NDLI: Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity

Content Provider	PubMed Central
Author	Zhao, Di Du, Huiqian Han, Yu Mei, Wenbo
Copyright Year	2014
Abstract	Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively.
Related Links	http://dx.doi.org/10.1155/2014/958671
Starting Page	958671
File Format	PDF
ISSN	1748670X
e-ISSN	17486718
Journal	Computational and Mathematical Methods in Medicine
Volume Number	2014
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2014-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Modelling and Simulation Immunology and Microbiology(all) Applied Mathematics Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Modeling and Simulation

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction

Undersampled MR Image Reconstruction with Data-Driven Tight Frame

MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

Accelerated Compressed Sensing Based CT Image Reconstruction

Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation

Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT

A Rate-Distortion-Based Merging Algorithm for Compressed Image Segmentation

Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization

Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity

Similar Documents

Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction

Undersampled MR Image Reconstruction with Data-Driven Tight Frame

MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

Accelerated Compressed Sensing Based CT Image Reconstruction

Wavelet Transform Fuzzy Algorithms for Dermoscopic Image Segmentation

Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT

A Rate-Distortion-Based Merging Algorithm for Compressed Image Segmentation

Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization

Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity