NDLI: Dynamic MR imaging reconstruction by three-dimensional dictionary learning

Content Provider	IEEE Xplore Digital Library
Author	Ying Song Jun Zhao
Copyright Year	2012
Description	Author affiliation: Sch. of Biomed. Eng., Shanghai Jiao Tong Univ., Shanghai, China (Ying Song; Jun Zhao)
Abstract	Magnetic Resonance Imaging (MRI) has been widely used in medical diagnosis due to its excellent discernibility to soft tissues and no radiation damage. Recent years, compressed sensing (CS) based reconstruction method for dynamic MRI is a hot topic for it enables accurate reconstruction from undersampled k-space data, which can significantly reduce the data-acquisition time. In this paper, we proposed a novel method for three-dimensional dynamic MRI reconstruction with higher undersampling rates and better imaging quality by extending the atoms in dictionary learning from two dimensions to three dimensions. In this way, spatial correlation among slices is fully exploited implicitly with no artificial interference. The proposed algorithm is simply composed of two steps: adaptive dictionary learning in one step, then restoring and filling in the three dimensional k-space in another step. Numerical experiments were carried out on three-dimensional MR images of anatomies with a variety of undersampling schemes and ratios. The results show that, by only 30 iterations, the proposed method improves the reconstruction quality over the state-of-the-art three-dimensional reconstruction methods with a reduction of more than 95% in normalized mean square error (NMSE). Besides, the influence of parameter variations to the reconstruction quality is also analyzed. The parameter variation ranging from 20% to 200% can only bias the image quality within 0.001 in NMSE.
Starting Page	35
Ending Page	39
File Size	1435859
Page Count	5
File Format	PDF
ISBN	9781467311830
e-ISBN	9781467311847
DOI	10.1109/BMEI.2012.6512928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MRI Dictionary learning Compressed sensing
Content Type	Text
Resource Type	Article

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

Compressed sensing MRI with Bayesian dictionary learning

Learning the sparsity basis in low-rank plus sparse model for dynamic MRI reconstruction

Spatiotemporal dictionary learning for undersampled dynamic MRI reconstruction via joint frame-based and dictionary-based sparsity

Reconstruction of highly under-sampled dynamic MRI using sparse representation of 1D temporal snippets

Tight frame learning for cardiovascular MRI

Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning

Compressed sensing reconstruction of 3D ultrasound data using dictionary learning

Learning space-time dictionaries for blind compressed sensing dynamic MRI reconstruction

Low-dimensional-structure self-learning and thresholding: Regularization beyond compressed sensing for MRI Reconstruction

Dynamic MR imaging reconstruction by three-dimensional dictionary learning

Similar Documents

Compressed sensing MRI with Bayesian dictionary learning

Learning the sparsity basis in low-rank plus sparse model for dynamic MRI reconstruction

Spatiotemporal dictionary learning for undersampled dynamic MRI reconstruction via joint frame-based and dictionary-based sparsity

Reconstruction of highly under-sampled dynamic MRI using sparse representation of 1D temporal snippets

Tight frame learning for cardiovascular MRI

Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning

Compressed sensing reconstruction of 3D ultrasound data using dictionary learning

Learning space-time dictionaries for blind compressed sensing dynamic MRI reconstruction

Low-dimensional-structure self-learning and thresholding: Regularization beyond compressed sensing for MRI Reconstruction

Dynamic MR imaging reconstruction by three-dimensional dictionary learning