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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".