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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".