Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Compressive Sensing (CS) is a widely used technique in biomedical signal acquisition and reconstruction. The technique is especially useful for reducing acquisition time for magnetic resonance imaging (MRI) signal acquisitions and reconstruction, where effects of patient fatigue and Claustrophobia n...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access Vol. 8; pp. 186222 - 186232
Main Authors: Wahid, Abdul, Shah, Jawad Ali, Khan, Adnan Umar, Ahmed, Manzoor, Razali, Hanif
Format: Journal Article
Language:English
Published: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Compressed sensing
Compressive sensing
Image acquisition
Image reconstruction
inverse problems in imaging
Iterative algorithms
Iterative methods
iterative thresholding algorithms
Machine learning
Magnetic resonance imaging
multi-layered convolutional sparse coding
Multilayers
Restoration
Training
ISSN:2169-3536, 2169-3536
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Compressive Sensing (CS) is a widely used technique in biomedical signal acquisition and reconstruction. The technique is especially useful for reducing acquisition time for magnetic resonance imaging (MRI) signal acquisitions and reconstruction, where effects of patient fatigue and Claustrophobia need mitigation. In addition to improving patient experience, faster MRI scans are important for time sensitive imaging, such as functional or cardiac MRI, where target movement is unavoidable. Inspired from recent research works on multi-layer convolutional sparse coding (ML-CSC) theory to model deep neural networks, this work proposes a multi-layer basis pursuit framework which combines the benefit from objective-based CS reconstructions and deep learning-based reconstruction by employing iterative thresholding algorithms for successfully training a CS-MRI restoration framework on GPU and reconstruct test images using parameters of the trained model. Extensive experiments show the effectiveness of the proposed framework on four MRI datasets in terms of faster convergence, improved PSNR/SSIM, and better restoration efficiency as compared to the state of the art frameworks with different CS ratios.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3028877