Fast GPU-based computation of spatial multigrid multiframe LMEM for PET

A modified orthogonal-distance ray-tracer method applied to dual rotation PET systems

Objective.a new projector, orthogonal-distance ray-tracer varying-full width at half maximum (OD-RT-VF), was developed to model a shift-variant elliptical point-spread function (PSF) response to improve the image quality (IQ) of a preclinical dual-rotation PET system.Approach.the OD-RT-VF projector models different FWHM values of the PSF in multiple directions, using half-height and half-width tube-of-response (ToR) values. The OD-RT-VF method's performance was evaluated against the original OD-RT method and a ToR model with constant response. The evaluation involved simulations of NEMA NU 4-2008 IQ and Derenzo phantoms, as well as a real mouse injected with [18F]-NaF scanned with the easyPET.3D system.Main results.the OD-RT-VF method demonstrated superior image resolution and uniformity (11.9% vs 15.9%) compared to the OD-RT model. In micro-derenzo phantom simulations, it resolved rods down to 1.0 mm, outperforming the other methods. For IQ phantom simulations, the OD-RT-VF projector at convergency achieved hot rods recovery coefficients ranging from 22.4% to 93.3% and lower spillover ratios in cold regions of 0.22 and 0.33 for air and water, respectively. For bone radiotracer imaging, OD-RT-VF produced clearer images of major skeletal parts, with less noise compared to OD-RT and better resolution compared to ToR projectors.Significance.the study shows that the OD-RT-VF projector method enhances PET imaging by providing better resolution, uniformity, and IQ. This model, in addition to a list-mode and GPU-based reconstruction addressing the data sparsity of dual-rotation PET geometries, unlocks their imaging potential for small animal imaging.

PARALLELPROJ-an open-source framework for fast calculation of projections in tomography

In this article, we introduce parallelproj, a novel open-source framework designed for efficient parallel computation of projections in tomography leveraging either multiple CPU cores or GPUs. This framework efficiently implements forward and back projection functions for both sinogram and listmode data, utilizing Joseph's method, which is further extended to encompass time-of-flight (TOF) PET projections. Our evaluation involves a series of tests focusing on PET image reconstruction using data sourced from a state-of-the-art clinical PET/CT system. We thoroughly benchmark the performance of the projectors in non-TOF and TOF, sinogram, and listmode employing multi CPU-cores, hybrid CPU/GPU, and exclusive GPU mode. Moreover, we also investigate the timing of non-TOF sinogram projections calculated in STIR (Software for Tomographic Image Reconstruction) which recently integrated parallelproj as one of its projection backends. Our results indicate that the exclusive GPU mode provides acceleration factors between 25 and 68 relative to the multi-CPU-core mode. Furthermore, we demonstrate that OSEM listmode reconstruction of state-of-the-art real-world PET data sets is achievable within a few seconds using a single consumer GPU.

A review of GPU-based medical image reconstruction

Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing.