工件(错误)
计算机科学
人工智能
计算机视觉
还原(数学)
降噪
混叠
迭代重建
图像质量
噪音(视频)
图像处理
计算机硬件
图像(数学)
滤波器(信号处理)
数学
几何学
作者
Utkarsh Agrawal,Rajesh Langoju,Yasuhiro Imai,Risa Shigemasa,Bipul Kumar Das
摘要
Pinwheel artifact is caused in multidetector Computed Tomography (CT) helical scans due to under-sampling of z-planes during thin slice reconstruction. There are a few hardware-based solutions like flying focal spot (FFS), which aims at generating more samples during the acquisition, thus enabling aliasing free thin slice images. However, these methods are expensive both in manufacturing capability, as well as impact on hardware life. Deep learning (DL) based methods have shown significant improvement in pin-wheel artifact reduction. Most DL-based methods use images generated from FFS or similar hardware-based enhancement to train the deep learning network, thus restricting usability of these methods on systems without these hardware enhancements. This work proposes a novel DL method to generate pin-wheel free thin-slice images from helical scans for systems not equipped with these hardware capabilities. Artifact-free thin slice images, which are used as targets for artifact reduction network are generated through DL-based super-resolution along z-direction from thick slice images reconstructed from the same scan. The framework is trained with ~16000 coronal/sagittal slices from GE-Revolution system. Clinical image review and statistical analysis of the inferencing results have shown significant artifact reduction and improved diagnostic image quality while reducing noise. A Likert score study shows significant enhancement of proposed method over other image processing-based solutions available.
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