Compressed Sensing For Magnetic Resonance Image Reconstruction
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| Author | : Angshul Majumdar |
| Publisher | : Cambridge University Press |
| Total Pages | : 228 |
| Release | : 2015-02-26 |
| Genre | : Technology & Engineering |
| ISBN | : 1316673928 |
Expecting the reader to have some basic training in liner algebra and optimization, the book begins with a general discussion on CS techniques and algorithms. It moves on to discussing single channel static MRI, the most common modality in clinical studies. It then takes up multi-channel MRI and the interesting challenges consequently thrown up in signal reconstruction. Off-line and on-line techniques in dynamic MRI reconstruction are visited. Towards the end the book broadens the subject by discussing how CS is being applied to other areas of biomedical signal processing like X-ray, CT and EEG acquisition. The emphasis throughout is on qualitative understanding of the subject rather than on quantitative aspects of mathematical forms. The book is intended for MRI engineers interested in the brass tacks of image formation; medical physicists interested in advanced techniques in image reconstruction; and mathematicians or signal processing engineers.
| Author | : Bhabesh Deka |
| Publisher | : Springer |
| Total Pages | : 122 |
| Release | : 2018-12-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9811335974 |
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.
| Author | : Mehmet Akcakaya |
| Publisher | : Academic Press |
| Total Pages | : 518 |
| Release | : 2022-11-04 |
| Genre | : Science |
| ISBN | : 012822746X |
Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction, along with the latest research“/li> Gives example codes for some of the methods presented Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction
| Author | : Sumit Datta |
| Publisher | : |
| Total Pages | : 133 |
| Release | : 2019 |
| Genre | : Compressed sensing (Telecommunication) |
| ISBN | : 9789811335983 |
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.
| Author | : Angshul Majumdar |
| Publisher | : CRC Press |
| Total Pages | : 222 |
| Release | : 2018-09-03 |
| Genre | : Technology & Engineering |
| ISBN | : 1482298899 |
The field of magnetic resonance imaging (MRI) has developed rapidly over the past decade, benefiting greatly from the newly developed framework of compressed sensing and its ability to drastically reduce MRI scan times. MRI: Physics, Image Reconstruction, and Analysis presents the latest research in MRI technology, emphasizing compressed sensing-based image reconstruction techniques. The book begins with a succinct introduction to the principles of MRI and then: Discusses the technology and applications of T1rho MRI Details the recovery of highly sampled functional MRIs Explains sparsity-based techniques for quantitative MRIs Describes multi-coil parallel MRI reconstruction techniques Examines off-line techniques in dynamic MRI reconstruction Explores advances in brain connectivity analysis using diffusion and functional MRIs Featuring chapters authored by field experts, MRI: Physics, Image Reconstruction, and Analysis delivers an authoritative and cutting-edge treatment of MRI reconstruction techniques. The book provides engineers, physicists, and graduate students with a comprehensive look at the state of the art of MRI.
| Author | : Angshul Majumdar |
| Publisher | : Cambridge University Press |
| Total Pages | : 227 |
| Release | : 2015-02-26 |
| Genre | : Computers |
| ISBN | : 1107103762 |
"Discusses different ways to use existing mathematical techniques to solve compressed sensing problems"--Provided by publisher.
| Author | : Joseph Suresh Paul |
| Publisher | : CRC Press |
| Total Pages | : 306 |
| Release | : 2019-11-05 |
| Genre | : Medical |
| ISBN | : 1351029258 |
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. Features: 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.
| Author | : Majid Sabbagh |
| Publisher | : |
| Total Pages | : 32 |
| Release | : 2016 |
| Genre | : Cardiovascular system |
| ISBN | : |
Cardiac magnetic resonance imaging (MRI) has become a crucial part of monitoring patients with congenital heart diseases. An important limitation of cardiac MRI using the prominent 3D steady-state free precession (3D-SSFP) sequence is its long scan time. Compressed sensing (CS) algorithm reduces the scan time by undersampling the data but increases the image reconstruction time because a non-linear optimization problem must be iteratively solved to estimate the missing data and reconstruct the images. The growing demand for reducing the examination time in cardiac MRI led us to investigate opportunities to accelerate this non-linear optimization problem to facilitate the migration of CS into the clinical environment. Using undersampled 3D-SSFP datasets acquired from five patients, we compared the speed and output quality of CS image reconstruction algorithm using a Central Processing Unit (CPU), a CPU with OpenMP parallelization, and two different Graphics Processing Unit (GPU) platforms. Reconstruction time had a mean of 13.1 minutes with a standard deviation of 3.8 minutes for the CPU, a mean of 11.5 minutes with a standard deviation of 3.6 minutes for the CPU with OpenMP parallelization, a mean of 2.2 minutes with a standard deviation of 0.3 minutes for the CPU with OpenMP plus NVIDIA k20m GPU, and a mean of 1.7 minutes with a standard deviation of 0.3 minutes for the CPU with OpenMP plus NVIDIA k40m GPU. The quality of images reconstructed on GPU and on CPU, as assessed by image subtraction, was comparable. Furthermore, necessary steps for implementation of rapid CS image reconstruction in the clinical environment are discussed.
| Author | : Mariya Doneva |
| Publisher | : Sudwestdeutscher Verlag Fur Hochschulschriften AG |
| Total Pages | : 132 |
| Release | : 2011 |
| Genre | : Magnetic resonance imaging |
| ISBN | : 9783838111018 |
This work explores and extends the concept of applying compressed sensing to MRI. Asuccessful CS reconstruction requires incoherent measurements,signal sparsity, and a nonlinearsparsity promoting reconstruction. To optimize the performance of CS, the acquisition, thesparsifying transform and the reconstruction have to be adapted to the application of interest.This work presents new approaches for sampling, signal sparsity and reconstruction, which areapplied to three important applications: dynamic MR imaging, MR parameter mapping andchemical-shift based water-fat separation.The methods presented in this work allow to more fully exploit the potential of compressedsensing to improve imaging speed. Future development of these methods, and combination withexisting techniques for fast imaging, holds the potential to improve the diagnostic quality ofexisting clinical MR imaging techniques and to open up opportunities for entirely new clinicalapplications of MRI.
| Author | : Farah Deeba |
| Publisher | : Springer Nature |
| Total Pages | : 170 |
| Release | : 2020-10-21 |
| Genre | : Computers |
| ISBN | : 3030615987 |
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
