MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)

Xi Peng; Dong Liang

doi:10.1109/LSP.2014.2376699

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MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)

Journal article

Peer reviewed

MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)

Xi Peng and Dong Liang

IEEE signal processing letters, Vol.22(8), pp.1184-1188

08/01/2015

DOI: 10.1109/LSP.2014.2376699

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Abstract

The problem of recovering an image from limited or sparsely sampled Fourier measurements occurs in the application of magnetic resonance imaging. To address this problem, we propose a novel MR image reconstruction method with convolutional characteristic constraints. We first estimate the convolutional characteristics using standard compressed sensing method in a parallel fashion. Then we use the recovered image characteristics to constrain the target image function. The image characteristics should either be sparser or of higher SNR than the original image to enable superior performance. In this work, we studied using thirteen kernels and experiments based on a brain data set were conducted. It is demonstrated that the proposed method outperforms the existing methods in terms of high quality imaging due to multiple characteristic constraints and the robustness to measurement noise.

Engineering

Engineering, Electrical & Electronic

Science & Technology

Technology

Details

Title: Subtitle: MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)
Creators: Xi Peng - Shenzhen Inst Adv Technol, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen 518055, Guangdong, Peoples R China
Dong Liang - National Center for Mathematics and Interdisciplinary Sciences
Resource Type: Journal article
Publication Details: IEEE signal processing letters, Vol.22(8), pp.1184-1188
Publisher: IEEE
DOI: 10.1109/LSP.2014.2376699
ISSN: 1070-9908
eISSN: 1558-2361
Number of pages: 5
Grant note: 2011CB707903 / National Basic Research Program 973; National Basic Research Program of China KQCX20120816155710259 / Shenzhen Peacock Plan 11301508; 61471350; 61102043; 81120108012 / National Nature Science Foundation of China; National Natural Science Foundation of China (NSFC)
Language: English
Date published: 08/01/2015
Academic Unit: Radiology
Record Identifier: 9984446281502771

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