MO‐D‐L100J‐03: Generating Patient Specific Motion Models Using a Navigator Channel and a Liver Population FEM Motion Model
Bibliographic record
Abstract
Purpose: To incorporate motion information from 2D images using a navigator technique and finite element modelling (FEM) to generate patient‐specific 3D motion models. Methods and Materials: A liver population motion model was created using a FEM deformable image registration platform, MORFEUS, to simulate liver deformation during a respiratory cycle. Twenty patient's exhale livers were constructed from the population FEM, and then deformed into their inhale livers. Deformation maps were generated and used to check the accuracy of the navigator‐updated patient models. NavigatorView, an in‐house algorithm, places a rectangular navigator channel to define a region of interest on 2D image slices. An operator chosen navigator is placed on the exhale image while another navigator is automatically placed at the corresponding location on the inhale image. Motion was calculated as the shift required to align the intensity profiles within the channels at the superior dome and the inferior tip of the liver on coronal CT slices and simulated radiograph images. The navigator shifts were used in a weighting equation to generate patient‐specific motion models from the population motion model. Results: The average accuracy ± standard deviation of the navigator channel at the superior and inferior edges is 0.12 ± 0.12cm and 0.25 ± 0.25cm respectively. The navigator‐updated patient‐specific models was 100% and 80% successful of the 20 patient cases for the coronal CT and simulated radiograph slices respectively, where a successful case achieved an accuracy error less than the image voxel size (0.25cm). Conclusions: The navigator technique allows for updating patient‐specific 3D motion models from a liver population model using more easily acquired 2D images. This can be a useful tool for image‐guided therapeutics, such as intra‐fraction image guided tracking during radiotherapy, where 2D data may be more rapidly acquired. Research sponsored by the National Cancer Institute of Canada — Terry Fox Foundation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".