Fast and memory‐efficient Monte Carlo‐based image reconstruction for whole‐body PET
Bibliographic record
Abstract
PURPOSE: Several studies have shown the benefit of an accurate system modeling using Monte Carlo techniques. For state-of-the-art whole-body positron emission tomography (PET) scanners, Monte Carlo-based image reconstruction is associated with a significant computational cost to calculate the system matrix as well as a large memory capacity to store it. In this article, the authors present a simulation-reconstruction framework to solve these problems on the Philips Gemini GS PET scanner. METHODS: A fast, realistic system matrix simulation module was developed using egs_pet, which is an efficient PET simulation code based on EGSnrc. The generated system matrix was then used in a rotator-based ordered subset expectation maximization (OS-EM) algorithm, which exploits the rotational symmetry of a cylindrical PET scanner. The system matrix was further compressed by using sparse storage techniques. RESULTS: The system matrix simulation took five days on 50 cores of Xeon 2.66 GHz, resulting in a system matrix of 2.01 GB. The entire system matrix could be stored in the main memory of a standard personal computer. The image quality in terms of contrast-noise trade-offs was considerably improved compared to a standard OS-EM algorithm. The image quality was also compared to the clinical software on the scanner using routine parameter settings. The contrast recovery coefficient of small hot spheres and cold spheres was significantly improved. CONCLUSIONS: The results indicated that the proposed framework could be used for this PET scanner with improved image quality. This method could also be applied to other state-of-the-art whole-body PET scanners and preclinical PET scanners with a similar shape.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".