FPGA Acceleration for Simultaneous Medical Image Reconstruction and Segmentation

The conventional approach of computed tomography (CT) is to solve each image processing task individually in sequence. An obvious drawback is that the measured data is only used once at the first step, and the possible errors, from noises in the measured data, inappropriate modeling, or inappropriate parameters, are not easy to be corrected and will be propagated into the later steps. As a consequence, approaches that combine the reconstruction and the specific processing task have become popular. This work adopts an iterative algorithm with simultaneous reconstruction and segmentation using the Mumford-Shah model, which can be applied not only to regularize the ill-posedness of the tomographic reconstruction problem, but also to compute segmentation directly from the measured data. The Mumford-Shah model is both mathematically and computationally difficult. In this paper, we accelerated this computation and data intensive application by FPGA devices and achieved 9.24X speedup over the conventional CPU implementation.