Spatial resolution recovery utilizing multi-ray tracing and graphic processing unit in PET image reconstruction
Bibliographic record
Abstract
1821 Objectives We aim to implement resolution modeling utilizing multi-ray tracing on the GPU platform to reduce the depth-of-interaction (DOI) effect and improve processing speed Methods Multi-ray tracing was used to trace multiple rays from the virtually created DOI layers considering the effect of crystal penetration to calculate the system matrix. Each ray was processed by a processing unit on the GPU in parallels to increase processing speed. The proposed method was tested on a PET ring insert being developed in our group. 3 different cases were tested: 1) no physical DOI or resolution modeling; 2) two physical DOI layers and no resolution modeling; and 3) no physical DOI design with different number of virtual DOI layers. The proposed method was first validated against the Monte Carlo simulation, including the dependency of accuracy on the number of virtual DOI layers. Second, the spatial resolution performances were compared among the 3 cases, in terms of the resolution FWHM of the sphere phantoms across the field-of-view. Third, contrast and noise performances were studied. Results The results based upon the analytical modeling are in good consistency with the Monte Carlo simulation results. The spatial resolution of the image was improved with the modeling, especially when more virtual DOI layers were modeled. For instance, the FWHM values are 2.4 mm, 1.6mm and 1.23mm when the number of layers increase from 2 to 4. Similar effect was observed for the contrast result, and the noise could be reduced with the modeling. The results indicate that the proposed method has the potential to be used as an alternative to other physical DOI designs and achieve comparable imaging performances, while reducing detector/system design cost and complexity. Conclusions The proposed method is able to improve both spatial resolution and contrast performance, comparable to those achieved by a PET system with two physical DOI layers. The implementation using the GPU platform and CUDA programming helps speed up the system matrix modeling task and PET image reconstruction on-the-fly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".