Unsupervised Learning-based Pulse Sequence Optimization framework for Magnetic Resonance Fingerprinting

Date

2023-02-13

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

ISMRM & ISMRT

Type

Conference

Peer reviewed

Yes

Abstract

The optimal design of the Magnetic resonance fingerprinting (MRF) sequence is still challenging due to the optimization of high-degrees-of-freedom acquisition parameters. In this paper, we propose a novel unsupervised learning-based pulse sequence design framework for efficient MRF sequence optimization. Specifically, we propose a novel pulse sequence generation network (PSG-Net) that fully exploits the sequence correlation to generate the optimal pulse sequence from a zero-initialized input. To achieve improved precision of parameter estimation, we use a predefined pulse sequence performance evaluation function that can directly represent tissue quantification separability as the loss function to update the parameters of the PSG-Net.

Description

Keywords

Pulse Sequence Design, MR Fingerprinting

Citation

Li, P., Zhang, Y., Lu, X. and Hu, Y. (2023) Unsupervised Learning-based Pulse Sequence Optimization framework for Magnetic Resonance Fingerprinting. 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, Canada, 03-08 June

Rights

Research Institute

Institute of Artificial Intelligence (IAI)