ArticleIEEE transactions on radiation and plasma medical sciences2025
YRT-PET: An Open-Source GPU-accelerated Image Reconstruction Engine for Positron Emission Tomography.
Article in IEEE transactions on radiation and plasma medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Impact of depth-of-interaction and spatially-variant point-spread-function model on the performance of ultra-high resolution brain PET.Physics in medicine and biology · 2026Article
- Attenuation Pre-Correction in Iterative PET Image Reconstruction: What is the Statistical Cost?IEEE transactions on radiation and plasma medical sciences · 2026Article
Corrections and comments
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Authors and funding
20 authors.
Funding
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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Registered trials
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