Prostate T<sub>1</sub> quantification using a magnetization‐prepared spiral technique
Bibliographic record
Abstract
PURPOSE: To adapt a magnetization-prepared spiral imaging technique, termed T1prep, for time-efficient radiofrequency (RF)-insensitive prostate T(1) quantification at 1.5 T and evaluate signal-to-noise ratio (SNR) limits to voxel-based versus subregion analysis. MATERIALS AND METHODS: A magnetization-prepared spiral imaging technique was adapted for robust T(1) contrast development, multislice imaging within 5 minutes, and data regression to a monoexponential decay. In vitro testing evaluated RF insensitivity of the multislice acquisition plus method accuracy. A pilot study was performed in 15 patients with low or intermediate risk localized prostate cancer. RESULTS: The multislice design displayed excellent RF insensitivity (<1% error for RF mistunings to ± 20%) and accuracy (within 3% of gold standard for T(1) values between 140 and 2100 msec). A clinical pilot study reported significantly reduced T(1) from PZ to CG to tumor subregions (PZ: 1421 ± 168 msec, n = 11; CG: 1314 ± 49 msec, n = 13; 1246 ± 68 msec, n = 8). SNR measurements identified an inappropriateness of voxel-based analysis. CONCLUSION: T1prep can quantify prostate T(1) as an adjunct measure for quantitative perfusion measurements and longitudinal treatment response monitoring. Intrapatient heterogeneities support T(1) assessment within individual patients. SNR calculations will support a transition to voxel-based analysis in future trials.
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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 | 0.002 |
| 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 | 0.000 | 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".