SU‐FF‐J‐05: Motion Estimation Using Cone‐Beam CT Projection Images
Bibliographic record
Abstract
Purpose: To investigate the motion information that can be extracted from the raw cone‐beam CT(CBCT) projection data of a fiducial marker in a respiratory motion phantom. Methods and Materials: A CBCT was acquired of a programmable respiratory phantom embedded with a gold seed fiducial marker. During acquisition, 650 raw projection images were sequentially captured as the imager rotated in a 360 degree arc. With a 60 second CBCT, and a 4 second respiratory period, the raw dataset contained motion information from 15 complete motion cycles. The images were binned based on respiratory phase, and the location of the gold seed in each image was determined. The back‐projections of the fiducial at the same phase but from different cycles (and therefore different gantry angles) produced a set of points representing fiducial positions at that phase. By using the average position, and by reconstructing positions for all phases, a trajectory was built. Results: When the fiducial's motion was identical from cycle to cycle, the difference between the actual and reconstructed average motion was less than 1mm. When the motion changes between cycles, an ‘average trajectory’ can be constructed, whose fidelity to the true average depends on the degree of variability of the true motion cycle to cycle. Conclusions: There is motion information present in the raw CBCT dataset that can be exploited with the use of an implanted fiducial marker. This particular example might provide a useful characterization of the internal motion at the treatment unit from the same dataset as is used for patient and target setup.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".