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Proceedings Paper

Texture enhanced optimization-based image reconstruction (TxE-OBIR) from sparse projection views
Author(s): Huiqiao Xie; Tianye Niu; Yi Yang; Yi Ren; Xiangyang Tang
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Paper Abstract

The optimization-based image reconstruction (OBIR) has been proposed and investigated in recent years to reduce radiation dose in X-ray computed tomography (CT) through acquiring sparse projection views. However, the OBIR usually generates images with a quite different noise texture compared to the clinical widely used reconstruction method (i.e. filtered back-projection – FBP). This may make the radiologists/physicians less confident while they are making clinical decisions. Recognizing the fact that the X-ray photon noise statistics is relatively uniform across the detector cells, which is enabled by beam forming devices (e.g. bowtie filters), we propose and evaluate a novel and practical texture enhancement method in this work. In the texture enhanced optimization-based image reconstruction (TxEOBIR), we first reconstruct a texture image with the FBP algorithm from a full set of synthesized projection views of noise. Then, the TxE-OBIR image is generated by adding the texture image into the OBIR reconstruction. As qualitatively confirmed by visual inspection and quantitatively by noise power spectrum (NPS) evaluation, the proposed method can produce images with textures that are visually identical to those of the gold standard FBP images.

Paper Details

Date Published: 29 March 2016
PDF: 6 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97834R (29 March 2016); doi: 10.1117/12.2216097
Show Author Affiliations
Huiqiao Xie, Emory Univ. School of Medicine (United States)
Tianye Niu, Zhejiang Univ. (China)
Yi Yang, Emory Univ. School of Medicine (United States)
Yi Ren, Sinovision Technologies (China)
Xiangyang Tang, Emory Univ. School of Medicine (United States)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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