Accelerating Water Removal in MRSI Using Domain Knowledge-Based Deep Learning
Bibliographic record
Abstract
Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique that provides spatially detailed information about metabolite levels. However, dominant water peaks can interfere with the detection of key metabolite signals. In this work, we propose an unsupervised deep learning model to enhance water peak removal in MRSI. Our approach incorporates domain knowledge into a deep autoencoder model to reconstruct and subtract water peaks, using both Gaussian and Lorentzian line shapes. Experimental results on an in vivo dataset demonstrate that our model performs comparable to the widely used HLSVD-PRO method, while significantly reducing computation time. A comparison of the mean squared errors within the water frequency range (4.0-6.0 ppm) shows a strong agreement between both methods in water removal, with an r2-score of 0.987. Furthermore, our model achieves a slightly higher water suppression ratio (27.35 vs. 20.19) and a lower metabolite reconstruction error (0.28 vs. 0.42). These findings suggest that the proposed model offers improved efficiency in water peak removal. The code for our model is available at https://github.com/Buchali/wr-mrsi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 0.001 |
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".