Investigation of image quality of MV and kV CBCT with low‐Z beams and high DQE detector
Bibliographic record
Abstract
PURPOSE: To investigate cone beam computed tomography (CBCT) image quality using novel combinations of kilovoltage (kV) and megavoltage (MV) beams and detector materials. METHODS: MV and kV CBCT imaging was simulated using the Fastcat hybrid Monte Carlo application. CBCT imaging with various beam energies was investigated: 2.5 and 6 MV photon beams generated with carbon, aluminum, and tungsten targets and a 120 kVp x-ray tube beam based off of a Varian Truebeam on-board imager (OBI). Cadmium tungstate (CWO), gadolinium oxysulfide (GOS), and cesium iodide (CsI) detectors with identical pixel pitch of 0.784 mm were evaluated. Modulation transfer functions (MTF) for all detector/beam combinations were calculated. MV and kV CBCT images for each detector/beam combination of a contrast phantom containing inserts with rib and spongiosa bone, lung, and adipose tissues were simulated with an imaging dose of 7 mGy. Contrast to noise ratio (CNR) of all inserts were compared for all detector/beam combinations. CBCT images of an anthropomorphic head phantom with silver amalgam fillings were also generated. RESULTS: The CWO/120 kVp beam combination resulted in the highest MTF at low frequencies and the CsI detector showed the highest MTF for all other beams and at high frequencies. The CWO/120 kVp beam combination showed the highest CNR for all tissues. The unoptimized CWO/2.5 MV carbon target beam showed the highest CNR of the MV beam/detector combinations with CNR 4% and 17% worse than the optimized Truebeam CsI 120 kVp setup with a bowtie filter and antiscatter grid. Additionally, the CWO 2.5 MV setup showed qualitative reduction of metal artifacts surrounding silver amalgam fillings in an anthropomorphic head phantom. CONCLUSION: This finding makes a compelling case that further optimization of this CWO carbon target setup could produce CBCT images with similar CNR to current OBI CBCT for equivalent dose with added resilience to metal artifacts.
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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) | 0.000 | 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 | 0.001 | 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".