@inproceedings{dd5dd2f341fb4d6a86dd26a50d8ce4ab,
title = "Accelerated augmented Lagrangian method for few-view CT reconstruction",
abstract = "Recently iterative reconstruction algorithms with total variation (TV) regularization have shown its tremendous power in image reconstruction from few-view projection data, but it is much more demanding in computation. In this paper, we propose an accelerated augmented Lagrangian method (ALM) for few-view CT reconstruction with total variation regularization. Experimental phantom results demonstrate that the proposed method not only reconstruct high quality image from few-view projection data but also converge fast to the optimal solution.",
keywords = "Augmented Lagrangian method, CT reconstruction, Total variation (TV), few-view",
author = "Junfeng Wu and Xuanqin Mou",
year = "2012",
doi = "10.1117/12.911783",
language = "英语",
isbn = "9780819489623",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
booktitle = "Medical Imaging 2012",
note = "Medical Imaging 2012: Physics of Medical Imaging ; Conference date: 05-02-2012 Through 08-02-2012",
}