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Reference-Guided Sparsifying Transform Design for Compressive Sensing MRI
Conference proceeding

Reference-Guided Sparsifying Transform Design for Compressive Sensing MRI

S. Derin Babacan, Xi Peng, Xian-Pei Wang, Minh N. Do and Zhi-Pei Liang
2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Vol.2011, pp.5718-5721
IEEE Engineering in Medicine and Biology Society Conference Proceedings
01/01/2011
DOI: 10.1109/IEMBS.2011.6091384
PMCID: PMC3769212
PMID: 22255638
url
https://www.ncbi.nlm.nih.gov/pmc/articles/3769212View
Open Access

Abstract

Compressive sensing (CS) MRI aims to accurately reconstruct images from undersampled k-space data. Most CS methods employ analytical sparsifying transforms such as total-variation and wavelets to model the unknown image and constrain the solution space during reconstruction. Recently, nonparametric dictionary-based methods for CS-MRI reconstruction have shown significant improvements over the classical methods. These existing techniques focus on learning the representation basis for the unknown image for a synthesis-based reconstruction. In this paper, we present a new framework for analysis-based reconstruction, where the sparsifying transform is learnt from a reference image to capture the anatomical structure of unknown image, and is used to guide the reconstruction process. We demonstrate with experimental data the high performance of the proposed approach over traditional methods.
Engineering Technology Engineering, Biomedical Engineering, Electrical & Electronic Science & Technology

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