GPU-based Low-dose 4DCT Reconstruction via Temporal Non-local Means

Abstract

Four-dimensional computed tomography (4DCT) has been widely used in cancer radiotherapy for accurate target delineation and motion measurement for tumors in thorax and upper abdomen areas. However, 4DCT simulation is associated with much higher imaging dose than conventional CT simulation, which is a major concern in its clinical application. Conventionally, each phase of 4DCT is reconstructed independently using the filtered backprojection (FBP) algorithm. The basic idea of our new algorithm is that, by utilizing the common information among different phases, the input information required to reconstruct image of high quality, and thus the imaging dose, can be reduced. We proposed a temporal non-local means (TNLM) method to explore the inter-phase similarity. All phases of the 4DCT images are reconstructed simultaneously by minimizing a cost function consisting of a data fidelity term and a TNLM regularization term. We utilized a forward-backward splitting algorithm and a Gauss-Jacobi iteration method to efficiently solve the minimization problem. The algorithm was also implemented on graphics processing unit (GPU) to achieve a high computational speed. Our reconstruction algorithm has been tested on a digital NCAT thorax phantom in three low dose scenarios. Our new algorithm generates visually much better CT images containing less image noise and streaking artifacts compared with the standard FBP algorithm. Quantitative analysis shows that much higher contrast-to-noise ratio and signal-to-noise ratio can be obtained using our algorithm. The total reconstruction time for all 10 phases of a slice ranges from 90 to 140 seconds on an NVIDIA Tesla C1060 GPU card.