CBCT with specification of imaging dose and CNR by anatomical volume of interest
Bibliographic record
Abstract
PURPOSE: A novel method has been developed for volume of interest (VOI) cone-beam CT (CBCT) imaging using a 2.35 MV/Carbon target linac imaging beam line combined with dynamic multileaf collimator sequences. METHODS: The authors demonstrate the concept of acquisition of multiple, separate imaging volumes, where volumes can be either completely separated or nested, and are associated with predetermined imaging dose and contrast-to-noise ratio (CNR) characteristics. Two individual MLC sequences were established in the planning system (Eclipse, Varian Medical) to collimate the beam according to a defined inner VOI (e.g., containing a target volume under image guidance) and an outer VOI (e.g., including surrounding landmarks or organs-at-risk). MLC sequences were interleaved as a function of gantry angle to produce a reconstructed CBCT image with nested VOIs. By controlling the ratio of inner-to-outer ratio of MLC segments (and thus Monitor Units) during acquisition, the relative dose and CNR in the two volumes can be controlled. Inner-to-outer ratios of 2:1 to 6:1 were examined. RESULTS: The concept was explored using an anatomical head phantom to assess image quality. A geometric phantom was used to quantify absolute dose and CNR values for the various sequences. The authors found that the dose in the outer VOI decreased by a functional relationship dependent on the inner-to-outer sequence ratio, while the CNR varied by the square root of dose, as expected. CONCLUSIONS: In this study the authors demonstrate flexibility in VOI CBCT by tailoring the imaging dose and CNR distribution in separate volumes within the patient anatomy. This would allow for high quality imaging of a target volume for alignment purposes, with simultaneous low dose imaging of the surrounding anatomy (e.g., for coregistration).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".