A robotically assisted 3D printed quality assurance lung phantom for Calypso
Bibliographic record
Abstract
Abstract Purpose . Radiation dose delivered to targets located near the upper-abdomen or in the thorax are significantly affected by respiratory-motion. Relatively large-margins are commonly added to compensate for this motion, limiting radiation-dose-escalation. Internal-surrogates of target motion, such as a radiofrequency (RF) tracking system, i.e. Calypso ® System, are used to overcome this challenge and improve normal-tissue sparing. RF tracking systems consist of implanting transponders in the vicinity of the tumor to be tracked using radiofrequency-waves. Unfortunately, although the manufacture provides a universal quality-assurance (QA) phantom, QA-phantoms specifically for lung-applications are limited, warranting the development of alternative solutions to fulfil the tests mandated by AAPM’s TG142. Accordingly, our objective was to design and develop a motion-phantom to evaluate Calypso for lung-applications that allows the Calypso ® Beacons to move in different directions to better simulate tru e lung-motion. Methods and Materials. A Calypso lung QA-phantom was designed, and 3D-printed. The design consists of three independent arms where the transponders were attached. A pinpoint-chamber with a buildup-cap was also incorporated. A 4-axis robotic arm was programmed to drive the motion-phantom to mimic breathing. After acquiring a four-dimensional-computed-tomography (4DCT) scan of the motion-phantom, treatment-plans were generated and delivered on a Varian TrueBeam ® with Calypso capabilities. Stationary and gated-treatment plans were generated and delivered to determine the dosimetric difference between gated and non-gated treatments. Portal cine-images were acquired to determine the temporal-accuracy of delivery by calculating the difference between the observed versus expected transponders locations with the known speed of the transponders’ motion. Results. Dosimetric accuracy is better than the TG142 tolerance of 2%. Temporal accuracy is greater than, TG142 tolerance of 100 ms for beam-on, but less than 100 ms for beam-hold. Conclusions. The robotic QA-phantom designed and developed in this study provides an independent phantom for performing Calypso lung-QA for commissioning and acceptance testing of Calypso for lung treatments.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".