ACCELERATING A MEDICAL 3D BRAIN MRI ANALYSIS ALGORITHM USING A HIGH-PERFORMANCE RECONFIGURABLE COMPUTER

Many automatic algorithms have been proposed for an-alyzing magnetic resonance imaging (MRI) data sets. These algorithms allow clinical researchers to generate quantita-tive data analyses with consistently accurate results. With the increasingly large data sets being used in brain mapping, there has been a significant rise in the need for methods to accelerate these algorithms, as their computation time can consume many hours. This paper presents the results from a recent study on implementing such quantitative analysis al-gorithms on High-Performance Reconfigurable Computers (HPRCs). A brain tissue classification algorithm for MRI, the Partial Volume Estimation (PVE), is implemented on an SGI RASC RC100 system using the Mitrion-C High-Level Language (HLL). The CPU-based PVE algorithm is profiled and computationally intensive floating-point func-tions are implemented on FPGA-accelerators. The images resulting from the FPGA-based algorithm are compared to those generated by the CPU-based algorithm for verifica-tion. The Similarity Indexes (SI) for pure tissues are calcu-lated to measure the accuracy of the images resulting from the FPGA-based implementation. The portion of the PVE algorithm that was implemented on hardware achieved a 11× performance improvement over the CPU-based implemen-tation. The overall performance improvement of the FPGA-accelerated PVE algorithm was 3.5 × with four FPGAs. 1