Clinical Implementation of Prostate Image Guided Radiation Therapy: A Prospective Study to Define the Optimal Field of Interest and Image Registration Technique Using Automated X-Ray Volumetric Imaging Software
Bibliographic record
Abstract
Alignment of the CBCT with the reference CT is called image registration (IR). The parameters for utilizing the automated Elekta XVI IR software for IGRT of the prostate still remain to be defined. In this study, we compare several automated XVI IR parameters to manual registration to identify the optimal automated IR technique for the prostate gland. 280 prostate IRs were conducted as follows: 210 automated, and 70 manual IR were performed using 70 CBCT scans of seven patients. The three arms of the automated registrations were: (i) extended FOI/Bone + grey scale (double IR); (ii) limited FOI/GS (single IR); and (iii) extended FOI/GS (single IR). Automated IRs were compared to manual IRs; x, y, z shifts, failures, and errors recorded for off-line analysis. Based on the most successful parameters, a departmental protocol was developed and 432 automated IR were performed (on 20 patients) for analysis. Automated IR were classified as: Successful, failed, error, or unregistered. In arm 1, the rate of successful, failed, error, and unregistered IR were 52.8%, 1.5%, 8.6%, 37.1%, respectively, arm 2: 90% successful, 10% failed, arm 3: 100% successful. Using the arm 3 parameters for the 432 automated IRs, the incidence of unregistered scans was 0%, rescanning was required in 1% of treatments, and the time for performing the auto IR was < 5.5 minutes. We found that extended FOI + single (GS) IR results in shifts comparable to manual IR using automated XVI software. We experienced multiple unsuccessful registrations with the other methods. We conclude that when utilizing the Elekta XVI automated IR software, the extended FOI/single IR results in successful registrations most often. In addition, it is currently effectively used in our clinical practice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".