Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms (Record no. 76803)
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fixed length control field | 04057nam a22005655i 4500 |
001 - CONTROL NUMBER | |
control field | 978-981-13-3597-6 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20220801214832.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 181229s2019 si | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9789811335976 |
-- | 978-981-13-3597-6 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 621.382 |
100 1# - AUTHOR NAME | |
Author | Deka, Bhabesh. |
245 10 - TITLE STATEMENT | |
Title | Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms |
Sub Title | A Convex Optimization Approach / |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2019. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XIII, 122 p. 38 illus., 23 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | Springer Series on Bio- and Neurosystems, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | 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 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
700 1# - AUTHOR 2 | |
Author 2 | Datta, Sumit. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-981-13-3597-6 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
100 1# - AUTHOR NAME | |
-- | (orcid)0000-0002-9679-6159 |
-- | https://orcid.org/0000-0002-9679-6159 |
264 #1 - | |
-- | Singapore : |
-- | Springer Nature Singapore : |
-- | Imprint: Springer, |
-- | 2019. |
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-- | computer |
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-- | rdamedia |
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-- | online resource |
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347 ## - | |
-- | text file |
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-- | rda |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal processing. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Biomedical engineering. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Radiology. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal, Speech and Image Processing . |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Biomedical Engineering and Bioengineering. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Radiology. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2520-8543 ; |
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-- | ZDB-2-ENG |
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-- | ZDB-2-SXE |
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