Iterative quality enhancement via residual-artifacts learning networks for low-dose CT.

Iterative quality enhancement via residual-artifacts learning networks for low-dose CT. Phys Med Biol. 2018 Sep 28;: Authors: Wang Y, Zeng D, Zhang Y, He J, Li S, Liao Y, Bian Z, Zhang H, Gao Y, Meng D, Zuo W, Ma J Abstract Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering X-ray tube current (mA). However, this strategy may lead to excessive noise and streak artifacts in the conventional filtered back-projection reconstructed images. To address this issue, some deep convolutional neural network (ConvNet) based approaches have been developed for low-dose CT imaging inspired by the recent development of machine learning. Nevertheless, some of the image textures reconstructed by the ConvNet could be corrupted by the severe streaks especially in ultra-low-dose cases, which could be close to prosthesis and hamper diagnosis. Therefore, in this work, we propose an iterative residual-artifacts learning ConvNet (IRLNet) approach to improve the reconstruction performance compared to the ConvNet based approaches. Specifically, the proposed IRLNet estimates the high-frequency details within the noise and then removes them iteratively, after eliminating severe streaks in the low-dose CT images, the residual low-frequency details can be processed through the conventional network. Moreover, the proposed...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research