YRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography
Bibliographic record
Abstract
Image reconstruction for positron emission tomography (PET) requires an accurate model of the PET scanner geometry and degrading factors to produce high-quality and clinically meaningful images. It is typically implemented by scanner manufacturers, with proprietary software designed specifically for each scanner. This limits the ability to perform direct comparisons between scanners or to develop advanced image reconstruction algorithms. Open-source image reconstruction software can offer an alternative to manufacturer implementations, allowing more control and portability. Several existing software packages offer a wide range of features and interfaces, but there is still a need for an engine that simultaneously offers reusable code, fast implementation and convenient interfaces for interoperability and extensibility. In this work, we introduce YRT-PET (Yale Reconstruction Toolkit for Positron Emission Tomography), an open-source toolkit for PET image reconstruction that aims for flexibility, reproducibility, speed, and interoperability with existing research software. The toolkit is implemented in C++ with CUDA-enabled GPU acceleration, relies on a plugin system to facilitate the use with multiple scanners, and offers Python bindings to enable the development of advanced algorithms. It includes support for list-mode/histogram data formats, multiple PET projectors, incorporation of time-of-flight information, event-by-event rigid motion correction, point-spread function modeling. It can incorporate correction factors such as normalization, randoms and scatter, obtained from scanner-specific plugins or provided by the user. The toolkit also includes an experimental module for scatter estimation without time-of-flight. To evaluate the capabilities of the software, two different scanners in four different contexts were tested: dynamic imaging, motion correction, deep image prior, and reconstruction for a limited-angle scanner geometry with time-of-flight. Comparisons with existing tools demonstrated good agreement in image quality and the effectiveness of the correction methods. The proposed software toolkit offers high versatility and potential for research, including the development of novel reconstruction algorithms and new PET scanner systems.
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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 | 0.000 |
| 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".