Prostate positioning errors associated with two automatic registration based image guidance strategies
Bibliographic record
Abstract
Daily image guidance for helical tomotherapy prostate patients is based on the registration of pre-treatment megavoltage CT (MVCT) images and the original planning CT. The goal of registration, whether manual or automatic, is the overlap of the prostate; otherwise prostate misplacement may compromise the efficacy of treatment or lead to increased toxicity. A previous study demonstrated that without the aid of implanted fiducials, manual registration results in inaccurate prostate positioning. The objective of this work is to quantify prostate misplacement that results from automatic bone matching (BM) and image matching (IM) registration algorithms. 204 MVCT images from 8 high risk tomotherapy prostate patients were incorporated into this retrospective study. BM and IM registration algorithms--based on maximization of mutual information of bony anatomy only and the entire image, respectively--were used to independently register MVCT images to their respective planning images. A correlation coefficient based algorithm that uses known planning CT contour information was used for automatic prostate localization in each MVCT image. Daily prostate misplacement was determined by repositioning as calculated from the BM and the IM algorithms. Mean (+/- SD) and maximum 3D prostate positioning errors were 3.7 +/- 2.1 mm and 11.8 mm for bone matching and 4.6 +/- 2.3 mm and 11.5 mm for image matching. In terms of translational directions, IM would lead to prostate positioning error > or = 3 mm in any of the LR, AP or SI directions in 62% of treatment fractions. The corresponding value for BM is 51%. The values for positioning errors > or = 5 mm were 29% and 17% for IM and BM, respectively. This data suggests automatic daily image guidance for tomotherapy prostate patients should be based on bone matching instead of image matching.
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 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 | 0.001 | 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 | 0.001 |
| 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".