4D cone-beam CT image reconstruction of Varian TrueBeam v1.6 projection images for clinical quality assurance of stereotactic ablative radiotherapy to the lung
Bibliographic record
Abstract
On-board cone-beam computed tomography (CBCT) imaging integrated with medical linear accelerators offers a viable tool for tumor localization just prior to radiation treatment delivery. However, the exact tumor location during treatment is not well-defined due to respiratory motion. This is taken into account during treatment planning by adding margins to the visible tumor volume defining the high dose region. The respiratory motion used to optimize the treatment plan is not guaranteed to be reproducible on the day of treatment, suggesting that the high dose region may not fully contain the tumor at all points of its trajectory during treatment. In this thesis, to image the tumor at the different portions of the breathing cycle, CBCT projections were binned by the respiratory signal at their time of acquisition. Reconstructing each bin created a 3D image depicting the tumor at one point of its trajectory. Combining the binned reconstructions added in a temporal component, defining a 4D-CBCT. 4D-CBCT reconstructions were performed on 6 stereotactic ablative radiotherapy (SABR) lung cancer patients. Imaging was performed using the Varian TrueBeam (v1.6) and respiratory information was captured with the infra-red camera-based Varian real-time position management (RPM) system. Both analytical and iterative reconstruction algorithms, and image quality metrics were used for a comparative study. Tumor motion was measured by tracking the visible tumor volume centroid from each 4D-CBCT image. The high dose regions defined during treatment planning were compared to the 4D-CBCT tumor volume during its trajectory using an overlap metric to determine if the tumor remained confined to the treatment volume, or not. 4D-CBCTs were found to be well reconstructed using iterative methods. When viewed sequentially the 4D-CBCT images visibly show tumor motion following a sinusoidal-like behavior. Examination of the tumor motion and overlap metric verify that the margins currently used to define the high dose region fully encompass the tumor during all times of its trajectory, i.e 100% overlap within error. The results indicate the current margins used for SABR patients at the British Columbia Cancer Agency are sufficient in providing adequate tumor coverage when accounting for tumor motion and setup uncertainties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".