Feasibility of real‐time lung tumor motion monitoring using intrafractional ultrasound and <scp>kV</scp> cone beam projection images
Bibliographic record
Abstract
PURPOSE: The ability to monitor intrafractional tumor motion is essential for radiation therapy of thoracic and abdominal tumors. This study aims to develop a method to track lung tumor motion using intrafractional continuous ultrasound (US) and periodic cone-beam projection images (CBPI). METHODS: and XVI platforms on an Elekta linac, respectively. The data were synchronized through a video capture card (VCE-PRO, IMPERX Inc.) which was triggered by the XVI system. In this way, a system was configured to allow real-time acquisition of the diaphragm position synchronized with periodic acquisition of the lung tumor position. Feasibility of the system was demonstrated by acquiring synchronized data on an in-house motion platform with embedded spheres of different materials and US images of the diaphragm on 5 volunteers of various body sizes. Finally, ultrasound b-mode images and CBPI were also acquired simultaneously from 3 lung cancer patients. RESULTS: Diaphragm motion monitoring under free breathing (FB) was successful with intracostal US imaging. We observed that diaphragm visualization decreased with the increase in the body size of the volunteer. The US system was able to track the motion as small as 2 mm in the phantom. The intrafractional CBPI acquired during VMAT delivery was successfully synchronized with US acquisition in a phantom study. Collected patient data showed a significant correlation between diaphragm motion, an internal surrogate monitored by US, and the tumor motion in superior-inferior (SI) direction monitored by XVI (P ˂ 0.0001). CONCLUSIONS: The feasibility of real-time lung tumor motion tracking in SI direction with continuous ultrasound and periodic CBPI was demonstrated. The real-time estimation of the target position from the two streams for lung cancer patients would enable respiration gating or tracking during SBRT.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".