Intensity-based registration of prostate brachytherapy implants and ultrasound
Bibliographic record
Abstract
PURPOSE: In prostate brachytherapy, determining the 3D location of the seeds relative to surrounding structures is necessary for calculating dosimetry. Ultrasound imaging provides the ability to visualize soft tissues, and implanted seeds can be reconstructed from C-arm fluoroscopy. Registration between these two complementary modalities would allow us to make immediate provisions for dosimetric deviation from the optimal implant plan. METHODS: We propose intensity-based registration between ultrasound and a reconstructed model of seeds from fluoroscopy. The ultrasound images are pre-processed with recursive thresholding and phase congruency. Then a 3D ultrasound volume is reconstructed and registered to the implant model using mutual information. RESULTS: A standard training phantom was implanted with 49 seeds. Average registration error between corresponding seeds relative to the ground truth is 0.09 mm. The effect of false positives in ultrasound was investigated by masking seeds from the fluoroscopy reconstructed model. The registration error remained below 0.5 mm at a rate of 30% false positives. CONCLUSION: Our method promises to be clinically adequate, where requirements for registration is 1.5 mm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".