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Compressed sensing magnetic resonanc...

Deka, Bhabesh.

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Compressed sensing magnetic resonance image reconstruction algorithms = a convex optimization approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Compressed sensing magnetic resonance image reconstruction algorithms/ by Bhabesh Deka, Sumit Datta.
Reminder of title:	a convex optimization approach /
Author:	Deka, Bhabesh.
other author:	Datta, Sumit.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xiii, 122 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction to Compressed Sensing Magnetic Resonance Imaging -- 2. Compressed Sensing MRI Reconstruction Problem -- 3. Fast Algorithms for Compressed Sensing MRI Reconstruction -- 4. Simulation Results -- 5. Performance Evaluation and Benchmark Setting -- 6. Conclusions and Future Directions.
Contained By:	Springer eBooks
Subject:	Magnetic resonance imaging. -
Online resource:	https://doi.org/10.1007/978-981-13-3597-6
ISBN:	9789811335976

Compressed sensing magnetic resonance image reconstruction algorithms = a convex optimization approach /
Deka, Bhabesh.

Compressed sensing magnetic resonance image reconstruction algorithmsa convex optimization approach /[electronic resource] :by Bhabesh Deka, Sumit Datta. - Singapore :Springer Singapore :2019. - xiii, 122 p. :ill., digital ;24 cm. - Springer series on bio- and neurosystems,v.92520-8535 ;. - Springer series on bio- and neurosystems ;v.9..

1. Introduction to Compressed Sensing Magnetic Resonance Imaging -- 2. Compressed Sensing MRI Reconstruction Problem -- 3. Fast Algorithms for Compressed Sensing MRI Reconstruction -- 4. Simulation Results -- 5. Performance Evaluation and Benchmark Setting -- 6. Conclusions and Future Directions.

This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.

ISBN: 9789811335976

Standard No.: 10.1007/978-981-13-3597-6doiSubjects--Topical Terms:

554355
Magnetic resonance imaging.

LC Class. No.: QC762.6.M34 / D453 2019

Dewey Class. No.: 616.07548

Compressed sensing magnetic resonance image reconstruction algorithms = a convex optimization approach /
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245 1 0 $a Compressed sensing magnetic resonance image reconstruction algorithms $h [electronic resource] : $b a convex optimization approach / $c by Bhabesh Deka, Sumit Datta.
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505 0 $a 1. Introduction to Compressed Sensing Magnetic Resonance Imaging -- 2. Compressed Sensing MRI Reconstruction Problem -- 3. Fast Algorithms for Compressed Sensing MRI Reconstruction -- 4. Simulation Results -- 5. Performance Evaluation and Benchmark Setting -- 6. Conclusions and Future Directions.
520 $a This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.
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