IP Library › Granted Patent US 11,550,014
Granted Patent B2
US 11,550,014 · App. 17/673,003 · Granted Jan 10, 2023

Artificial intelligence based reconstruction for phase contrast magnetic resonance imaging

Inventors: Daniel B. Ennis (Palo Alto, CA); Matthew J. Middione (St. George, UT); Julio A. Oscanoa Aida (Stanford, CA); Shreyas S. Vasanawala (Stanford, CA)
Assignees: The Board of Trustees of the Leland Stanford Junior University; The United States of America as represented by The Department Of Veterans Affairs
G01R33/5635A61B5/0263A61B5/7267G01R33/5608G06N3/0454
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Quick Facts
Patent No.
US 11,550,014
App. No.
17/673,003
Granted
Jan 10, 2023
Kind
B2
Abstract

A method for phase-contrast magnetic resonance imaging (PC-MRI) acquires undersampled PC-MRI data using a magnetic resonance imaging scanner and reconstructs MRI images from the undersampled PC-MRI data by reconstructing a first flow-encoded image using a first convolutional neural network, reconstructing a complex difference image using a second convolutional neural network, combining the complex difference image and the first flow-encoded image to obtain a second flow-encoded image, and generating a velocity encoded image from the first flow-encoded image and second flow-encoded image using phase difference processing.

Claims (13)

1. A method for phase-contrast magnetic resonance imaging (PC-MRI), the method comprising:

(a) acquiring undersampled PC-MRI data using a magnetic resonance imaging scanner; and

(b) reconstructing MRI images from the undersampled PC-MRI data by

i. reconstructing a first flow-encoded image using a first convolutional neural network;

ii. reconstructing a complex difference image using a second convolutional neural network;

iii. combining the complex difference image and the first flow-encoded image to obtain a second flow-encoded image; and

iv. generating a velocity encoded image from the first flow-encoded image and second flow-encoded image using phase difference processing.

2. The method of claim 1 wherein acquiring the undersampled PC-MRI data using the magnetic resonance imaging scanner comprises acquiring multidimensional (2D or 4D PC-MRI) data.

3. The method of claim 1 wherein the first convolutional neural network is an unrolled convolutional neural network.

4. The method of claim 1 wherein the first convolutional neural network is a DL-ESPIRiT network modified for PC-MRI data.

5. The method of claim 1 wherein the second convolutional neural network is an unrolled convolutional neural network.

6. The method of claim 1 wherein the second convolutional neural network is a DL-ESPIRiT network trained with CD PC-MRI data.

7. The method of claim 1 wherein reconstructing the complex difference image using the second convolutional neural network comprises inputting to the second convolutional neural network a difference of two portions of the undersampled PC-MRI data having different velocity encodings.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 25, 2022
From: ENNIS, DANIEL B.
To: THE UNITED STATES OF AMERICA AS REPRESENTED BY THE DEPARTMENT OF VETERANS AFFAIRS; THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY
Reel/Frame 059400/0830 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 17, 2022
From: MIDDIONE, MATTHEW J.; OSCANOA AIDA, JULIO A.; VASANAWALA, SHREYAS S.
To: THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY
Reel/Frame 059040/0394 →
Continuity (2)
Provisional Application 63149670 · Feb 16, 2021
Related Publication 20220260660A1 · Aug 18, 2022