Sci-Thurs PM: Delivery-10: Marker trajectory reconstruction using cone-beam CT projection images
Bibliographic record
Abstract
Image guidance and daily verification is becoming increasingly important in radiotherapy today, especially when dealing with moving targets. Cone-beam CT (CBCT) is a 3D imaging modality available in the treatment room, but it is difficult to assess motion from this integrated image. If a fiducial marker is placed in a moving target, it can easily be identified in the raw projection images that are captured during the CBCT. Normally, this projection data is discarded after reconstruction, but we show a method that can be used to extract trajectory information from this data. A CBCT was acquired of a moveable phantom with a known motion and an implanted gold seed. During the scan, the phantom underwent 14 cycles of motion. The fiducial marker location was determined from each raw projection image, and the data was separated into individual breathing cycles. Each point in each cycle was then assigned a 'breath phase' based temporal position in the cycle. To reconstruct a single 3D position in room coordinates, two nearly orthogonal images at the same 'breath phase' but in two different breaths were used. Multiple reconstructions from 14 nearly orthogonal pairs produced points which on average should represent the 4D trajectory. When compared to the true motion, the reconstructed average trajectory had an accuracy of less than 1mm. We have shown that in the ideal case of identical motion in each cycle we can accurately measure the 4D trajectory. Future work will use this tool to collect and estimate the trajectories for more realistic motions which differ from cycle to cycle.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".