Evaluation of adipose tissue volume quantification with IDEAL fat–water separation
Bibliographic record
Abstract
PURPOSE: To validate iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) for adipose tissue volume quantification. IDEAL allows MRI images to be produced only from adipose-containing tissues; hence, quantifying adipose tissue should be simpler and more accurate than with current methods. MATERIALS AND METHODS: Ten healthy controls were imaged with 1.5 Tesla (T) Spin Echo (SE), 3.0T T1-weighted spoiled gradient echo (SPGR), and 3.0T IDEAL-SPGR. Images were acquired from the abdomen, pelvis, mid-thigh, and mid-calf. Mean subcutaneous and visceral adipose tissue volumes were compared between the three acquisitions for each subject. RESULTS: There were no significant differences (P>0.05) between the three acquisitions for subcutaneous adipose tissue volumes. However, there was a significant difference (P=0.0002) for visceral adipose tissue volumes in the abdomen. Post hoc analysis showed significantly lower visceral adipose tissue volumes measured by IDEAL versus 1.5T (P<0.0001) and 3.0T SPGR (P<0.002). The lower volumes given by IDEAL are due to its ability to differentiate true visceral adipose tissue from other bright structures like blood vessels and bowel content that are mistaken for adipose tissue in non-fat suppressed images. CONCLUSION: IDEAL measurements of adipose tissue are equivalent to established 1.5T measurement techniques for subcutaneous depots and have improved accuracy for visceral depots, which are more metabolically relevant.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".