2D-3D registration for brain radiation therapy using a 3D CBCT and a single limited field-of-view 2D kV radiograph
Bibliographic record
Abstract
We report results of an intensity-based 2D-3D rigid registration framework for patient positioning and monitoring during brain radiotherapy. We evaluated two intensity-based similarity measures, the Pearson Correlation Coefficient (ICC) and Maximum Likelihood with Gaussian noise (MLG) derived from the statistics of transmission images. A useful image frequency band was identified from the bone-to-no-bone ratio. Validation was performed on gold-standard data consisting of 3D kV CBCT scans and 2D kV radiographs of an anthropomorphic head phantom acquired at 23 different poses with parameter variations along six degrees of freedom. At each pose, a single limited field of view kV radiograph was registered to the reference CBCT. The ground truth was determined from markers affixed to the phantom and visible in the CBCT images. The mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters along the x, y and z axes for ICC were φ x : 0.08(0.04)°, φ y : 0.10(0.09)°, φ z : 0.03(0.03)°, t x : 0.13(0.11) mm, t y : 0.08(0.06) mm and t z : 0.44(0.23) mm. For MLG, the corresponding results were φ x : 0.10(0.04)°, φ y : 0.10(0.09)°, φ z : 0.05(0.07)°, t x : 0.11(0.13) mm, t y : 0.05(0.05) mm and t z : 0.44(0.31) mm. It is feasible to accurately estimate all six transformation parameters from a 3D CBCT of the head and a single 2D kV radiograph within an intensity-based registration framework that incorporates the physics of transmission images.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".