Po‐Poster ‐ 26: Investigation of normalized mutual information for co‐registration of CT — MR images of permanent prostate implants
Bibliographic record
Abstract
Post‐implant dosimetric evaluation is the standard contemporary method for assessing permanent prostate implant quality and determining the dose received by the prostate and organs at risk over the course of treatment. In current practice evaluation is performed using a CT image set typically acquired one month after the implant. However, due to poor visualization of prostate contours on the CT images, the evaluation can be difficult and is only approximate. MRI allows a better appreciation of prostate contours, but the visibility of the ensemble of the seeds is insufficient to allow its use as the only means of evaluating the treatment. Image registration of CT and MR image sets can combine the benefits of both imaging modalities and provide the means for sufficiently accurate dosimetric analysis. Mutual information is an efficient registration technique that can be used to fuse selected volumes of interest such as the prostate and neighbouring tissues. In our study we investigate optimization of registration search parameters, such as threshold level, translation and rotation angle ranges, and volume boundaries for fusing CT — MR images of a tissue equivalent prostate phantom implanted with inactive seeds, simulated image data, and clinical data sets. The results obtained so far are encouraging and suggest that mutual information‐based registration may eventually enable automatic fusion of clinical CT and MR images for prostate implant post‐dosimetry.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".