Fully 3D iterative CT reconstruction using polar coordinates
Bibliographic record
Abstract
PURPOSE: This paper demonstrates the feasibility of fully 3D iterative computed tomography reconstruction of highly resolved fields of view using polar coordinates. METHODS: System matrix is computed using a ray-tracing approach in cylindrical or spherical coordinates. By using polar symmetries inherent to the acquisition geometry, the system matrix size can be reduced by a factor corresponding to the number of acquired projections. Such an important decrease in size allows the system matrix to be precomputed, and loaded all at once into memory prior to reconstruction. By carefully ordering the field of view voxels and the sinogram data, reconstruction speed is also enhanced by a cache-oblivious computer implementation. The reconstruction algorithm is also compatible with the ordered-subsets acceleration method. A final polar-to-Cartesian transformation is applied to the reconstructed image in order to allow proper visualization. RESULTS: The ray-tracing and reconstruction algorithms were implemented in polar representation. Large 3D system matrices were calculated in cylindrical and spherical coordinates, and the performance assessed against Cartesian ray-tracers in terms of speed and memory requirements. Images of analytical phantoms were successfully reconstructed in both cylindrical and spherical coordinates. Fully 3D images of phantoms and small animals were acquired with a Gamma Medica Triumph X-O small animal CT scanner and reconstructed using the manufacturer's software and the proposed polar approach to demonstrate the accuracy and robustness of the later. The noise was found to be reduced while preserving the same level of spatial resolution, without noticeable polar artifacts. CONCLUSIONS: Under a reasonable set of assumptions, the memory size of the system matrix can be reduced by a factor corresponding to the number of projections. Using this strategy, iterative reconstruction from high resolution clinical and preclinical systems can be more easily performed using general-purpose personal computers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".