Simultaneous <b>pro</b>ton density <b>f</b>at‐fraction and <b>i</b>maging with water‐specific <b>T<sub>1</sub></b> mapping (PROFIT<sub>1</sub>): application in liver
Bibliographic record
Abstract
Purpose To describe and validate a simultaneous proton density fat‐fraction (PDFF) imaging and water‐specific T1 mapping (T1(Water)) approach for the liver (PROFIT1) with mapping and low sensitivity to calibration or inhomogeneity. Methods A multiecho gradient‐echo sequence, with and without saturation preparation, was designed for simultaneous imaging of liver PDFF, , and T1(Water) (three slices in ~13 seconds). Chemical‐shift‐encoded MRI processing yielded fat‐water separated images and maps. T1(Water) calculation utilized saturation and nonsaturation‐recovery water‐separated images. Several variable flip angle schemes across k‐space (increasing flip angles in sequential RF pulses) were evaluated for minimization of T1 weighting, to reduce the dependence of T1(Water) and PDFF (reduced flip angle dependence). T1(Water) accuracy was validated in mixed fat‐water phantoms, with various PDFF and T1 values (3T). In vivo application was illustrated in five volunteers and five patients with nonalcoholic fatty liver disease (PDFF, T1(Water), ). Results A sin3(θ) flip angle pattern (0 < θ < π/2 over k‐space) yielded the largest PROFIT1 signal yield with negligible dependence for both T1(Water) and PDFF. Mixed fat‐water phantom experiments illustrated excellent agreement between PROFIT1 and gold‐standard spectroscopic evaluation of PDFF and T1(Water) (<1% T1 error). In vivo PDFF, T1(Water), and maps illustrated independence of the PROFIT1 values from inhomogeneity and significant differences between volunteers and patients with nonalcoholic fatty liver disease for T1(Water) (927 ± 56 ms vs. 1033 ± 23 ms; P < .05) and PDFF (2.0% ± 0.8% vs. 13.4% ± 5.0%, P < .05). was similar between groups. Conclusion The PROFIT1 pulse sequence provides fast simultaneous quantification of PDFF, T1(Water), and with minimal sensitivity to miscalibration or inhomogeneity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".