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Proceedings Paper

Low-dose CT reconstruction assisted by a global CT image manifold prior
Author(s): Chenyang Shen; Guoyang Ma; Xun Jia
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Paper Abstract

The use of X-ray Computed Tomography (CT) leads to the concern of lifetime cancer risk. Low-dose CT scan with reduced mAs can reduce the radiation exposure, but the image quality is usually degraded due to excessive image noise. Numerous studies have been conducted to regularize CT image during reconstruction for better image quality. In this paper, we propose a fully data-driven manifold learning approach. An auto-encoder-decoder convolutional neural network is established to map an entire CT image to the inherent low-dimensional manifold, and then to restore the CT image from its manifold representation. A novel reconstruction algorithm assisted by the leant manifold prior is developed to achieve high quality low-dose CT reconstruction. We perform comprehensive simulation studies using patient abdomen CT images. The trained network is capable of restoring high-quality CT images with average error of ~ 20 HU. The manifold prior assisted reconstruction scheme achieves high-quality low-dose CT reconstruction, with average reconstruction error of ~ 38.5 HU, 4.6 times and 3 times lower than that of filtered back projection method and total-variation based iterative reconstruction method, respectively.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107205 (28 May 2019); doi: 10.1117/12.2534959
Show Author Affiliations
Chenyang Shen, Univ. of Texas Southwestern Medical Ctr. (United States)
Guoyang Ma, Univ. of Texas Southwestern Medical Ctr. (United States)
Westwood High School (United States)
Xun Jia, Univ. of Texas Southwestern Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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