Measurement and correction of the bulk magnetic susceptibility effects of fat: application in venous oxygen saturation imaging
Bibliographic record
Abstract
Purpose To develop a correction method for the effects of the magnetic susceptibility of fat (χFat) on the calculation of venous oxygen saturation (SvO2). Theory The magnetic field shifts associated with the magnetic susceptibility of deoxyhemoglobin can be used to estimate SvO2, a measure of oxygen extraction and metabolism. However, the distinct magnetic susceptibility of fat surrounding targeted veins will give rise to magnetic field perturbations that will extend into the vein and surrounding tissues, potentially confounding the calculation of SvO2. Methods Multi‐echo modified Dixon fat–water separated imaging was used to quantify fat–water distributions around the superficial femoral vein (venous return from the lower leg). Fat fraction images were used to generate χFat images, to calculate and remove the associated fat‐susceptibility‐induced magnetic field shifts before the estimation of SvO2. This approach was evaluated at rest and with plantar flexion exercise to evaluate calf muscle oxygen extraction in 10 healthy subjects. Results The presence of fat around the vein resulted in complex magnetic field shifts and errors in estimated SvO2. Corrected resting SvO2 values were significantly larger than those measured with conventional methods, at rest (72.6 ± 11.0% vs. 65.2 ± 12.2%, P < 0.05) and post‐exercise (37.4 ± 12.3% vs. 31.7 ± 12.7%, P < 0.05), with larger errors in individuals and/or regions with increased fat volumes. Estimation and removal of the field‐effects from χFat enabled the use of fat tissues for the measurement and removal of the background magnetic field. Conclusions The magnetic susceptibility effects of fat can confound SvO2 estimation, but the susceptibility field effects can estimated and removed with the use of modified Dixon fat–water separated imaging.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".