TU‐FF‐A3‐05: Dosimetric Effect of Cupping Artefact in MVCBCT Images of the Head and Neck Region
Bibliographic record
Abstract
Purpose: To quantify the dose calculation accuracy achievable with 3D anatomical images obtained by Megavoltage Cone‐Beam Computed Tomography (MVCBCT) for the head and neck region (H&N). Method and Materials: MVCBCT images of inserts of different density immersed in water were obtained. This allowed the tuning of the parameters used for the image reconstruction. A MVCBCT number versus material density curve was also extracted for dose calculation purposes. MVCBCT images of a Rando phantom head were then acquired on a linac treatment couch with two different gain image calibrations and in two different positions relative to the room isocenter. Voxel‐based and band‐pass filter cupping artifact reduction methods were applied on all MVCBCT images. Images of the same phantom were also obtained with a kVCT. All images were transferred to a treatment planning system and dose calculations performed with various beam configurations. The dose differences obtained with the kVCT images and the MVCBCT images were analyzed using a gamma index function. Results: At best, 96.1% and 98.8% of the dose points calculated with the MVCBCT images were within the dose calculated with the kVCT image by [2%, 2 mm] and [3%, 3 mm], respectively. The worst cases observed had fractions of 87.7% and 96.3% of the dose points that agreed within [2%, 2 mm] and [3%, 3 mm], respectively. The cupping artifact reduction methods tested did not significantly improve the dose calculation for most cases. Conclusion: With proper calibration, dose calculations with MVCBCT images in the H&N region are feasible with an accuracy of [3 %, 3 mm] or less. The cupping artifact for H&N imaging does not lead to important dose calculation errors. Dose calculation with patient MVCBCTs and treatment plans are ongoing. Conflict of Interest: Research sponsored by Siemens OCS.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".