TH-D-BRC-01: Improvement of Megavoltage Cone-Beam CT Image Quality Using a Low-Atomic Number X-Ray Target
Bibliographic record
Abstract
Purpose: to investigate the application of an unflattened photon beam, generated using a low atomic number (Z) x-ray target, to MV cone-beam computed tomography (CBCT) imaging. Improvements of image contrast and contrast-to-noise-ratio (CNR) versus dose are quantified and compared to the standard 6MV beam. Limitation of the contrast advantage with patient separation is examined. Method and Materials: The experimental beam was generated by a 2100EX linac (Varian Medical, Inc) by placing a 1.0 cm-thick Al target 9 mm below the primary collimator vacuum window and operating the linac in 6 MeV electron mode. The flattening filtration was removed. Projections were acquired using an AS1000 detector every 2° through 360°. CBCT contrast was compared for both the low-Z-target and 6MV beams. CNR was measured as a function of dose using a bone/lung phantom containing a central ionization chamber. The same phantom was located in cylindrical containers ranging in diameter from 13 cm to 25 cm to measure the rate of reduction of CBCT contrast with separation. Finally, a pig head was imaged allowing a qualitative comparison. Results: Contrast is improved by a factor ranging from 1.8 to 3.4 (mean 2.3) with the low-Z-target beam. Over an imaging dose range from 3 cGy to 23.5 cGy, CNR improves by a consistent factor of 1.7 and 2.4 for bone and lung, respectively. Contrast deteriorates with separation more rapidly for the low-Z-target beam than for 6MV; however for the maximum diameter of 25 cm contrast remains superior by a factor of 1.4 and 1.5 for bone and lung, respectively. Images of the pig head demonstrate qualitatively improved CNR and preservation of spatial resolution. Conclusion: Contrast and CNR are improved significantly in CBCT images using the low-Z-target beam, over a clinically-useful range of patient separation. Conflict of interest: Research sponsored by 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".