Evaluation of accelerated iterative X-ray CT image reconstruction using floating point graphics hardware

Statistical reconstruction methods offer possibilities to improve image quality as compared with analytical methods, but current reconstruction times prohibit routine application in clinical and micro-CT. In particular for cone-beam X-ray CT, the use of graphics hardware has been proposed to accelerate the forward and back-projection operations, in order to reduce reconstruction times. In the past, wide application of this texture hardware mapping approach was hampered owing to limited intrinsic accuracy. Recently, however, floating point precision has become available in the latest generation commodity graphics cards. In this paper, we implement this feature to construct a graphics hardware accelerated version of the ordered subset convex reconstruction algorithm. The aim of this paper is to study the impact of using graphics hardware acceleration for statistical reconstruction on the reconstructed image accuracy. We compare the unaccelerated algorithm, with the graphics hardware accelerated version, and for the latter we consider two different interpolation techniques. A simulation study of a micro-CT scanner with a mathematical phantom shows that at almost preserved reconstructed image accuracy speed-ups of one to two orders of magnitude can be achieved, compared with the unaccelerated algorithm, and depending on the phantom and detector sizes. Reconstruction from physical phantom data reconfirms the usability of the accelerated algorithm for practical cases.