SU‐E‐I‐172: Fast Computation of High Resolution LOR‐Based 3D OSEM PET Algorithm Using the GPU Device
Bibliographic record
Abstract
Purpose: The line‐of‐response OSEM (LOR‐OSEM) algorithm allows a PET image reconstruction from sinograms without any data compression(span=1, mashing=1). The main objective of this work is to accelerate the computation of this algorithm for modern PET scanners such as the Philips Gemini GXL by its implementation on modern GPU devices.Methods: We implemented the LOR‐OSEM algorithm on the NVIDIA Tesla 2050 GPU. The implementation incorporates the attenuation and normalization correction in the sensitivity matrix as weight factors (ANW‐LOR‐OSEM algorithm). The system matrices are built on‐the‐fly by using the multi‐ray Siddon algorithm. We used 3 rays per detector pair in the tangential direction and 2 rays in the axial direction. To reduce this computation time, the symmetries of the scanner were exploited. This implementation was validated using Monte Carlo simulated data with the GATE package.Results: The reconstruction was computed for a 188×188×57 array (FOV=376 mm, 2×2×3.15 mm̂3 voxel size) and for a 144×144×57 array (FOV=576 mm, 4×4×3.15 mm̂3 voxel size). If the sinograms are pre‐corrected for attenuation and detector efficiency, and if the projection data matrix which depends only of the scanner geometry is pre‐calculated, the time to compute the LOR‐OSEM algorithm for 10 subsets, 1 iteration and 112 million coincidences is 30.5 seconds for the 188×188×57 array and 29.4 seconds for the 144×144×57 array. This time is 73.4 seconds for the 188×188×57 array and 72.7 seconds for the 144×144×57 array for the ANW‐LOR‐OSEM algorithm Conclusions: The LOR‐OSEM algorithm was successfully implemented on a Tesla C2050 GPU, including the calculation of the sensitivity matrix, for a PET system that has 85 million LORs. The reported reconstruction times are compatible with a clinical use. The NVIDIA Tesla GPU appears to be a low‐cost, high‐ performance solution for advanced PET reconstruction such as real time 4D gated reconstruction.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".