Abstract
3-D raw data collections introduce noise and artifacts that need to be recovered from degradation by an automated filtering system before further machine analysis. Serving this goal, five performance-efficient FPGA-prototyped processors are devised to realize parallel 3-D "filtering algorithm". These parallel processors tackle the major bottlenecks and limitations of existing multiprocessor systems in input volumetric data, processing word-length, output boundary conditions and inter-processor communications. Then, greyscale 256x256x20 MRI case study are efficiently filtered and improved by a class of common convolution operators and their developed ones respectively. Analytically, the performance of the five implemented processors are evaluated in term of area, speed, dynamic power, and throughput. All five processors efficiently perform in high real-time throughput up to (114 VPS), lowest power consumption of down to (64 mW) at maximum operating frequency. The devised processors can be embedded in mobile MRI or fMRI scanner and as a pre-filtering stage in any portable automated fMRI systems.