Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine

3D GPU-based image reconstruction algorithm for the application in a clinical organ-targeted PET camera

Functional medical imaging is unique in its ability to visualize molecular interactions and pathways in the body. Organ-targeted Positron Emission Tomography (PET) is a functional imaging technique that has emerged to meet the demands of precision medicine and has shown advantages in terms of sensitivity and image quality compared to whole-body (WB) PET. A common application for organ-targeted PET is oncology, particular breast cancer imaging. In this work we present the application of Graphics Processing Unit (GPU) to significantly accelerate reconstruction of clinical breast images acquired with an organ-targeted PET camera and reconstructed using the Maximum Likelihood Estimation Maximization (MLEM) algorithm. The PET camera is configured with two planar detector heads with a sensing area of 232mm×174mm. Acquired raw image data are converted into list mode format and reconstructed by a GPU-based 3D MLEM algorithm that was developed specifically for the limited-angle capabilities of the planar PET geometry. The algorithm applies corrections including attenuation and scatter to provide clinical grade image quality. We demonstrate that a transition from originally developed Central Processing Unit (CPU) to newly developed GPU-based algorithm improves single iteration speed by more than 400 times, while preserving image quality. The latter has been assessed on clinical data and through phantom tests performed according to the National Electrical Manufacturers Association (NEMA) NU-4 standards. The gain in reconstruction speed is expected to result in improved patient throughput capabilities of the clinical organ-targeted PET. Indeed, GPU-based image reconstruction reduces time needed for a typical breast image reconstruction to less than 5 minutes thus making it shorter than the image acquisition time. This is of particular importance in improving patient throughput and clinical adaptation of the PET system