GPU Based Cone Beam Computed Tomography Reconstruction by the Inexact Alternating Direction Method

GPU based sparse reconstruction shows great significance in cone beam computed tomography (CBCT). This paper proposes a GPU based efficient algorithm for sparse view CBCT reconstruction. The reconstruction problem is converted to a constrained optimization using total variation minimization. The alternating direction method is adopted to solve it efficiently. Furthermore, a linearized proximity and FFT techniques are used for improving computation efficiency. To tackle with the most time consumption of forward and backward projection operation, the GPU hardware acceleration is utilized. The simulation experiments indicate that the new method is able to realize high accuracy reconstruction for CBCT with high speed.