Paper Info
Title
Video Compressive Sensing for Dynamic MRI.
Abstract
We present a video compressive sensing framework, termed kt-CSLDS, to accelerate the image acquisition process of dynamic magnetic resonance imaging (MRI). We are inspired by a state-of-the-art model for video compressive sensing that utilizes a linear dynamical system (LDS) to model the motion manifold. Given compressive measurements, the state sequence of an LDS can be first estimated using system identification techniques. We then reconstruct the observation matrix using a joint structured sparsity assumption. In particular, we minimize an objective function with a mixture of wavelet sparsity and joint sparsity within the observation matrix. We derive an efficient convex optimization algorithm through alternating direction method of multipliers (ADMM), and provide a theoretical guarantee for global convergence. We demonstrate the performance of our approach for video compressive sensing, in terms of reconstruction accuracy. We also investigate the impact of various sampling strategies. We apply this framework to accelerate the acquisition process of dynamic MRI and show it achieves the best reconstruction accuracy with the least computational time compared with existing algorithms in the literature.
Year
DOI
Venue
2014
10.1186/1471-2202-13-S1-P183
BMC Neuroscience
Keywords
Field
DocType
biomedical research,bioinformatics
Computer vision,Computer science,Image quality,Artificial intelligence,Dynamic imaging,Dynamic contrast-enhanced MRI,Optical flow,Image resolution,Image registration,Compressed sensing,Wavelet
Journal
Volume
Citations 
PageRank 
abs/1401.7715
0
0.34
References 
Authors
2
4
Name
Order
Citations
PageRank
Jianing Shi1994.91
Wotao Yin25038243.92
Aswin C. Sankaranarayanan377051.51
Richard G. Baraniuk45053489.23