TH‐D‐AUD‐04: Development and Evaluation of An Ultrasound‐Guided Tracking and Gating System for Radiotherapy of Liver Metastases
Bibliographic record
Abstract
Purpose: To investigate the feasibility of ultrasound imaging for tumor localization and respiratory gating for the treatment of liver metastases with radiotherapy. Method and Materials: An ultrasound‐guided tracking and gating system was developed for stereotactic body radiotherapy. We use an existing infrared marker system to track a moving ultrasound probe which is obtaining images of target motion throughout the respiratory cycle. The reconstructed ultrasound video of target motion in room coordinates is displayed with the treatment beam projection on the imaging plane to determine optimum gating levels. To investigate the system, a phantom was constructed to model the respiratory motion of a liver tumor and also to enable imaging with ultrasound and kV x‐rays for comparison purposes. Ultrasound video and orthogonal localizing x‐ray images were taken of the same moving tumor model. We compared the timing to an existing clinical system. Results: Ultrasound has a better soft tissue contrast than x‐ray and is capable of producing up to 26 fps video. We measured a time delay of 22 ± 11 ms at 2.4 s period, and 30 ± 25 ms at 4.8 s period of breathing motion. The same figures for the clinical x‐ray system were 81± 29 ms at 2.4 s, and 5 ± 18 ms at 4.8 s period. This was comparable to our system's delay. None of the measured delays in our system would compromise planning margins, the largest observed error extending to only 1.1 mm. Conclusion: An ultrasound‐guided tracking and gating system and a moving phantom was developed. The image quality and timing delay of the system was equivalent to that of an existing clinical solution. Further investigation needed to apply three‐dimensional gating signal and to test imaging on actual patients before clinical application. Research sponsored by the Alberta Cancer 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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".