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020			_a9789811335976
024	7		_a10.1007/978-981-13-3597-6 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQC762.6.M34 _b2019 EB
100	1		_aDeka, Bhabesh _9671130
245	1	0	_aCompressed Sensing Magnetic Resonance Image Reconstruction Algorithms : _bA Convex Optimization Approach _cby Bhabesh Deka, Sumit Datta.
264		1	_aSingapore _bImprint: Springer _c2019
300			_a1 recurso en línea (XIII, 122 páginas) _b38 ilustraciones, 23 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aSpringer Series on Bio- and Neurosystems _x2520-8535 _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	3		_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		7	_2embne _aResonancia magnética nuclear (Medicina) _9141826
650		7	_2embne _9143820 _aIngeniería biomédica
700	1		_aDatta, Sumit. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9789811335969
776	0	8	_iPrinted edition: _z9789811335983
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-3597-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Engineering
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _ek _zSI