Data Processing in Multidimensional MRI For Biomarker Identification: Is It Necessary?
Bibliographic record
Abstract
Multidimensional MRI (MD-MRI) is an emerging technique that holds promise for identifying tissue characteristics that could be indicative of pathologies. Before these characteristics can be interpreted, MD-MRI measurements are converted into an spectrum. These spectra are then utilized to obtain some understanding of the underlying tissue microstructure, often through the use of statistical, machine learning, and mathematical modeling methods. The aim of this study was to compare outcomes of using unprocessed MDMRI signals for statistical regression in comparison to the corresponding spectra. Backed by a theoretical argument, we described an experimental procedure regressing both MDMRI signals and spectra to histological outcomes intrasubject. Through using multiple conventional ML methods, and a proposed method using convex sets, we aimed to see which yielded the highest accuracy. Both theory and experimental evidence suggest that, without a priori information, statistical regression was best performed on the MDMRI signal. We conclude, barring any a priori information regarding tissue changes, there is no significant advantage to performing regression analysis on reconstructed spectra in the process of biomarker identification.
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 | 0.032 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".