Integration of digital fluoroscopy with CT‐based radiation therapy planning of lung tumors
Bibliographic record
Abstract
Radiation dose escalation may be a means to increase the local control rate of inoperable lung tumors. Treatment plans involve the creation of a uniform planning target volume (PTV) to ensure proper coverage despite patient breathing and setup error. This may lead to unnecessary radiation of normal tissue in shallow breathers or target underdosing for patients with excess internal motion. Therefore, the nature of tumor motion for each patient should be measured in 3D, something that cannot be done with CT alone. We have developed a method that acquires 2D real-time fluoroscopic images (loops) and coregisters them with 2D digitally reconstructed radiographs (DRR) formed from the CT scan. The limitations of CT to encompass motion can be overcome by merging the two modalities together. The accuracy of the coregistration method is tested with a stationary grid of radio-opaque markers at various spatial positions. The in-plane (at-depth) displacement between markers on the fluoroscopic image versus the DRR varies with position across the image due to slight misalignments between the x-ray source used in fluoroscopy and the virtual source used for the DRR relative to the test object. At clinically relevant positions, the maximum, measured in-plane displacement, is 1.1 mm. The method is applied to the thorax of an anthropomorphic phantom and a good fit is observed between the appearances of the bony anatomical structures on the coregistered image. Finally, a series of motion measurements are carried out on two oscillating cylindrical objects. The degree of motion as measured by fluoroscopy is accurate to within 1.0 mm, whereas the DRR is inconsistent in predicting motion. The coregistration of fluoroscopic loops with the DRR shows at what point within the oscillation the DRR fails to encompass motion. For any treatment site involving target motion, this real-time imaging is a useful asset in the planning stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".