SU‐FF‐I‐96: Motion Adaptation for Registering Daily Online CBCT Images to Planning CT Images
Bibliographic record
Abstract
Purpose: Perform online image guidance (IGRT) and motion assessment using deformable registration by adapting patient liver motion models. Materials and Methods: 4DCT and daily 4DCBCT images were obtained for 15 patients treated under a stereotactic‐body radiotherapy protocol. Finite element models of patient liver respiratory motion were generated using a biomechanical‐model based platform, MORFEUS from 4DCT images. Motion was assessed using a Navigator channels (NC) technique. Five NCs, rectangular regions of interest, were placed on two images at corresponding spatial locations to define left (LR), anterior‐posterior (AP), and superior‐inferior (SI) liver edges. To determine interfraction liver/tumour positioning, the 4DCT exhale and daily 4DCBCT exhale images were used, while intrafraction liver/ tumour motion used exhale and inhale 4DCBCT images. The NC determines the 1D displacement between both images. This 1D motion adapts the MORFEUS‐based liver motion model. NC adaptation accuracy was evaluated by comparing the results with MORFEUS registration. Results: NC refinement has been performed on 8 patients to date in less than 2 mins each. Inter‐fraction motion of the liver exceeded 1 cm. Inter‐fraction NC technique accuracy (absolute mean (SD)) was 0.15 (0.11), 0.16 (0.10), and 0.15 (0.09) cm, in the LR, AP, and SI directions, respectively. Intra‐fraction motion exceeded 1.5cm. NC technique accuracy was 0.14 (0.10), 0.15 (0.08), and 0.18 (0.09) cm, in the LR, AP, and SI directions, respectively. The accuracy of the NC technique to identify the center of mass shift of the tumour for intra‐fraction motion was 0.16 (0.15), 0.14 (0.12), and 0.15 (0.10) cm, in the LR, AP, and SI directions, respectively. Conclusion: NC adaptation technique can accurately and efficiently integrate deformable registration into the IGRT process to account for inter and intra‐fraction motion. Research sponsored by the National Cancer Institute of Canada‐Terry Fox Foundation, Elekta Oncology Systems, and NIH 5RO1CA124714‐02.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".