Feasibility and reproducibility of measurement of whole muscle blood flow, oxygen extraction, and VO<sub>2</sub> with dynamic exercise using MRI
Bibliographic record
Abstract
PURPOSE: Develop an MRI method to estimate skeletal muscle oxygen consumption (VO2 ) with dynamic exercise using simultaneous measurement of venous blood flow (VBF) and venous oxygen saturation (SvO2 ). METHODS: Real-time imaging of femoral VBF using a complex-difference method was interleaved with imaging of venous hemoglobin oxygen saturation (SvO2 ) using magnetic susceptometry to estimate muscle VO2 (Fick principle). Nine healthy subjects performed repeated 5-watt knee-extension (quadriceps) exercise within the bore of a 1.5 Tesla MRI scanner, for test/re-test comparison. VBF, SvO2 , and derived VO2 were estimated at baseline and immediately (<1 s) postexercise and every 2.4 s for 4 min. RESULTS: Quadriceps muscle mass was 2.43 ± 0.31 kg. Mean baseline values were VBF = 0.13 ± 0.06 L/min/kg, SvO2 = 69.4 ± 10.1%, and VO2 = 6.8 ± 4.1 mL/min/kg. VBF, SvO2 , and VO2 values from peak exercise had good agreement between trials (VBF = 0.9 ± 0.1 versus 1.0 ± 0.1 L/min/kg, R(2) = 0.83, CV = 7.6%; SvO2 = 43.2 ± 13.5 versus 40.9 ± 13.1%, R(2) = 0.88, CV = 15.6%; VO2 = 95.7 ± 18.0 versus 108.9 ± 17.3 mL/min/kg, R(2) = 0.88, CV = 12.3%), as did the VO2 recovery time constant (26.1 ± 3.5 versus 26.0 ± 4.0 s, R(2) = 0.85, CV = 6.0%). CV = coefficient of variation. CONCLUSION: Rapid imaging of VBF and SvO2 for the estimation of whole muscle VO2 is compatible with dynamic exercise for the estimation of peak values and recovery dynamics following exercise with good reproducibility.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".