Technical Note: Cone beam CT imaging for 3D image guided brachytherapy for gynecological HDR brachytherapy
Bibliographic record
Abstract
PURPOSE: This paper focuses on a novel image guidance technique for gynecological brachytherapy treatment. The present standard technique is orthogonal x-ray imaging to reconstruct the 3D position of the applicator when the availability of CT or MR is limited. Our purpose is to introduce 3D planning in the brachytherapy suite using a cone beam CT (CBCT) scanner dedicated to brachytherapy. This would avoid moving the patient between imaging and treatment procedures which may cause applicator motion. This could be used to replace the x-ray images or to verify the treatment position immediately prior to dose delivery. METHODS: The sources of CBCT imaging artifacts in the case of brachytherapy were identified and removed where possible. The image quality was further improved by modifying the x-ray tube voltage, modifying the compensator bowtie filter and optimizing technical parameters such as the detector gain or tube current. RESULTS: The image quality was adequate to reconstruct the applicators in the treatment planning system. The position of points A and the localization of the organs at risk (OAR) ICRU points is easily achieved. This allows identification of cases where the rectum had moved with respect to the ICRU point which would require asymmetrical source loading. A better visualization is a first step toward a better sparing of the OAR. CONCLUSIONS: Treatment planning for gynecological brachytherapy is aided by CBCT images. CBCT presents advantages over CT: acquisition in the treatment room and in the treatment position due to the larger clearance of the CBCT, thereby reducing problems associated to moving patients between rooms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".