03937nam 22005055 450 991035028010332120181229201819.0981-13-3597-410.1007/978-981-13-3597-6(CKB)4100000007334907(MiAaPQ)EBC5627090(DE-He213)978-981-13-3597-6(EXLCZ)99410000000733490720181229d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCompressed Sensing Magnetic Resonance Image Reconstruction Algorithms[electronic resource] A Convex Optimization Approach /by Bhabesh Deka, Sumit Datta1st ed. 2019.Singapore :Springer Singapore :Imprint: Springer,2019.1 online resource (122 pages)Springer Series on Bio- and Neurosystems,2520-8535 ;9981-13-3596-6 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.Springer Series on Bio- and Neurosystems,2520-8535 ;9Biomedical engineeringRadiology, MedicalSignal, Image and Speech Processinghttp://scigraph.springernature.com/things/product-market-codes/T24051Biomedical Engineering and Bioengineeringhttp://scigraph.springernature.com/things/product-market-codes/T2700XImaging / Radiologyhttp://scigraph.springernature.com/things/product-market-codes/H29005Biomedical engineering.Radiology, Medical.Signal, Image and Speech Processing.Biomedical Engineering and Bioengineering.Imaging / Radiology.616.07548Deka Bhabeshauthttp://id.loc.gov/vocabulary/relators/aut981946Datta Sumitauthttp://id.loc.gov/vocabulary/relators/autBOOK9910350280103321Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms2241031UNINA