Registration between ultrasound and fluoroscopy or CT in prostate brachytherapy
Bibliographic record
Abstract
PURPOSE: In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted seeds can be visualized by fluoroscopy. Intraoperative dosimetry optimization is possible using a combination of TRUS and fluoroscopy, but requires localization of the fluoroscopy-derived seed cloud, relative to the anatomy as seen on TRUS. The authors propose to develop a method of registration of TRUS images and the implants reconstructed from fluoroscopy. METHODS: A phantom was implanted with 48 seeds then imaged with TRUS and CT. Seeds were reconstructed from CT yielding a cloud of seeds. Fiducial-based ground-truth registration was established between the TRUS and CT. TRUS images are filtered, compounded, and registered to the reconstructed implants by using an intensity-based metric. The authors evaluated a volume-to-volume and point-to-volume registration scheme. In total, seven TRUS filtering techniques and three image similarity metrics were analyzed. The method was also tested on human subject data captured from a brachytherapy procedure. RESULTS: For volume-to-volume registration, noise reduction filter and normalized correlation metrics yielded the best result: An average of 0.54 +/- 0.11 mm seed localization error relative to ground truth. For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: An average of 0.38 +/- 0.19 mm seed localization error relative to the ground truth. In human patient data, C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intraoperative fluoroscopy imaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86 +/- 1.26 mm. CONCLUSIONS: Fully automated registration between TRUS and the reconstructed seeds performed well in ground-truth phantom experiments and qualitative observation showed adequate performance on early clinical patient data.
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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) | 0.000 | 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) | 0.000 | 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".