A GPU Algorithm in a Distributed Computing System for 3D MRI Denoising
Salvatore Cuomo, Ardelio Galletti, Livia Marcellino · 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC) · 2015
An interesting challenge in E-health is to perform real-time diagnosis. In many distributed computing systems the data processing stage, generally assigned on standard computational CPU environments, is a critical aspect. In particular, the analysis of magnetic resonance imaging (MRI) for improving the quality of images and helping the diagnosis requires an high computational complexity. Using Graphics Processing Units (GPUs) on High Performance Computing (HPC), the images processing step can be accelerated by speeding the whole diagnosis procedure. In this paper, we propose a parallel algorithm, on a GPU environment, for MRI denoising in order to make the diagnostic system more efficient. As case study, we consider the Optimized Blockwise Non Local Means (OB-NLM) method. Its intrinsic nature makes it perfectly suited for parallelization and multithreading implementation, especially for GPUs architectures. The results show a significant improvement of the entire healthcare practice procedure in terms of performances.