SU-E-J-106: The Use of Deformable Image Registration with Cone-Beam CT for a Better Evaluation of Cumulative Dose to Organs
Bibliographic record
Abstract
Purpose: The knowledge of dose accumulation in the patient tissues in radiotherapy helps in determining the treatment outcomes. This project aims at providing a workflow to map cumulative doses that takes into account interfraction organ motion without the need for manual re-contouring. Methods: Five prostate cancer patients were studied. Each patient had a planning CT (pCT) and 5 to 13 CBCT scans. On each series, a physician contoured the prostate, rectum, bladder, seminal vesicles and the intestine. First, a deformable image registration (DIR) of the pCTs onto the daily CBCTs yielded registered CTs (rCT) . This rCT combined the accurate CT numbers of the pCT with the daily anatomy of the CBCT. Second, the original plans (220 cGy per fraction for 25 fractions) were copied on the rCT for dose re-calculation. Third, the DIR software Elastix was used to find the inverse transform from the rCT to the pCT. This transformation was then applied to the rCT dose grid to map the dose voxels back to their pCT location. Finally, the sum of these deformed dose grids for each patient was applied on the pCT to calculate the actual dose delivered to organs. Results: The discrepancy between the planned D98 and D2 and these indices re-calculated on the rCT, are, on average, of −1 ± 1 cGy and 1 ± 2 cGy per fraction, respectively. For fractions with large anatomical motion, the D98 discrepancy on the re-calculated dose grid mapped onto the pCT can raise to −17 ± 4 cGy. The obtained cumulative dose distributions illustrate the same behavior. Conclusion: This approach allowed the evaluation of cumulative doses to organs with the help of uncontoured daily CBCT scans. With this workflow, the easy evaluation of doses delivered for EBRT treatments could ultimately lead to a better follow-up of prostate cancer patients.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".