Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms (eBook)

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.

Dr. Bhabesh Deka has been an Associate Professor at the Department of Electronics and Communication Engineering (ECE) at Tezpur University, Assam, India since January 2012. He is also a Visvesvaraya Young Faculty Research Fellow (YFRF) of the Ministry of Electronics & Information Technology (MeitY), Government of India. His major research interests are image processing (particularly, inverse ill-posed problems), computer vision, compressive sensing MRI and biomedical signal analysis. He is actively engaged in the development of low-cost Internet of Things (IoT) enabled systems for mobile healthcare, high-throughput compressed sensing based techniques for rapid magnetic resonance image reconstruction, and parallel computing architectures for real-time image processing and computer vision applications. He has published a number of articles in peer-reviewed national and international journals of high repute. He is also a regular reviewer for a various leading journals, including IEEE Transactions on Image Processing, IEEE Access, IEEE Signal Processing Letters, IET Image Processing, IET Computer Vision, Biomedical Signal Processing and Control, Digital Signal Processing, and International Journal of Electronics and Communications (AEU). He is associated with a number of professional bodies and societies, like, Fellow, IETE; Senior Member, IEEE (USA); Member, IEEE Engineering in Medicine and Biology (EMB) Society (USA); and Life Member, Institution of Engineers (India).

Mr. Sumit Datta is currently pursuing his Ph.D. in the area of compressed sensing magnetic resonance image reconstruction at the Department of Electronics and Communication Engineering (ECE), Tezpur University, Assam, India. He received his B.Tech. in Electronics and Communication Engineering from National Institute of Technology Agartala (NITA), Tripura, India, in 2011 and his M.Tech. in Bioelectronics from Tezpur University in 2014. His research interests include image processing, biomedical signal and image processing, compressed sensing MRI, and parallel computing. He has published a number of articles in peer-reviewed national and international journals, such as IEEE Signal Processing Letters, IET Image Processing, Journal of Optics, and the Multimedia Tools and Applications. 

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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 forthe 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.