Practical medical applications of quantitative MR relaxometry
Bibliographic record
Abstract
Conventional MR images are qualitative, and their signal intensity is dependent on several complementary contrast mechanisms that are manipulated by the MR hardware and software. In the absence of a quantitative metric for absolute interpretation of pixel signal intensities, one that is independent of scanner hardware and sequences, it is difficult to perform comparisons of MR images across subjects or longitudinally in the same subject. Quantitative relaxometry isolates the contributions of individual MR contrast mechanisms (T1, T2, T2) and provides maps, which are independent of the MR protocol and have a physical interpretation often expressed in absolute units. In addition to providing an unbiased metric for comparing MR scans, quantitative relaxometry uses the relationship between MR maps and physiology to provide a noninvasive surrogate for biopsy and histology. This study provides an overview of some promising clinical applications of quantitative relaxometry, followed by a description of the methods and challenges of acquiring accurate and precise quantitative MR maps. It concludes with three case studies of quantitative relaxometry applied to studying multiple sclerosis, liver iron, and acute myocardial infarction.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".