Parallel approach to iterative tomographic reconstruction for high resolution PET imaging
Bibliographic record
Abstract
The authors present a parallel approach to iterative reconstruction algorithms for high resolution 2-D and 3-D PET imaging on a multiprocessor machine where processors are connected by fast Ethernet connections. An efficient parallel implementation of the ML-EM and OS-EM approaches is formulated. It makes use of pre-calculated transition matrices, utilizing the combination of four techniques for storage reduction: (1) a sparse matrix approach, storing only non-negligible values, (2) the elimination of all symmetries, (3) the elimination of matrix elements outside the aperture of the tomograph and (4) the partition of the transition matrix amongst the different processors. This approach can be used in practice for very large 2-D images, as it reduces the storage size of transition matrices per processor by a factor of 8200 for images of 64/spl times/64 up to a factor of 98000 for images of 1024/spl times/1024. A performance analysis of the parallel algorithm is presented. The parallel ML-EM approach with a pre-computed transition matrix can reconstruct 128/spl times/128 pixels images in 0.25 sec/iteration, as opposed to several minutes when the matrix is calculated on the fly by a single processor. Some parametrisations of the parallel OS-FM approach offer total reconstruction times of 2 seconds for 128/spl times/128 pixels images.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".