Practical medical applications of quantitative MR relaxometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it