A Method for Narrow Field-Of-View Region-Of-Interest Computed Tomography
Bibliographic record
Abstract
This paper presents a method for reconstructing of an internal tomographic image for a region-of-interest (RoI) within a section, using narrowed radiation beams. This reduces radiation exposure, and with the aid of an iterative image reconstruction algorithm, RoI images are reconstructed with a quality comparable to conventional computed tomography (CT) full field-of-view images. Region-of-interest image reconstruction is formulated as a discrete problem to avoid the truncated (incomplete) problem associated with the conventional analytic filtered backprojection method. This in turn allows local reconstruction of RoI images without any prior information or constraints. A coarse image of the entire section is first reconstructed with the aid of a modified convex maximum likelihood (MCML) algorithm. The coarse image is then used to account for the effect of the RoI surroundings. With RoI-specific projections, an RoI image is then reconstructed with the MCML method at the desired pixel size. The proposed method is evaluated using an anthropomorphic thorax phantom, with the heart as its RoI, showing an image quality comparable to that of a conventional CT, but with a about 72% reduction in radiation exposure. Unlike existing approaches that require some a prior knowledge of a segment of the image, this approach does not require any prior image information or any constrains on the solution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".