HPC for iterative image reconstruction in CT

Algebraic Reconstruction Techniques (ART) for computed tomography (CT) have proven to produce better images with fewer projections, hence, reducing the side-effects of the carcinogenic nature of X-ray imaging. However, the iterative nature of ART prohibits its commercial use because of the long processing time. Parallel processing through high performance computers (HPC) is one solution to speedup ART algorithm. The work discussed in the literature on parallel computing and CT primarily focuses on the algorithms based on Fourier techniques, with a lack of development of parallel approaches for ART techniques. The main reason for this has been the extensive computational requirements needed for this algorithm. With the boom in information technology and advanced architectures, we show in this paper that the ART algorithm can be parallelized on high performance computers, with significant performance gain while maintaining the image quality. In this paper, we examine the efficiency of ART on a shared memory machine available on the Western Canada Research Grid consortium without impeding image quality. We show that a 6 processor IBM P-server could reconstruct the same image from 36 angles in approximately 5.038 seconds (36 processors is 1.183 seconds), with an efficiency of 93.35%. In other words, a parallel algorithm reconstruction could be done in about the same amount of time as a 180 angle sequential Fourier back projection reconstruction, yielding approximately equivalent image quality, with an 80% reduction in dose.