Constrained Bayesian streak artifact reduction approach for contrast enhanced computed tomography imaging of the intervertebral disc
Bibliographic record
Abstract
A promising approach for the study of progressive herniation damage of the intervertebral discs under flexion/extension motions as well as compressive loads is the use of contrast-enhanced computed tomography (CECT). One of the biggest limitations of using CECT is the presence of significant streak artifacts in the acquired tomograms, due primarily to the contrast agent injected into the intervertebral disc. To address this issue, a novel constrained Bayesian approach to streak artifact reduction in CECT imagery is introduced in this paper. The problem of artifact reduction is formulated as a constrained Bayesian estimation problem in projection space, and a non-parametric Parzen window estimation approach is employed to estimate the underlying posterior distributions. Experimental results show that the proposed approach provides significant artifact reduction while preserving the intervertebral disc regions to allow for clear visualization of progressive intervertebral disc damage.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".