Multi‐echo gradient recalled echo imaging of the pelvis for improved depiction of brachytherapy seeds and fiducial markers facilitating radiotherapy planning and treatment of prostatic carcinoma
Bibliographic record
Abstract
PURPOSE: MR localization of implanted devices for radiotherapy (RT) in prostatic carcinoma is critical for treatment planning. This clinical note studies the application of a multi-echo gradient recalled echo (GRE) pulse sequence with sum of squares echo combination (ME GRE) to enhance detection of seeds and fiducials. MATERIALS AND METHODS: Fifteen patients who underwent MRI using fast spin echo (FSE), single-echo and ME GRE over a 9-month period were retrospectively evaluated by two readers who assessed overall image quality, depiction of seeds/fiducials and image sharpness using a 5-point scale (1 = poor, 2 = suboptimal, 3 = adequate, 4 = above average, 5 = excellent). Image scores were compared using the Wilcoxon sign rank test. RESULTS: In all 15 patients, both readers rated the depiction of seeds/fiducials with ME GRE as excellent. In all 15 patients, overall image quality and image sharpness with ME GRE was rated as excellent by reader 1. In 12/15 patients, overall image quality and image sharpness with ME GRE was rated as excellent and in the other patients above average by reader 2. There was a difference in depiction of seeds/fiducials comparing GRE to FSE (P < 0.001) and ME to single echo GRE (P < 0.001). Overall image quality and sharpness was higher with ME compared with single echo GRE (P < 0.001) and similar to FSE (P = 0.26 and P = 0.16). CONCLUSION: Multi-echo GRE provides better detection of implanted seeds and fiducial markers when compared with both FSE and single-echo GRE potentially improving RT treatment planning for prostate carcinoma.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".