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28 May 2019 Evaluation of image quality of a deep learning image reconstruction algorithm
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Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110722X (2019) https://doi.org/10.1117/12.2534961
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Meghan Yue, Jie Tang, Brian E. Nett, Jiang Hsieh, Roy Nilsen, and Jiahua Fan "Evaluation of image quality of a deep learning image reconstruction algorithm", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110722X (28 May 2019); https://doi.org/10.1117/12.2534961
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