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Proceedings Paper

Evaluation of image quality of a deep learning image reconstruction algorithm
Author(s): Meghan Yue; Jie Tang; Brian E. Nett; Jiang Hsieh; Roy Nilsen; Jiahua Fan
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Paper Abstract

The iterative reconstruction methods ASiR and ASiR-V have been accepted by hundreds of sites as their standard of care for a variety of protocols and applications. While the reduction in noise has been significant some readers have a preference for the classic image appearance. To maintain the classic image appearance of FBP at the same dose levels used for the standard of care with ASiR-V we introduce, Deep Learning Image Reconstruction (DLIR), a technique using artificial neural networks. This paper demonstrates that DLIR can maintain or improve upon the performance of the conventional iterative reconstruction algorithm (ASiR-V) in terms of low contrast detectability, noise, and spatial resolution.

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, 110722X (28 May 2019); doi: 10.1117/12.2534961
Show Author Affiliations
Meghan Yue, GE Healthcare (United States)
Jie Tang, GE Healthcare (United States)
Brian E. Nett, GE Healthcare (United States)
Jiang Hsieh, GE Healthcare (United States)
Roy Nilsen, GE Healthcare (United States)
Jiahua Fan, GE Healthcare (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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