Design of iterative ROI transmission tomography reconstruction procedures and image quality analysis
Bibliographic record
Abstract
PURPOSE: An iterative edge-preserving CT reconstruction algorithm for high-resolution imaging of small regions of the field of view is investigated. It belongs to a family of region-of-interest reconstruction techniques in which a low-cost pilot reconstruction of the whole field of view is first performed and then used to deduce the contribution of the region of interest to the projection data. These projections are used for a high-resolution reconstruction of the region of interest (ROI) using a regularized iterative algorithm, resulting in significant computational savings. This paper examines how the technique by which the pilot reconstruction of the full field of view is obtained affects the total runtime and the image quality in the region of interest. METHODS: Previous contributions to the literature have each focused on a single approach for the pilot reconstruction. In this paper, two such approaches are compared: the filtered backprojection and a low-resolution regularized iterative reconstruction method. ROI reconstructions are compared in terms of image quality and computational cost over simulated and physical phantom (Catphan600) studies, in order to assess the compromises that most impact the quality of the ROI reconstruction. RESULTS: With the simulated phantom, new artifacts that appear in the ROI images are caused by significant errors in the pilot reconstruction. These errors include excessive coarseness of the pilot image grid and beam-hardening artifacts. With the Catphan600 phantom, differences in the imaging model of the scanner and that of the iterative reconstruction algorithm cause dark border artifacts in the ROI images. CONCLUSIONS: Inexpensive pilot reconstruction techniques (analytical algorithms, very-coarse-grid penalized likelihood) are practical choices in many common cases. However, they may yield background images altered by edge degradation or beam hardening, inducing projection inconsistency in the data used for ROI reconstruction. The ROI images thus have significant streak and speckle artifacts, which adversely affect the resolution-to-noise compromise. In these cases, edge-preserving penalized-likelihood methods on not-too-coarse image grids prove to be more robust and provide the best ROI image quality.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".