IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

Phantoms are essential for assessing PET system performance, but most existing brain phantoms do not provide the anatomical fidelity required for the next-generation high-resolution PET systems. To address this limitation, we developed HYDRA-OL (HYDrodynamically filled Realistic Anatomy of the Occipital Lobe) phantoms using high-resolution 3D printing based on the BigBrain atlas. In this study, two phantom types were created, HYDRA-OL_G (gray matter only) and HYDRA-OL_GW (gray and white matter), enabling contrast modulation between gray matter (GM) and white matter (WM). Precise anatomical fidelity and repositioning accuracy were confirmed via CT. A custom closed-loop filling apparatus ensured efficient and reproducible phantom preparation. Residual air in the phantom after 15 minutes was estimated to be less than 0.01% of the fillable space. To compare spatial resolution between scanners, the HYDRA-OL_G phantom was imaged on four different PET systems (NeuroEXPLORER (NX), HRRT, Vision, and Focus220), with NX and Focus showing superior spatial resolution compared to the other two systems. To generate variable GM:WM contrast, list mode datasets obtained on the NX for both HYDRA-OL_G and HYDRA-OL_GW phantoms were down-sampled and merged to form one list mode data. Reconstructed images were successfully produced with GM:WM contrast ratio of 4:1, 2:1, and 4:3. Overall, the HYDRA phantom platform is a versatile tool for accurately representing human brain anatomy. It is likely to be more useful than previous phantoms for evaluating image resolution and optimizing reconstruction parameters for next-generation brain PET systems.

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.