期刊
MEDICAL IMAGE ANALYSIS
卷 53, 期 -, 页码 179-196出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2019.02.001
关键词
Compressed sensing; Dynamic MRI; Parallel MRI; Image reconstruction; Frequency filter; Multi-scale 3D convolutional sparse coding; Elastic net regularization; Total variation; Genetic algorithm; GPU
类别
资金
- Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) - Korean government, MSIT [NRF-2015M3A9A7029725]
- Next-Generation Information Computing Development Program through the NRF - MSIT [NRF-2016M3C4A7952635]
- Basic Science Research Program through the NRF - Ministry of Education [NRF-2017R1D1A1A09000841]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2016M3C4A7952635] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this paper, we propose a novel image reconstruction algorithm using multi-scale 3D convolutional sparse coding and a spectral decomposition technique for highly undersampled dynamic Magnetic Resonance Imaging (MRI) data. The proposed method recovers high-frequency information using a shared 3D convolution-based dictionary built progressively during the reconstruction process in an unsupervised manner, while low-frequency information is recovered using a total variation based energy minimization method that leverages temporal coherence in dynamic MRI. Additionally, the proposed 3D dictionary is built across three different scales to more efficiently adapt to various feature sizes, and elastic net regularization is employed to promote a better approximation to the sparse input data. We also propose an automatic parameter selection technique based on a genetic algorithm to find optimal parameters for our numerical solver which is a variant of the alternating direction method of multipliers (ADMM). We demonstrate the performance of our method by comparing it with state-of-the-art methods on 15 single-coil cardiac, 7 single-coil DCE, and a multi-coil brain MRI datasets at different sampling rates (12.5%, 25% and 50%). The results show that our method significantly outperforms the other state-of-the-art methods in reconstruction quality with a comparable running time and is resilient to noise. (C) 2019 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据