MR images based on respective subsets of contrasts, coils, and/or readout segments, and then combining the reconstructed MR images to obtain desired multi-contrast results.

US Patent:

20210386391, Dec 16, 2021

Filed:

Jun 16, 2020

Appl. No.:

16/902760

Inventors:

- Shangha, CN
Ziyan Wu - Cambridge MA, US
Terrence Chen - Cambridge MA, US

Assignee:

Shanghai United Imaging Intelligence Co., LTD. - Shangha

International Classification:

A61B 6/00
G06N 3/08
G06N 3/04
G06K 9/62

Abstract:

An apparatus is configured to receive input image data corresponding to output image data of a first radiology scanner device, translate the input image data into a format corresponding to output image data of a second radiology scanner device and generate an output image corresponding to the translated input image data on a post processing imaging device associated with the first radiology scanner device. Medical images from a new scanner can be translate to look as if they came from a scanner of another vendor.

US Patent:

20210272014, Sep 2, 2021

Filed:

Feb 28, 2020

Appl. No.:

16/804907

Inventors:

- Shanghai, CN
Ziyan Wu - Cambridge MA, US
Abhishek Sharma - Cambridge MA, US
Arun Innanje - Cambridge MA, US
Terrence Chen - Cambridge MA, US

Assignee:

Shanghai United Imaging Intelligence Co., LTD. - Shanghai

International Classification:

G06N 20/00
H04L 29/06

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.

US Patent:

20210272297, Sep 2, 2021

Filed:

Jan 21, 2021

Appl. No.:

17/154450

Inventors:

- SHANGHAI, CN
Shanhui Sun - Lexington MA, US
Terrence Chen - Lexington MA, US

Assignee:

SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD. - SHANGHAI

International Classification:

G06T 7/32
G06T 7/00
A61B 5/318
A61B 5/055
A61B 5/00

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.

US Patent:

20210224634, Jul 22, 2021

Filed:

Jan 22, 2020

Appl. No.:

16/749964

Inventors:

- Shanghai, CN
Shanhui SUN - Cambridge MA, US
Terrence CHEN - Cambridge MA, US

Assignee:

SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD. - Shanghai

International Classification:

G06N 3/04
G06N 3/08
G01R 33/561
G01R 33/56

Abstract:

A system for image reconstruction in magnetic resonance imaging (MRI) is provided. The system may obtain undersampled k-space data associated with an object, wherein the undersampled K-space data may be generated based on magnetic resonance (MR) signals collected by an MR scanner that scans the object. The system may construct an ordinary differential equation (ODE) that formulates a reconstruction of an MR image based on the undersampled k-space data. The system may further generate the MR image of the object by solving the ODE based on the undersampled k-space data using an ODE solver.