Influence of Field of View and Bowtie Filtration on Cone Beam Computed Tomography Image Quality and Scatter-to-Primary Ratio
Bibliographic record
Abstract
Purpose/Aim: The image quality (IQ) of cone-beam computed tomography (CBCT) is often reduced due to X-ray scatter, causing issues such as shading, skin-line artifacts, decreased contrast-to-noise ratio, and inaccurate computed tomography (CT) numbers. This study establishes six metrics for assessing IQ, focusing on both traditional metrics, such as contrast-to-noise ratio, and clinically relevant measures of CT signal accuracy. Using a commercial CBCT system for image-guided radiation therapy (IGRT), the study examines how these metrics vary with axial field-of-view (FOV z ) and bowtie filter use to understand the effects of X-ray scatter on IQ. Materials and Methods: Catphan-600 phantom was scanned at five longitudinal FOV z settings (2–27 cm, Superior-Inferior) with and without a bowtie filter, and all software-based scatter corrections were disabled. Six metrics were evaluated: shading (m shading ), periphery accuracy (m periphery ), noise (m noise ), contrast-to-noise ratio (m CNR ), CT number accuracy (m CT# ), and linearity (m linearity ). Results: All six metrics demonstrated a notable decline in IQ as the FOV z increased from 2 to 27 cm. Specifically, the CNR decreased by half, while m shading increased by 250 HU. The bowtie filter improved CT number accuracy at the periphery by approximately 100–140 HU, partially mitigating the impact of a larger FOV z on IQ. Conclusions: As the FOV z increases, quantitative assessments reveal significant artifacts. Using a bowtie filter improves CNR and CT number accuracy while reducing shading and skin-line artifacts. For enhanced IQ in clinical therapy, minimizing the FOV z is recommended. The evaluation framework established in this study provides a valuable tool for system comparison and assessing scatter correction techniques, aiding in accurate low-contrast detection and supporting advancements in online and adaptive radiotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".