SU‐FF‐I‐24: The Influence of Bowtie Filtration On Cone‐Beam CT Image Quality
Bibliographic record
Abstract
Purpose: The large variation of x‐ray fluence at the detector across the imaged field‐of‐view in cone‐beam CT causes the loss of skin‐line and reduces CT number accuracy and image uniformity. We report the performance of a bowtie filter (BTF) as a compensator for improved uniformity, skin‐line and CT number accuracy. Method: Image enhancement is performed using a BT filter with a 20cm collimator. The BTF was inserted 30cm from the x‐ray source on an Elekta Synergy XVI. This filter modulates the 2D x‐ray fluence making it non‐uniform across the field‐of‐view. This compensates for the limited attenuation near the skin, resulting in enhancement of skin‐line and uniformity. Two phantoms (1) CatPhan and 2) CatPhan with irregular acrylic annulus (Cat‐Irreg) were scanned on an Elekta CBCT system. The reconstructed images with and without a BTF were analyzed. The images were transformed into polar coordinates to allow quanitfication of radial lag artifacts (radar artifact) for the Cat‐Irreg phantom. Results: The use of the bowtie filter demonstrated a considerable improvement in CT accuracy in the skin‐line region. The uniformity increases 30% for Cat‐Irreg phantom, and skin‐line edges for both phantoms are visible. CT number accuracy with the BT filter improved by 2% for the CatPhan phantom while no improvement was evident for the Cat‐Irreg phantom. The CT♯ linearity for the catphan phantom with the BT filter improved by 8%. In addition, a 45% reduction in the “radar artifact” was observed in the Cat‐Irreg phantom images acquired with the BT filter. Conclusion: The implemented BTF shows improvement in image quality including uniformity and skin‐line reconstruction. This compensator is static and makes many compromises for anatomical imaging site, patient size, and imaged field‐of‐view. The ideal compensator would optimize the fluence profile to account for numerous properties of the patient and imaging system. Research sponsored by Elekta.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".