FPGA Acceleration for Simultaneous Medical Image Reconstruction and Segmentation

Abstract

X-ray computed tomography is an important technique for clinical diagnose and non destructive testing. In many applications a number of image processing steps are needed before the image information can be used. Obtaining a segmentation of the image is one such image processing step and also is important for applications. The conventional approach is to first reconstruct the image and conduct image segmentation by other image processing methods afterwards. An emerging technique is to obtain the tomographic images and image segmentation simultaneously. An iterative algorithm with simultaneous reconstruction and segmentation using Mumford-Shah model has been proposed, which can be applied not only to regularize the ill-posedness of the tomographic reconstruction problem, but also to provide the image segmentation. The Mumford-Shah model is both mathematically and computationally difficult. In this paper, we accelerate the proposed algorithm with simultaneous reconstruction and segmentation using the Mumford-Shah model by FPGA devices. The algorithm is hand-optimized with both algorithmic domain knowledge and platform-specific information before translated into FPGA implementation using high-level synthesis and other electronic system-level design tools. A high-level performance model is used to guide the design and optimization process at early stages. The computational kernel and frequent invoked Radon transformation is parallelized by tiling the entire image to sub-images. Other optimization techniques including loop pipelining, loop merging, data streaming and computation sharing across computation modules are used to improve the performance. Intensive optimizations are also adopted to maximize the use of FPGA on-chip block RAMs against off-chip DRAMs to increase memory bandwidth. Experimental results show that a 9.24X speedup can be achieved by the FPGA accelerator over the CPU implementation for this computation and data intensive application.