View-interpolation of sparsely sampled sinogram using convolutional neural network

Hoyeon Lee; Jongha Lee; Suengryong Cho

doi:10.1117/12.2254244

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24 February 2017 View-interpolation of sparsely sampled sinogram using convolutional neural network

Hoyeon Lee; Jongha Lee; Suengryong Cho

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Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 1013328 (2017) https://doi.org/10.1117/12.2254244
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Spare-view sampling and its associated iterative image reconstruction in computed tomography have actively investigated. Sparse-view CT technique is a viable option to low-dose CT, particularly in cone-beam CT (CBCT) applications, with advanced iterative image reconstructions with varying degrees of image artifacts. One of the artifacts that may occur in sparse-view CT is the streak artifact in the reconstructed images. Another approach has been investigated for sparse-view CT imaging by use of the interpolation methods to fill in the missing view data and that reconstructs the image by an analytic reconstruction algorithm. In this study, we developed an interpolation method using convolutional neural network (CNN), which is one of the widely used deep-learning methods, to find missing projection data and compared its performances with the other interpolation techniques.

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Hoyeon Lee, Jongha Lee, and Suengryong Cho "View-interpolation of sparsely sampled sinogram using convolutional neural network", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013328 (24 February 2017); doi: 10.1117/12.2254244; https://doi.org/10.1117/12.2254244

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