Learned Tensor Low-CP-Rank and Bloch response manifold priors for Non-Cartesian MRF Reconstruction

Date

2023-02-13

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

ISMRM & ISMRT

Type

Conference

Peer reviewed

Yes

Abstract

We propose a deep unrolled network for non-Cartesian MRF reconstruction by unrolling the MRF reconstruction model regularized by the tensor low-rank and the Bloch resonance manifold priors. To avoid computationally burdensome singular value decomposition, we propose a learned CP decomposition module to exploit the tensor low-rank priors of MRF data. Inspired by the MRF imaging mechanism, we also propose a Bloch response manifold module to learn the mapping between reconstructed MRF data and the multiple parameter maps. Numerical experiments show that the proposed network can improve the reconstruction quality of MRF data and multi-parameter maps within significantly reduced computational time.

Description

Keywords

Image Reconstruction, MR Fingerprinting, Tensor Low-rank, CP Decomposition, Bloch Response Manifold, non-Cartesian

Citation

Li, P., Li, X., Lu, X. and Hu, Y. (2023) Learned Tensor Low-CP-Rank and Bloch response manifold priors for Non-Cartesian MRF Reconstruction. 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, Canada, 03-08 June

Rights

Research Institute

Institute of Artificial Intelligence (IAI)