Accelerating a Medical 3D Brain MRI Analysis Algorithm using a High-Performance Reconfigurable Computer

Many automatic algorithms have been proposed for analyzing magnetic resonance imaging (MRI) data sets. These algorithms allow clinical researchers to generate quantitative 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 study on implementing such quantitative analysis algorithms 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 functions are implemented on FPGA-accelerators. The images resulting from the FPGA-based algorithm are compared to those generated by the CPU-based algorithm for verification. The similarity indexes (SI) for pure tissues are calculated 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 times performance improvement over the CPU-based implementation. The overall performance improvement of the FPGA-accelerated PVE algorithm was 3.5x with four FPGAs.