Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.

ABSTRACT:

Purpose

To develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data.

Methods

The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem.

Res

SUBMITTER: Li Y 

PROVIDER: S-EPMC11604835 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature