Generalizable, sequence-invariant deep learning image reconstruction for subspace-constrained quantitative MRI.

ABSTRACT:

Purpose

To develop a deep subspace learning network that can function across different pulse sequences.

Methods

A contrast-invariant component-by-component (CBC) network structure was developed and compared against previously reported spatiotemporal multicomponent (MC) structure for reconstructing MR Multitasking images. A total of 130, 167, and 16 subjects were imaged using T₁, T₁-T₂, and T₁-T₂- T2*$$ {\mathrm{T}}_2^{\ast } $$ -fat fraction (FF) mapping sequences, respectively. We compared CBC and MC networks in matched-sequence experiments (same sequence for training and testing), then examined their cross-sequence performance and generalizability by unmatched-sequence experiments (different sequences for training and

SUBMITTER: Hu Z 

PROVIDER: S-EPMC12021335 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature