Comparison of 5 Rectal Preparation Strategies for Prostate MRI and Impact on Image Quality
Bibliographic record
Abstract
PURPOSE: To compare 5 different rectal preparation strategies for prostate MRI. METHODS: This 5-arm quality-assurance study evaluated 56 patients per arm (280 patients) including: no preparation, clear-fluids diet (CFD) beginning at 00:00 hours on the day of MRI, Fleet®-enema, enema + CFD, enema + CFD + IV-antispasmodic agent. The study was powered to 0.80 with alpha-error of 0.05. Three blinded radiologists independently evaluated T2-Weighted (T2W) and Diffusion Weighed Imaging (DWI) for: rectal diameter (maximal AP diameter), rectal content (stool, fluid, gas), rectal motion, T2W/DWI image quality, T2W image sharpness and DWI susceptibility artifact using 5-point Likert scales. Overall comparisons were performed using analysis of variance (ANOVA) and Kruskal-Wallis, with pair-wise comparisons using paired t-tests and Wilcoxon sign-rank tests. RESULTS: Rectal diameter and amount of gas were lower in enema compared to non-enema groups (p < 0.001), with smallest diameter and least gas in the enema + CFD + IV-antispasmodic group (p = 0.022-<0.001). T2W image quality and sharpness were highest in the enema + CFD groups (p < 0.001) with no difference comparing enema + CFD with/without IV-antispasmodic (p = 0.064, 0.084). Motion artifact was least in enema + CFD + IV-antispasmodic group compared to all other groups (p < 0.001), followed by the enema + CFD group (p = 0.008-<0.001). DWI image quality was highest (p < 0.001) and DWI susceptibility artifact lowest (p < 0.001) in the enema + CFD groups (p < 0.001) and did not differ comparing enema + CFD with/without anti-spasmodic (p = 0.058-0.202). CONCLUSIONS: Use of enema + clear-fluids diet before prostate MRI yields the highest T2W and DWI image quality with the least DWI artifact. IV-antispasmodic use reduces motion on T2W but does not improve image quality on T2W or DWI, or lessen DWI artifact compared to enema + clear-fluids diet.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".