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Proceedings Paper

Comparison of deep learning approaches to low dose CT using low intensity and sparse view data
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Paper Abstract

Recently there has been considerable interest in using deep learning to improve the quality of low dose CT (LDCT) images. LDCT may be achieved by reducing the beam intensity, or by acquiring sparse-view data at full beam intensity. Additionally, if reducing beam intensity, one can consider denoising either the raw (sinogram) data, or the reconstructed image. We compare the performance of a convolutional neural network (CNN) in improving image quality using three approaches: denoising low-intensity images, denoising low-intensity sinograms prior to reconstruction, and denoising sparse-view images. Our results indicate that images produced from low-intensity data are superior to images produced from sparse-view data, after correction by the CNN. Additionally, in the low-intensity case, denoising in the sinogram or image domain provides comparable image quality.

Paper Details

Date Published: 1 March 2019
PDF: 7 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109484A (1 March 2019); doi: 10.1117/12.2512597
Show Author Affiliations
Thomas Humphries, Univ. of Washington Bothell (United States)
Dong Si, Univ. of Washington Bothell (United States)
Sean Coulter, Univ. of Washington Bothell (United States)
Matthew Simms, Univ. of Washington Bothell (United States)
Ruiwen Xing, Univ. of Washington Bothell (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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