Improving image quality and reducing dose with 2.5 MV diamond target volume‐of‐interest cone beam CT imaging
Bibliographic record
Abstract
PURPOSE: Over the past decades, continuous efforts have been made to improve megavoltage (MV) image quality versus dose characteristics, including the implementation of low atomic number (Z) targets in MV beamlines and the development of more efficient detectors. Recently, a diamond target beam within a commercial radiotherapy treatment platform demonstrated improved planar contrast-to-noise-ratio (CNR) per unit dose using a novel 2.5 MV sintered diamond target beam, which enabled image acquisition on the order of mGy. The present work assesses cone beam CT (CBCT) image quality characteristics for the novel 2.5 MV diamond target beam and the effects of volume-of-interest (VOI) collimation on the image quality and imaging dose distribution. METHODS: A sintered diamond target was incorporated into the target arm of the linear accelerator, replacing the 2.5 MV commercial copper imaging target. CBCT image quality was evaluated against the commercial imaging beam with regard to spatial resolution and CNR versus dose. In addition to full-field acquisitions, we investigated VOI techniques that collimate the imaging beam to preselected anatomy, to determine potential image quality improvements and dose sparing capacity. Using an anthropomorphic phantom, VOI regions were defined to encompass the maxillary and ethmoid sinuses and ranged in dimension from 3 cm to 4.85 cm equivalent radius. The MLC was fit to each VOI structure throughout a full CBCT arc and the corresponding MLC sequences were produced as XML scripts for acquisition. Calibrated radiochromic film was used in phantom to measure cumulative axial dose distributions during each CBCT acquisition. RESULTS: for the 2.5 MV diamond target beam compared to the commercial beam. Using VOI techniques, CNR increased monotonically as a function of equivalent radius at the bone-tissue interface. At the bone-sinus interface, the CNR for the full-field case was slightly decreased compared to the largest VOI case. Imaging dose in the anteroposterior direction increased with increasing VOI equivalent radius. CONCLUSION: The novel 2.5 MV sintered diamond target beam presents a simple modification to the commercial imaging beam which provides improved image quality in full-field CBCT and the potential for simultaneous dose sparing and CNR improvement at high-contrast interfaces using VOI acquisition techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".