YRT-PET: An Open-Source GPU-Accelerated Image Reconstruction Engine for Positron Emission Tomography

• Published in: IEEE transactions on radiation and plasma medical sciences p. 1
• Main Authors: Najmaoui, Yassir, Chemli, Yanis, Toussaint, Maxime, Petibon, Yoann, Marty, Baptiste, Fontaine, Kathryn, Gallezot, Jean-Dominique, Razdevsek, Gasper, Orehar, Matic, Dhaynaut, Maeva, Guehl, Nicolas, Dolenec, Rok, Pestotnik, Rok, Johnson, Keith, Ouyang, Jinsong, Normandin, Marc, Tetrault, Marc-Andre, Lecomte, Roger, Fakhri, Georges El, Marin, Thibault
• Format: Journal Article
• Language: English
• Published: IEEE 11.10.2025
• Subjects: Biomedical imaging; C++; Engines; Geometry; graphics processing unit (GPU); Graphics processing units; Image reconstruction; Plasmas; Positron emission tomography; positron emission tomography (PET); Python; Software; Software algorithms
• ISSN: 2469-7311; 2469-7303
• DOI: 10.1109/TRPMS.2025.3619872

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.