ArticleMagnetic resonance in medicine2025
Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.
Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.Magnetic resonance in medicine · 2025Article
- Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI.IEEE transactions on bio-medical engineering · 2024Article
- High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions.IEEE signal processing magazine · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data.
methodsThe 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.
resultsThe proposed method has been evaluated using simulations as well as in vivo
conclusionA novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.
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