Iterative reconstruction for image enhancement and dose reduction in diagnostic cone beam CT imaging
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Iterative reconstruction is well-established in diagnostic multidetector computed tomography (MDCT) for dose reduction and image quality enhancement. Its application to diagnostic cone beam computed tomography (CBCT) is only emerging and warrants a quantitative evaluation. METHODS: Several phantoms and a canine head specimen were imaged using a commercially available small-field CBCT scanner. Raw projection data were reconstructed using the Feldkamp-Davis-Kress (FDK) method with different filters, including denoising via total variation (TV) minimization (FDK-TV). Iterative reconstruction was carried out using the TV-regularized ordered subsets convex technique (OSC-TV). Signal-to-noise ratio (SNR), noise power spectrum (NPS) and spatial resolution of images were estimated. Dose levels were measured via the weighted computed tomography dose index, while low-dose image quality degradation was estimated via structural similarity (SSIM). RESULTS: OSC-TV and FDK-TV were shown to significantly improve image signal-to-noise ratio (SNR) compared to FDK with a standard filter, 5.8 and 4.0 times, respectively. Spatial resolution attained with different algorithms varied moderately across different experiments. For low-dose acquisitions, image quality decreased dramatically for FDK but not for FDK-TV nor OSC-TV. For low-dose canine head images acquired using about 1/5 of the dose compared to a reference image, SSIM dropped to about 0.3 for FDK, while remaining at 0.92 for FDK-TV and 0.96 for OSC-TV. CONCLUSION: OSC-TV was shown to improve image quality compared to FDK and FDK-TV. Moreover, this iterative approach allowed for significant dose reduction while maintaining image quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it