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Iterative reconstruction for low dose CT using Plug-and-Play alternating direction method of multipliers (ADMM) framework
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Paper Abstract

Concerns over the risks of radiation dose from diagnostic CT motivated the utilization of low dose CT (LdCT). However, due to the extremely low X-ray photon statistics in LdCT, the reconstruction problem is ill-posed and noisecontaminated. Conventional Compressed Sensing (CS) methods have been investigated to enhance the signal-to-noise ratio of LdCT at the cost of image resolution and low contrast object visibility. In this work, we adapted a flexible, iterative reconstruction framework, termed Plug-and-Play (PnP) alternating direction method of multipliers (ADMM), that incorporated state-of-the-art denoising algorithms into model-based image reconstruction. The PnP ADMM framework is achieved by combining a least square data fidelity term with a regularization term for image smoothness and was solved through the ADMM. An off-the-shelf image denoiser, the Block-Matching 3D-transform shrinkage (BM3D) filter, is plugged in to substitute an ADMM module. The PnP ADMM was evaluated on low dose scans of ACR 464 phantom and two lung screening data sets and is compared with the Filtered Back Projection (FBP), the Total Variation (TV), the BM3D post-processing method, and the BM3D regularization method. The proposed framework distinguished the line pairs at 9 lp/cm resolution on the ACR phantom and the fissure line in the left lung, resolving the same or better image details than FBP reconstruction of higher dose scans with up to 18 times less dose. Compared with conventional iterative reconstruction methods resulting in comparable image noise, the proposed method is significantly better at recovering image details and improving low contrast conspicuity.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094906 (15 March 2019); doi: 10.1117/12.2512484
Show Author Affiliations
Qihui Lyu, Univ. of California, Los Angeles (United States)
Dan Ruan, Univ. of California, Los Angeles (United States)
John Hoffman, Univ. of California, Los Angeles (United States)
Ryan Neph, Univ. of California, Los Angeles (United States)
Michael McNitt-Gray, Univ. of California, Los Angeles (United States)
Ke Sheng, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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