Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model.
- Provides details for optimizing regularization parameters in each type of reconstruction.
- Presents comparison of regularization approaches for each type of pMRI reconstruction.
- Includes discussion of case studies using clinically acquired data.
- MATLAB codes are provided for each reconstruction type.
- Contains method-wise description of adapting regularization to optimize speed and accuracy.
This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.
Table of Contents
Preface. Acknowledgement. Author Biography. Parallel MR image reconstruction. Regularization techniques for MR image reconstruction. Regularization parameter selection methods in parallel MR image reconstruction. Multi-filter calibration for autocalibrating parallel MRI. Parameter adaptation for wavelet regularization in parallel MRI. Parameter adaptation for total variation based regularization in parallel MRI. Combination of parallel magnetic resonance imaging and compressed sensing using L1-SPIRiT. Matrix completion methods. References. L MATLAB Codes.
Joseph Suresh Paul
Joseph Suresh Paul is currently a Professor at the Indian Institute of Information Technology and Management- Kerala (IIITM-K), India. He obtained his Ph.D. degree in Electrical Engineering from the Indian Institute of Technology, Madras, India in the year 2000. His research is focused on MR imaging from the perspective of accelerating image acquisition, with the goal of enhancing clinically relevant features using filters integrated into the reconstruction process. His other interests include mathematical applications to problems in MR image reconstruction, compressed sensing, and super resolution techniques for MRI. He has published a number of articles in peer-reviewed international journals of high repute.
Raji Susan Mathew
Raji Susan Mathew is currently pursuing her Ph.D. degree in the area of MR image reconstruction. She received bachelor degree in Electronics and Communication Engineering from the Mahatma Gandhi university, Kottayam and master's degree in signal processing from the Cochin university of science and technology, Kochi in 2011 and 2013. She is a recipient of the Maulana Azad National Fellowship (MANF) by the University Grants Commission (UGC), India. Her research interests include regularization techniques for MR image reconstruction and Compressed Sensing.