A GPU-Accelerated MRI Simulator for Synthetic Image Generation

Magnetic Resonance Imaging (MRI) is an imaging technique that provides detailed structural and functional information about organs and tissues. To support the validation of MRI measurement techniques and provide a tool for scanner benchmarking, we develop an MRI simulator exploiting graphics processing units (GPUs) for computational acceleration. The GPU-Accelerated MRI Simulator (GAMS) enables us to generate synthetic images with high-resolution detail for quantitative estimation of relaxation times. GAMS is designed to reproduce the entire MRI image acquisition pipeline, including the definition of the MRI pulse sequence in a standard Pulseq format, the input of the digital phantom and its voxelization, the time integration of the Bloch equation at voxel level, and the processing of the derived k-space for image synthesis. The Bloch equation solver is implemented in CUDA Fortran, using GPUs to increase computational efficiency. The solver accuracy is tested in simple cases by comparison with analytical solutions and with the open-source Jülich Extensible MRI Simulator (JEMRIS). GAMS is applied to an inverse reconstruction problem to estimate the relaxation properties of a calibration phantom, starting with synthetic data. Then, it is used to reproduce the imaging process of a high-resolution digital brain phantom, generating realistic images under diverse MRI pulse sequences, which enable differentiation of tissues based on relaxation properties and/or proton density. The results demonstrate GAMS's ability to generate synthetic images of complex anatomical structures comparable to those acquired with real MRI scanners and to reliably quantify sample relaxation properties. The simulator is also characterized by high computational efficiency, extensibility for multi-GPU implementation, and cross-platform compatibility. These features make GAMS a valuable tool for the metrological assessment of quantitative MRI (qMRI) and the testing of new pulse sequences for qMRI technique development.