Dynamic <sup>31</sup>P spectroscopic imaging of skeletal muscles combining flyback echo‐planar spectroscopic imaging and compressed sensing
Bibliographic record
Abstract
PURPOSE: P-MRSI) experiments require temporal resolution on the order of seconds to concurrently assess different muscle groups. A highly accelerated pulse sequence combining flyback echo-planar spectroscopic imaging (EPSI) and compressed sensing was developed and tested in a phantom and healthy humans during an exercise-recovery challenge of the lower leg muscles, using a clinical 3T MRI. METHODS: field of view (i.e., 8 × 8 matrix) was combined with compressed sensing through the inclusion of pseudorandom gradient blips to sub-sample the ky-kt dimensions by a factor of 2.7×, achieving a temporal resolution of 9 s. The sequence was first tested in a phantom to assess performance compared to fully sampled EPSI (fidEPSI) and phase encoded chemical shift imaging (fidCSI). Then, tests were performed in 11 healthy volunteers during an exercise-recovery challenge of the lower leg muscles. Voxels containing tissue from different muscle groups were evaluated measuring percentage phosphocreatine (%PCr) depletion, time constant of PCr recovery (τPCr) and intracellular pH at rest and following exercise. RESULTS: The sequence was capable to track the dynamic PCr response of multiple muscles simultaneously. No statistical differences were found in the metabolite ratio, pH or linewidth when compared with fidEPSI and fidCSI in the phantom study. Dynamic experiments showed differences in PCr depletion when comparing soleus with gastrocnemius muscles. Intracellular pH, τPCr and %PCr decrease were consistent with reported values. CONCLUSION: P-MRSI combining flyback EPSI and compressed sensing is capable of assessing concurrent energy metabolism in multiple muscle groups using a clinical 3T MR system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".