PHIVE: A Physics-Informed Variational Encoder Enables Rapid Spectral Fitting of Brain Metabolite Mapping at 7T
Bibliographic record
Abstract
ABSTRACT Magnetic Resonance Spectroscopic Imaging (MRSI) enables non-invasive mapping of brain metabolite concentrations but remains computationally intensive and challenging due to a low signal-to-noise ratio (SNR) and overlapping spectral features. Traditional spectral fitting methods, such as LCModel, are time-consuming and often lack comprehensive uncertainty quantification. In this study, we propose Physics-Informed Variational Encoder (PHIVE), a novel deep learning framework that integrates physics-based priors into a variational autoencoder architecture for rapid and accurate metabo-lite quantification. PHIVE enables simultaneous estimation of metabolite concentrations and uncertainty metrics, including Cramér-Rao Lower Bound (CRLB), aleatoric, and epistemic uncertainties. PHIVE was evaluated on whole-brain MRSI data from 7T acquisitions of healthy controls. The method achieved comparable accuracy to LCModel for key metabolites, such as Total N-acetylaspartate (tNAA), Glutamate-Glutamine complex (Glx), and Myo-inositol (mIns) while demonstrating a six-order magnitude reduction in computational time (6 ms per dataset). Uncertainty quantification highlighted PHIVE’s robustness in regions with low SNR. Additionally, a conditional baseline modeling approach was introduced, enabling dynamic flexibility in spectral baseline estimation during inference time. These results suggest that PHIVE offers a fast, reliable, and interpretable solution for high-resolution metabolite quantification, paving the way for real-time MRSI applications in clinical and research settings. Future work will focus on expanding its validation across diverse datasets and investigating its utility in longitudinal and multicenter studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".