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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. [ABSTRACT FROM AUTHOR]

Reconstruction of super-resolution CT images using deep learning requires a large number of high-resolution images. However, high-resolution images are often limited to access due to CT performance and operation factors. In this paper, a new semi-supervised generative adversarial network is presented to accurately recover high-resolution CT images from low-resolution counterparts. We use a deep unsupervised network of 16 residual blocks to design the generator and build a discriminator based on a supervised network. We also apply a parallel 1 × 1 convolution operation to reduce the dimensionality of each hidden layer's output. Four types of loss functions are presented to build a new one for enforcing the mappings between the generator and discriminator. The bulk specification layer in the commonly used residual network is removed to construct a new type of residual network. In terms of experiments, we conduct an objective and subjective comprehensive evaluation with several state-of-the-art methods. The comparison results show that our proposed network has better advantages in super-resolution image reconstruction. [ABSTRACT FROM AUTHOR]