TU-E-BRA-11: Volume of Interest Cone Beam CT with a Low-Z Linear Accelerator Target: Proof-of-Concept
Bibliographic record
Abstract
PURPOSE: This study demonstrates feasibility and advantages of volume of interest (VOI) cone beam CT (CBCT) imaging performed with an x-ray beam generated from 2.35 MeV electrons incident on a carbon linear accelerator target. METHODS: The electron beam energy was reduced to 2.35 MeV in a Varian 21EX linear accelerator containing a 7.6 mm thick carbon x-ray target. Arbitrary imaging volumes were defined in the planning system to produce dynamic MLC sequences capable of tracking off-axis VOIs in phantoms. To reduce truncation artefacts, missing data in projection images were completed using a priori DRR information from the planning CT set. The feasibility of the approach was shown through imaging of an anthropomorphic phantom and the head-and-neck section of a lamb. TLD800 and EBT2 radiochromic film measurements were used to compare the VOI dose distributions with those for full-field techniques. CNR was measured for VOIs ranging from 4 to 15 cm diameter. RESULTS: The 2.35 MV/Carbon beam provides favorable CNR characteristics, although marked boundary and cupping artefacts arise due to truncation of projection data. These artefacts are largely eliminated using the DRR filling technique. Imaging dose was reduced by 5-10% and 75% inside and outside of the VOI, respectively, compared to full-field imaging for a cranial VOI. For the 2.35 MV/Carbon beam, CNR was shown to be approximately invariant with VOI dimension for bone and lung objects. This indicates that the advantage of the VOI approach with the low-Z target beam is substantial imaging dose reduction, not improvement of image quality. CONCLUSIONS: VOI CBCT using a 2.35 MV/Carbon beam is a feasible technique whereby a chosen imaging volume can be defined in the planning system and tracked during acquisition. The novel x-ray beam affords good CNR characteristics while imaging dose is localized to the chosen VOI. Funding for this project has been received from Varian Medical, Incorporated.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".