Download or read book Signal Processing Techniques for Improving Image Reconstruction of Parallel Magnetic Resonance Imaging and Dynamic Magnetic Resonance Imaging written by Huajun She and published by . This book was released on 2015 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book MRI written by Angshul Majumdar and published by CRC Press. This book was released on 2018-09-03 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Principles of Magnetic Resonance Imaging written by Zhi-Pei Liang and published by Wiley-IEEE Press. This book was released on 2000 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: Mathematical fundamentals Signal generation and detection principles Signal characteristics Signal localization principles Image reconstruction techniques Image contrast mechanisms Image resolution, noise, and artifacts Fast-scan imaging Constrained reconstruction Complete with a comprehensive set of examples and homework problems, Principles of Magnetic Resonance Imaging is the must-read book to improve your knowledge of this revolutionary technique.

Download or read book Improved Signal and Image Interpolation in Biomedical Applications The Case of Magnetic Resonance Imaging MRI written by Ciulla, Carlo and published by IGI Global. This book was released on 2009-03-31 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents novel concepts supported through mathematics to create unique theories related to interpolation"--Provided by publisher.

Download or read book Signal Processing for Magnetic Resonance Imaging and Spectroscopy written by Hong Yan and published by CRC Press. This book was released on 2002-02-20 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: This reference/text contains the latest signal processing techniques in magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) for more efficient clinical diagnoses-providing ready-to-use algorithms for image segmentation and analysis, reconstruction and visualization, and removal of distortions and artifacts for increased detec

Download or read book Advanced Image Processing in Magnetic Resonance Imaging written by Luigi Landini and published by CRC Press. This book was released on 2018-10-03 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: The popularity of magnetic resonance (MR) imaging in medicine is no mystery: it is non-invasive, it produces high quality structural and functional image data, and it is very versatile and flexible. Research into MR technology is advancing at a blistering pace, and modern engineers must keep up with the latest developments. This is only possible with a firm grounding in the basic principles of MR, and Advanced Image Processing in Magnetic Resonance Imaging solidly integrates this foundational knowledge with the latest advances in the field. Beginning with the basics of signal and image generation and reconstruction, the book covers in detail the signal processing techniques and algorithms, filtering techniques for MR images, quantitative analysis including image registration and integration of EEG and MEG techniques with MR, and MR spectroscopy techniques. The final section of the book explores functional MRI (fMRI) in detail, discussing fundamentals and advanced exploratory data analysis, Bayesian inference, and nonlinear analysis. Many of the results presented in the book are derived from the contributors' own work, imparting highly practical experience through experimental and numerical methods. Contributed by international experts at the forefront of the field, Advanced Image Processing in Magnetic Resonance Imaging is an indispensable guide for anyone interested in further advancing the technology and capabilities of MR imaging.

Download or read book Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and published by Academic Press. This book was released on 2022-11-04 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Reduced data Magnetic Resonance Imaging Reconstruction Methods written by Lei Hou Hamilton and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Imaging speed is very important in magnetic resonance imaging (MRI), especially in dynamic cardiac applications, which involve respiratory motion and heart motion. With the introduction of reduced-data MR imaging methods, increasing acquisition speed has become possible without requiring a higher gradient system. But these reduced-data imaging methods carry a price for higher imaging speed. This may be a signal-to-noise ratio (SNR) penalty, reduced resolution, or a combination of both. Many methods sacrifice edge information in favor of SNR gain, which is not preferable for applications which require accurate detection of myocardial boundaries. The central goal of this thesis is to develop novel reduced-data imaging methods to improve reconstructed image performance. This thesis presents a novel reduced-data imaging method, PINOT (Parallel Imaging and NOquist in Tandem), to accelerate MR imaging. As illustrated by a variety of computer simulated and real cardiac MRI data experiments, PINOT preserves the edge details, with flexibility of improving SNR by regularization. Another contribution is to exploit the data redundancy from parallel imaging, rFOV and partial Fourier methods. A Gerchberg Reduced Iterative System (GRIS), implemented with the Gerchberg-Papoulis (GP) iterative algorithm is introduced. Under the GRIS, which utilizes a temporal band-limitation constraint in the image reconstruction, a variant of Noquist called iterative implementation iNoquist (iterative Noquist) is proposed. Utilizing a different source of prior information, first combining iNoquist and Partial Fourier technique (phase-constrained iNoquist) and further integrating with parallel imaging methods (PINOT-GRIS) are presented to achieve additional acceleration gains.

Download or read book Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Bhabesh Deka and published by Springer. This book was released on 2018-12-29 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Compressed Sensing for Magnetic Resonance Image Reconstruction written by Angshul Majumdar and published by Cambridge University Press. This book was released on 2015-02-26 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Improved Image Reconstruction Methods for Parallel Magnetic Resonance Imaging written by Kaiyu Zheng and published by . This book was released on 2010 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Parallel Imaging in Clinical MR Applications written by Stefan O. Schönberg and published by Springer Science & Business Media. This book was released on 2007-01-11 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the first in-depth introduction to parallel imaging techniques and, in particular, to the application of parallel imaging in clinical MRI. It will provide readers with a broader understanding of the fundamental principles of parallel imaging and of the advantages and disadvantages of specific MR protocols in clinical applications in all parts of the body at 1.5 and 3 Tesla.

Download or read book Advances in Parallel Imaging Reconstruction Techniques written by Peng Qu and published by . This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Advances in Parallel Imaging Reconstruction Techniques" by Peng, Qu, 瞿蓬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Advances in Parallel Imaging Reconstruction Techniques submitted by Qu Peng for the degree of Doctor of Philosophy at The University of Hong Kong in February 2006 In recent years, a new approach to magnetic resonance imaging (MRI), known as "parallel imaging," has revolutionized the field of fast MRI. By using sensitivity information from an RF coil array to perform some of the spatial encoding which is traditionally accomplished by magnetic field gradient, parallel imaging techniques allow reduction of phase encoding steps and consequently decrease the scan time. This thesis presents the author''s investigations in the reconstruction techniques of parallel MRI. After reviewing the conventional methods, such as the image-domain-based sensitivity encoding (SENSE), the k-space-based simultaneous acquisition of spatial harmonics (SMASH), generalized auto-calibrating partially parallel acquisition (GRAPPA), and the iterative SENSE method which is applicable to arbitrary k-space trajectories, the author proposes several advanced reconstruction strategies to enhance the performance of parallel imaging in terms of signal-to-noise (SNR), the power of aliasing artifacts, and computational efficiency. First, the conventional GRAPPA technique is extended in that the data interpolation scheme is tailored and optimized for each specific reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix for the fit procedure, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can reduce residual artifacts for GRAPPA reconstruction. Second, a novel discrepancy-based method for regularization parameter choice is introduced into GRAPPA reconstruction. By this strategy, adaptive regularization in GRAPPA can be realized which can automatically choose nearly optimal parameters for the reconstructions so as to achieve good compromise between SNR and artifacts. It is demonstrated by MRI experiments that the discrepancy-based parameter choice strategy significantly outperforms those based on the L-curve or on a fixed singular value threshold. Third, the convergence behavior of the iterative non-Cartesian SENSE reconstruction is analyzed, and two different strategies are proposed to make reconstructions more stable and robust. One idea is to stop the iteration process in due time so that artifacts and SNR are well balanced and fine overall image quality is achieved; as an alternative, the inner-regularization method, in combination with the Lanczos iteration process, is introduced into non-Cartesian SENSE to mitigate the ill-conditioning effect and improve the convergence behavior. Finally, a novel multi-resolution successive iteration (MRSI) algorithm for non-Cartesian parallel imaging is proposed. The conjugate gradient (CG) iteration is performed in several successive phases with increasing resolution. It is demonstrated by spiral MRI results that the total reconstruction time can be reduced by over 30% by using low resolution in initial stages of iteration. In sum, the author describes several developments in image reconstruction for sensitivity-encoded MRI. The great potential of parallel imaging in modern applications can be further enh

Download or read book Improvement in High Acceleration Parallel Magnetic Resonance Imaging Using Efficient Graph based Energy Minimization Methods written by Gurmeet Singh and published by . This book was released on 2008 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accelerated Imaging Techniques for Chemical Shift Magnetic Resonance Imaging written by Curtis N. Wiens and published by . This book was released on 2013 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical shift imaging is a magnetic resonance imaging technique that separates the signal from two or more chemical species. The cost of chemical shift encoding is increased acquisition time as multiple acquisitions are required at different echo times. Image accelera tion techniques, typically parallel imaging, are often used to improve coverage and resolution. This thesis describes a new technique for estimating the signal to noise ratio for parallel imaging reconstruction s and proposes new image reconstructions for a ccelerated chemical shift imaging using compressed sensing and/or parallel imaging for two applications: water- at separation and metabolic imaging of hyperpolarized [1-13C] pyruvate. Spatially varying noise in parallel imaging reconstructions makes measurements of the signal to noise ratio, a commonly used metric for image for image quality, difficult. Existing approaches have limitations: they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A signal to noise ratio estimation technique is proposed that does not exhibit these limitations. Water-fat imaging of highly undersampled datasets from the liver, calf, knee, and abdominal cavity are demonstrated using a customized IDEAL-SPGR pulse sequence and an integrated compressed sensing, parallel imaging, water-fat reconstruction. This method offer s image quality comparable to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this method offers improved image quality when compared to the current standard of parallel imaging. Accelerated metabolic imaging of hyperpolarized [1-13C] pyruvate and its metabolic by-products lactate, alanine, and bicarbonate is demonstrated using an integrated compressed sensing, metabolite separation reconstruction. Phantoms are used to validate this technique while retrospectively and prospectively accelerated 3D in vivo datasets are used to demonstrate feasibility. An alternative approach to accelerated metabolic imaging is demonstrated using high performance magnetic field gradient set. This thesis addresses the inherently slow acquisition times of chemical shift imaging by examining the role compressed sensing and parallel imaging can play in chemical shift imaging. An approach to SNR assessment for parallel imaging reconstruction is proposed and approaches to accelerated chemical shift imaging are described for applications in water-fat imaging and metabolic imaging of hyperpolarized [1-13C] pyruvate.