Share Email Print

Proceedings Paper

Iterative reconstruction of cone-beam breast CT using plug-and-play projected gradient descent
Author(s): Qihui Lyu; Tianye Niu; Dan Ruan; Ke Sheng
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Concerns on the risks of radiation dose in the cone-beam breast CT (CBBCT) motivated the development of low dose CBBCT (LdCBBCT). Due to the noisy and inadequate data acquisition in LdCBBCT, the conventional analytical Filtered Back Projection (FBP) algorithm tends to result in severe image artifacts and overwhelming noise. Model-based iterative reconstruction methods managed to reduce artifacts and enhance the signal-to-noise ratio but were unable to recover many fine structures and low contrast objects pertinent to diagnosis and treatment. To maintain the strengths of the model-based optimization framework and overcome its limitations in signal recovery, we adapted a CNN-based iterative reconstruction framework, termed Plugand-Play (PnP) proximal gradient descent (PGD) framework, that incorporated state-of-the-art deep-learningbased denoising algorithms with model-based image reconstruction. The PnP-PGD framework is achieved by combining a least-square data fidelity term for data consistency with a non-local regularization for image smoothness, which was solved via PGD. A deep convolutional neural network (DCNN) was plugged in to substitute the proximal operator of the regularization term. The PnP-PGD was evaluated on LdCBBCT scans of a breast phantom and was compared with Filtered Back Projection (FBP), Total Variation (TV), the BlockMatching 3D-transform shrinkage (BM3D), and the DCNN based post-processing method. Compared with FBP, iterative reconstruction, and BM3D, the proposed PnP-PGD substantially reduced image noise and artifacts. Compared with the DCNN based post-processing method, the PnP-PGD improved image contrast-tonoise ratio (CNR). The proposed PnP-PGD takes advantage of both model-based reconstruction and deeplearning-based denoisers, showing improved image quality.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124J (16 March 2020); doi: 10.1117/12.2549787
Show Author Affiliations
Qihui Lyu, Univ. of California, Los Angeles (United States)
Tianye Niu, Zhejiang Univ. School of Medicine and The Institute of Translational Medicine (China)
Dan Ruan, Univ. of California, Los Angeles (United States)
Ke Sheng, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?