YRT-PET: An Open-Source GPU-accelerated Image Reconstruction Engine for Positron Emission Tomography
Why this work is in the frame
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Bibliographic record
Abstract
Github: https://github.com/YaleBioImaging/yrt-pet Read the Docs: https://yrt-pet.readthedocs.io/ Image reconstruction for positron emission tomography (PET) is typically implemented by manufacturers, specifically for a given family of scanners, which limits the ability to perform direct comparisons between scanners, and to develop scanner-agnostic 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 and Python bindings. 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, normalization correction, and corrections for degrading factors such as randoms and scatter. 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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it