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Proceedings Paper

Image reconstruction from fully-truncated and sparsely-sampled line integrals using iCT-Net
Author(s): Yinsheng Li; Ke Li; Chengzhu Zhang; Juan Montoya; Guang-Hong Chen
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Paper Abstract

Image reconstruction from line integrals is one of foundations in computed tomography (CT) for medical diagnosis and non-destructive detection purpose. To accurately recover the density function from measurements taken over straight lines, analytic-formula-based or optimization-based inversions have been discovered over the past several decades. Accurate image reconstruction can be achieved if the acquired dataset satisfies data sufficiency conditions and data consistency conditions. However, if these conditions are violated, accurate image reconstruction remains an intellectual challenge provided that significant a priori information about image object and/or physical process of data acquisition need to be incorporated. In this work, we show that a deep learning method based upon a brand new network architecture, termed intelligent CT neural network (iCT-Net), can be employed to discover accurate image reconstruction solutions from fully-truncated and sparsely-sampled line integrals without explicit incorporations of a priori information of either image object or data acquisition process. After a two-stage training, the trained iCT-Net was directly applied to real human subject data to demonstrate the generalizability of iCT-Net to experimental data.

Paper Details

Date Published: 7 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109480S (7 March 2019); doi: 10.1117/12.2513088
Show Author Affiliations
Yinsheng Li, Univ. of Wisconsin-Madison (United States)
Ke Li, Univ. of Wisconsin-Madison (United States)
Chengzhu Zhang, Univ. of Wisconsin-Madison (United States)
Juan Montoya, Univ. of Wisconsin-Madison (United States)
Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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