Dose Images Reconstruction Based on X-ray-Induced Acoustic Computed Tomography

Abstract

The accurate reconstruction of the in vivo dose is a critical step in radiation therapy. X-ray-induced acoustic imaging is a promising technology for in vivo dose reconstruction, as it enables the nonradiative and noninvasive monitoring of radiation dose. However, current X-ray acoustic imaging methods suffer from several limitations, including high signal-to-noise ratio, poor imaging quality and massive loss of structural information. To address these limitations, we propose a dose image reconstruction method based on tensor sparse dictionary learning. Specifically, we combine tensor coding with compressed sensing data, extend two-dimensional dictionary learning to three-dimensional by using tensor product, and then utilize the spatial information of X-ray acoustic signal more efficiently. To reduce the artifacts of reconstruction images caused by spare sampling, we design the alternate iterative solution of the tensor sparse coefficient and tensor dictionary. In addition, we build the X-ray-induced acoustic dose images reconstruction system, simulate the X-ray acoustic signals based on patients’ information from Sichuan Cancer Hospital, and then create the simulated datasets. Compared to some typical state-of-the art imaging methods, the experimental results demonstrate that our method can significantly improve the quality of reconstructed images and the accuracy of dose distribution.