Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR

Sajan Goud Lingala; Yue Hu; Edward DiBella; Mathews Jacob

doi:10.1109/TMI.2010.2100850

Back

Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR

Journal article

Open access

Peer reviewed

Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR

Sajan Goud Lingala, Yue Hu, Edward DiBella and Mathews Jacob

IEEE transactions on medical imaging, Vol.30(5), pp.1042-1054

05/2011

DOI: 10.1109/TMI.2010.2100850

PMCID: PMC3707502

PMID: 21292593

Files and links (1)

url

https://www.ncbi.nlm.nih.gov/pmc/articles/3707502View

Open Access

Abstract

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-dependent KL transform makes our approach ideally suited to a range of dynamic imaging problems, even when the motion is not periodic. In comparison to current KLT-based methods that rely on a two-step approach to first estimate the basis functions and then use it for reconstruction, we pose the problem as a spectrally regularized matrix recovery problem. By simultaneously determining the temporal basis functions and its spatial weights from the entire measured data, the proposed scheme is capable of providing high quality reconstructions at a range of accelerations. In addition to using the compact representation in the KLT domain, we also exploit the sparsity of the data to further improve the recovery rate. Validations using numerical phantoms and in vivo cardiac perfusion MRI data demonstrate the significant improvement in performance offered by the proposed scheme over existing methods.

Magnetic Resonance Imaging

Optimization

Heuristic algorithms

Transforms

Minimization

k-t SLR

Data driven transforms

Image reconstruction

dynamic magnetic resonance imaging (MRI)

low rank and sparse matrix recovery

Details

Title: Subtitle: Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR
Creators: Sajan Goud Lingala - Dept. of Biomed. Eng., Univ. of Rochester, Rochester, NY, USA
Yue Hu - Dept. of Electr. & Comput. Eng., Univ. of Rochester, Rochester, NY, USA
Edward DiBella - Dept. of Radiol., Univ. of Utah, Salt Lake City, UT, USA
Mathews Jacob - Depts. of Biomed. Eng. & Imaging Sci., Univ. of Rochester, Rochester, NY, USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.30(5), pp.1042-1054
DOI: 10.1109/TMI.2010.2100850
PMID: 21292593
PMCID: PMC3707502
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 05/2011
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984070334602771

Metrics

38 Record Views

570 Times Cited - Web of Science