ArticleMagnetic resonance in medicine2023
Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Deep Nonlinear Subspace Modeling and Reconstruction for Diffusion-Weighted Imaging Using Denoising Auto-Encoder.Magnetic resonance in medicine · 2026Article
- Uncovering Heart Rate Response Patterns to Threat Pictures Through Deep Latent Representation Learning with a Variational Autoencoder.Psychophysiology · 2026Article
- Ultra-high temporal resolution 4D angiography using arterial spin labeling with subspace reconstruction.Magnetic resonance in medicine · 2025Article
- Single-shot echo planar time-resolved imaging for multi-echo functional MRI and distortion-free diffusion imaging.Magnetic resonance in medicine · 2025Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
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Authors and funding
8 authors.
Funding
Abstract
purposeTo improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model.
methodsBuilding on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T
resultsAn auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T
conclusionDirectly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.
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Registered trials
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