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020 _a9789811335976
_9978-981-13-3597-6
024 7 _a10.1007/978-981-13-3597-6
_2doi
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100 1 _aDeka, Bhabesh.
_eauthor.
_0(orcid)0000-0002-9679-6159
_1https://orcid.org/0000-0002-9679-6159
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_940750
245 1 0 _aCompressed Sensing Magnetic Resonance Image Reconstruction Algorithms
_h[electronic resource] :
_bA Convex Optimization Approach /
_cby Bhabesh Deka, Sumit Datta.
250 _a1st ed. 2019.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2019.
300 _aXIII, 122 p. 38 illus., 23 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series on Bio- and Neurosystems,
_x2520-8543 ;
_v9
505 0 _a1. 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 _aThis 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.
650 0 _aSignal processing.
_94052
650 0 _aBiomedical engineering.
_93292
650 0 _aRadiology.
_932514
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aRadiology.
_932514
700 1 _aDatta, Sumit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_940751
710 2 _aSpringerLink (Online service)
_940752
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811335969
776 0 8 _iPrinted edition:
_z9789811335983
830 0 _aSpringer Series on Bio- and Neurosystems,
_x2520-8543 ;
_v9
_940753
856 4 0 _uhttps://doi.org/10.1007/978-981-13-3597-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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999 _c76803
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