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Proceedings Paper

A cascaded deep-learning reconstruction method for sparse-view kV-switching dual-energy CT
Author(s): Ruoqiao Zhang; Jian Zhou; Zhou Yu
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Paper Abstract

Dual-energy CT (DECT) has become increasingly popular in practice due to its unique capability of material differentiation. One typical implementation of DECT is to use fast kV switching acquisition technique, which rapidly alternates the X-ray tube voltage between two predetermined kVs in a frequent manner. However, usage of such technique may be limited in practice, as it typically requires sophisticated hardware of high cost and lacks of dose efficiency due to difficulty in tube current modulation. One possible solution is to reduce the frequency of voltage switching during acquisition. However, this alternative approach may potentially compromise the image quality, as it results in sparse measurements for both kVs. In this paper, we proposed a cascaded deep-learning reconstruction framework for sparse-view kV-switching DECT, where two deep convolutional neural networks were employed in the reconstruction, one completing the missing views in the sinogram space and the other improving image quality in the image space. We demonstrated the feasibility of proposed method using sparse-view kV-switching data simulated from rotate-rotate DECT scans with phantom and clinical data. Experimental results show that the proposed method on sparse-view kV-switching data achieve comparable image quality and quantitative accuracy as compared to traditional method on fully-sampled rotate-rotate data

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131223 (16 March 2020); doi: 10.1117/12.2549852
Show Author Affiliations
Ruoqiao Zhang, Canon Medical Research USA, Inc. (United States)
Jian Zhou, Canon Medical Research USA, Inc. (United States)
Zhou Yu, Canon Medical Research USA, Inc. (United States)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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