Biochemical and Physiological MR Imaging of Skeletal Muscle at 7 Tesla and Above
Bibliographic record
Abstract
Ultra-high field (UHF; >or=7 T) magnetic resonance imaging (MRI), with its greater signal-to-noise ratio, offers the potential for increased spatial resolution, faster scanning, and, above all, improved biochemical and physiological imaging of skeletal muscle. The increased spectral resolution and greater sensitivity to low-gamma nuclei available at UHF should allow techniques such as (1)H MR spectroscopy (MRS), (31)P MRS, and (23)Na MRI to be more easily implemented. Numerous technical challenges exist in the performance of UHF MRI, including changes in relaxation values, increased chemical shift and susceptibility artifact, radiofrequency (RF) coil design/B (1)(+) field inhomogeneity, and greater RF energy deposition. Nevertheless, the possibility of improved functional and metabolic imaging at UHF will likely drive research efforts in the near future to overcome these challenges and allow studies of human skeletal muscle physiology and pathophysiology to be possible at >or=7 T.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".