Abstract

Recently, cone-beam computed tomography (CBCT) equipped with image-guided radiation therapy (IGRT) has been used to precisely identify the location of target lesion. However, the treatment accuracy for respiratory-sensitive regions is still low due to long scanning time and target motion, and the imaging dose is also relatively high compared to other imaging systems. These issues can be solved by using 4D IGRT with digital tomosynthesis (DT). Also, an application of graphics processing unit (GPU) computing can reduce reconstruction time. The purpose of this study was to develop a 4D DT imaging technique for IGRT and compare image quality between 3D DT and 4D DT. And, we also compared reconstruction time between CPU and GPU computing. Projections were separately obtained through 3 phases for simulating 4D DT imaging. We measured profile, normalized root-mean-square error (NRMSE), contrast-to-noise ratio (CNR), noise-power spectrum (NPS) and figure-of-merit (FOM). NRMSEs and CNRs of 4D DT images were averagely 0.56 and 0.74 times lower than those of 3D DT images, respectively. The NPSs of 4D DT images were higher than 3D DT images. The FOMs of 4D DT images were averagely 1.65 times higher than 3D DT images. The GPU computing reduced reconstruction time in 3D and 4D imaging by a factor of 0.11 and 0.56, respectively. As a results, the 4D DT imaging enables the high-precision IGRT for respiratory-sensitive regions, and the real-time 4D imaging would be possible by using the GPU computing.

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