Accelerated iterative image reconstruction methods based on block-circulant system matrix derived from a cylindrical image representation
Bibliographic record
Abstract
Iterative image reconstruction methods based on an accurate and fully three-dimensional (3D) system probability matrix are well-known to provide images of higher quality. However, the size of the system matrix and the computation burden often make such methods impractical. To address this problem, we proposed to use a cylindrical image representation that preserves both in-plane and axial symmetries between the tubes of response for a given camera, leading to a system matrix having a block-circulant structure. For 3D image reconstruction, such a system matrix can be structured into a block-circulant matrix where blocks are themselves block-circulant. By storing only non-redundant parts of the block-circulant matrix, memory requirements can be reduced by a factor equivalent to the total number of system symmetries. The block-circulant system matrix can be stored in the Fourier domain representation to accelerate the forward and back projection steps of the iterative image reconstruction methods. When represented in the Fourier domain, the system matrix sparsity is reduced compared to the spatial domain representation, but some null values are still preserved.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".