Evaluating the accuracy of multicomponent <scp>T</scp><sub>2</sub> parameters for luminal water imaging of the prostate with acceleration using inner‐volume 3<scp>D</scp><scp>GRASE</scp>
Bibliographic record
Abstract
Purpose Prostate cancer can be detected using a multicomponent T2 mapping technique termed luminal water imaging. The purpose of this study is twofold: 1) To accelerate the luminal water imaging acquisition by using inner volume selection as part of a gradient and spin echo sequence, and 2) to evaluate the accuracy of luminal water fractions and multicomponent T2 relaxation times. Methods The accuracy of parameter estimates was assessed using Monte Carlo simulations, in phantom experiments and in the prostate (in 5 healthy subjects). Two fitting methods, nonnegative least squares and biexponential fitting with stimulated echo correction, were compared. Results Results demonstrate that inner volume selection in a gradient and spin echo sequence is effective for accelerating prostate luminal water imaging by at least threefold. Evaluation of the accuracy shows that the estimated luminal water fractions are relatively accurate, but the short‐ and long‐T2 relaxation times should be interpreted with caution in noisy scenarios (SNR < 100) and when the corresponding fractions are small ( < 0.5). The mean luminal water fractions obtained at SNR above 100 are 0.27 ± 0.07 for the peripheral zone for both fitting methods, 0.16 ± 0.04 for the transition zone with nonnegative least squares, and 0.16 ± 0.03 for the transition zone with biexponential fitting including stimulated echo correction. Conclusion The shortened scan duration allows the luminal water imaging sequence to be easily integrated into a standard multiparametric prostate MRI protocol.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".