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...

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Vydáno v:	IEEE access Ročník 8; s. 186222 - 186232
Hlavní autoři:	Wahid, Abdul, Shah, Jawad Ali, Khan, Adnan Umar, Ahmed, Manzoor, Razali, Hanif
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Shah, Jawad Ali Khan, Adnan Umar Wahid, Abdul Ahmed, Manzoor Razali, Hanif
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Snippet	Compressive Sensing (CS) is a widely used technique in biomedical signal acquisition and reconstruction. The technique is especially useful for reducing...
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SubjectTerms	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
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Title	Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction
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