The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 22, 2025

Filed:

Feb. 03, 2023
Applicant:

The Hong Kong Polytechnic University, Hong Kong, CN;

Inventors:

Jing Cai, Hong Kong, CN;

Haonan Xiao, Hong Kong, CN;

Tian Li, Hong Kong, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/055 (2006.01); G01R 33/48 (2006.01); G01R 33/56 (2006.01); G01R 33/565 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
A61B 5/055 (2013.01); G01R 33/4826 (2013.01); G01R 33/5608 (2013.01); G01R 33/56509 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01);
Abstract

A computer-implemented method for training a convolutional neural network (CNN) using training data comprising a pair of original and downsampled 4D magnetic resonance imaging (MRI) data is provided. The CNN is used to generate multi-parametric 4D magnetic resonance (MR) images based on multi-parametric 3D MR images in real-time. The method includes receiving a 4D MR image formed by a plurality of fixed images of different frames; converting the plurality of fixed images into a plurality of k-space data by non-uniform fast Fourier transform (NUFFT); applying radial scan to the k-space data to simulate real-time MR image acquisition, and generating a plurality of downsampled fixed images by inverse NUFFT; training a CNN with training data comprising the 4D MR image and the corresponding downsampled 4D MR image; and estimating the multi-parametric 4D MR image in real-time by applying apply the predicted DVF to the multi-parametric 3D MR images.


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