Fast Dynamic Magnetic Resonance Imaging Using Sparse Recovery Methods And Novel Signal Encoding Formulations
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Fast Dynamic Magnetic Resonance Imaging Using Sparse Recovery Methods And Novel Signal Encoding Formulations
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Author : Vimal Singh
language : en
Publisher:
Release Date : 2015
Fast Dynamic Magnetic Resonance Imaging Using Sparse Recovery Methods And Novel Signal Encoding Formulations written by Vimal Singh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Magnetic resonance imaging (MRI) is a non-invasive imaging modality that provides excellent soft tissue contrast without using ionizing radiations. These qualities/properties make MRI the preferred imaging modality for critical organs like heart and brain. Over the past decade, the advancement in hardware and image reconstruction algorithms has led to substantial improvements in MRI in terms of imaging speeds, quality and reliability. However, MRI speeds need to be further improved while retaining/maintaining the image quality given that the emerging medical diagnostic procedures are increasingly relying on detailed characterization of physiological functions that evolve on time scales too fast to be captured using conventional MRI methods. This dissertation starts with presenting a sparse signal recovery based fast MRI method. This method synergistically combines a data redundancy scheme for high frequency details with a novel and physically realizable MR signal encoding formulation. The new signal encoding formulation uses clinically deployed tagging radio frequency pulses to mix information in the spatial frequency domain prior to acquisition. Thus, the new formulation leads to a more uniform coverage of spatial frequency information even at high accelerations. The synergistic combination of image-detail redundancy encoding with tagging based signal encoding allows recovery of edges and fine structures with unprecedented quality. Next, this dissertation evaluates the use of fast spiral trajectories for high spatial resolution functional imaging of human superior colliculus. Gradient efficient and motion-robust spiral trajectories are used to keep fMRI scan durations short. . However, high resolution imaging of human subcortical structures using these trajectories is limited due to the weak functional responses of SC structures and also low signal-to-noise ratio associated with small voxels. To improve the functional sensitivity of spiral trajectories, dual echo variants are used. Combination of two echoes of the dual-echo variants reduces noise and thereby improves the functional sensitivity of high resolution fMRI. Lastly, this dissertation presents a novel formulation for fast dynamic MRI which combines the generic linear dynamical system model with sparse recovery techniques. Specifically, the formulation uses a known prior spatio-temporal model to predict the underlying image and uses sparse recovery techniques to recover the residual image. The spatio-temporal evolution model inherently encodes for coupled data redundancies in the spatial- and temporal-dimensions. Also, the generalizability of the formulation in choosing the evolution model allows it to be applicable to various physiological functions.
Novel Compressed Sensing Algorithms With Applications To Magnetic Resonance Imaging
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Author : Yue Hu
language : en
Publisher:
Release Date : 2014
Novel Compressed Sensing Algorithms With Applications To Magnetic Resonance Imaging written by Yue Hu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
"Magnetic Resonance Imaging (MRI) is a widely used non-invasive clinical imaging modality. Unlike other medical imaging tools, such as X-rays or computed tomography (CT), the advantage of MRI is that it uses non-ionizing radiation. In addition, MRI can provide images with multiple contrast by using different pulse sequences and protocols. However, acquisition speed, which remains the main challenge for MRI, limits its clinical application. Clinicians have to compromise between spatial resolution, SNR, and scan time, which leads to sub-optimal performance. The acquisition speed of MRI can be improved by collecting fewer data samples. However, according to the Nyquist sampling theory, undersampling in k-space will lead to aliasing artifacts in the recovered image. The recent mathematical theory of compressed sensing has been developed to exploit the property of sparsity for signals/images. It states that if an image is sparse, it can be accurately reconstructed using a subset of the k-space data under certain conditions. Generally, the reconstruction is formulated as an optimization problem. The sparsity of the image is enforced by using a sparsifying transform. Total variation (TV) is one of the commonly used methods, which enforces the sparsity of the image gradients and provides good image quality. However, TV introduces patchy or painting-like artifacts in the reconstructed images. We introduce novel regularization penalties involving higher degree image derivatives to overcome the practical problems associated with the classical TV scheme. Motivated by novel reinterpretations of the classical TV regularizer, we derive two families of functionals, which we term as isotropic and anisotropic higher degree total variation (HDTV) penalties, respectively. The numerical comparisons of the proposed scheme with classical TV penalty, current second order methods, and wavelet algorithms demonstrate the performance improvement. Specifically, the proposed algorithms minimize the staircase and ringing artifacts that are common with TV schemes and wavelet algorithms, while better preserving the singularities. Higher dimensional MRI is also challenging due to the above mentioned trade-offs. We propose a three-dimensional (3D) version of HDTV (3D-HDTV) to recover 3D datasets. One of the challenges associated with the HDTV framework is the high computational complexity of the algorithm. We introduce a novel computationally efficient algorithm for HDTV regularized image recovery problems. We find that this new algorithm improves the convergence rate by a factor of ten compared to the previously used method. We demonstrate the utility of 3D-HDTV regularization in the context of compressed sensing, denoising, and deblurring of 3D MR dataset and fluorescence microscope images. We show that 3D-HDTV outperforms 3D-TV schemes in terms of the signal to noise ratio (SNR) of the reconstructed images and its ability to preserve ridge-like details in the 3D datasets. To address speed limitations in dynamic MR imaging, which is an important scheme in multi-dimensional MRI, we combine the properties of low rank and sparsity of the dataset to introduce a novel algorithm to recover dynamic MR datasets from undersampled k-t space data. We pose the reconstruction as an optimization problem, where we minimize a linear combination of data consistency error, non-convex spectral penalty, and non-convex sparsity penalty. The problem is solved using an iterative, three step, alternating minimization scheme. Our results on brain perfusion data show a signicant improvement in SNR and image quality compared to classical dynamic imaging algorithms"--Page vii-ix.
Fast Quantitative Magnetic Resonance Imaging
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Author : Guido Buonincontri
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-02-20
Fast Quantitative Magnetic Resonance Imaging written by Guido Buonincontri and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-20 with Medical categories.
Among medical imaging modalities, magnetic resonance imaging (MRI) stands out for its excellent soft-tissue contrast, anatomical detail, and high sensitivity for disease detection. However, as proven by the continuous and vast effort to develop new MRI techniques, limitations and open challenges remain. The primary source of contrast in MRI images are the various relaxation parameters associated with the nuclear magnetic resonance (NMR) phenomena upon which MRI is based. Although it is possible to quantify these relaxation parameters (qMRI) they are rarely used in the clinic, and radiological interpretation of images is primarily based upon images that are relaxation time weighted. The clinical adoption of qMRI is mainly limited by the long acquisition times required to quantify each relaxation parameter as well as questions around their accuracy and reliability. More specifically, the main limitations of qMRI methods have been the difficulty in dealing with the high inter-parameter correlations and a high sensitivity to MRI system imperfections. Recently, new methods for rapid qMRI have been proposed. The multi-parametric models at the heart of these techniques have the main advantage of accounting for the correlations between the parameters of interest as well as system imperfections. This holistic view on the MR signal makes it possible to regress many individual parameters at once, potentially with a higher accuracy. Novel, accurate techniques promise a fast estimation of relevant MRI quantities, including but not limited to longitudinal (T1) and transverse (T2) relaxation times. Among these emerging methods, MR Fingerprinting (MRF), synthetic MR (syMRI or MAGIC), and T1‒T2 Shuffling are making their way into the clinical world at a very fast pace. However, the main underlying assumptions and algorithms used are sometimes different from those found in the conventional MRI literature, and can be elusive at times. In this book, we take the opportunity to study and describe the main assumptions, theoretical background, and methods that are the basis of these emerging techniques. Quantitative transient state imaging provides an incredible, transformative opportunity for MRI. There is huge potential to further extend the physics, in conjunction with the underlying physiology, toward a better theoretical description of the underlying models, their application, and evaluation to improve the assessment of disease and treatment efficacy.
Principles Of Magnetic Resonance Imaging
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Author : Zhi-Pei Liang
language : en
Publisher: Wiley-IEEE Press
Release Date : 2000
Principles Of Magnetic Resonance Imaging written by Zhi-Pei Liang and has been published by Wiley-IEEE Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Medical categories.
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.
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms
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Author : Bhabesh Deka
language : en
Publisher: Springer
Release Date : 2018-12-29
Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Bhabesh Deka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-29 with Technology & Engineering categories.
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.
Low Rank And Sparse Reconstruction In Dynamic Magnetic Resonance Imaging Via Proximal Splitting Methods
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Author :
language : en
Publisher:
Release Date : 2015
Low Rank And Sparse Reconstruction In Dynamic Magnetic Resonance Imaging Via Proximal Splitting Methods written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Compressed Sensing For Magnetic Resonance Image Reconstruction
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Author : Angshul Majumdar
language : en
Publisher: Cambridge University Press
Release Date : 2015-02-26
Compressed Sensing For Magnetic Resonance Image Reconstruction written by Angshul Majumdar and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-26 with Technology & Engineering categories.
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.
Sparse Recovery With Partial Support And Signal Value Knowledge And Applications In Dynamic Mri
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Author : Wei Lu
language : en
Publisher:
Release Date : 2011
Sparse Recovery With Partial Support And Signal Value Knowledge And Applications In Dynamic Mri written by Wei Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Advanced Image Processing In Magnetic Resonance Imaging
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Author : Luigi Landini
language : en
Publisher: CRC Press
Release Date : 2018-10-03
Advanced Image Processing In Magnetic Resonance Imaging written by Luigi Landini and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with Technology & Engineering categories.
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.
Sparsity Based Methods For Cardiac Magnetic Resonance Image Reconstruction And Analysis
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Author : Yang Yu
language : en
Publisher:
Release Date : 2015
Sparsity Based Methods For Cardiac Magnetic Resonance Image Reconstruction And Analysis written by Yang Yu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Heart categories.
In signal processing, sparseness means that there are only small amounts of non-zero elements. This property has been widely observed in various types of signals. However, the data sparseness is hard to be regularized due to its non-convex nature. The recent development of the compressed sensing technique builds a theoretical connection between the sparse constraint and its convex relaxation. This discovery motivates us to explore different types of sparse properties for the generation and analysis of the cardiac magnetic resonance images (MRIs). In this work, our proposed a series of sparse optimization algorithms have been applied to cardiac image reconstruction, segmentation and motion tracking problems for fast and robust analyzing the cardiac data. The cardiac imaging is a challenging problem to MRI due to its fast motion. We proposed a novel calibration-less algorithm to accelerate the generation of dynamic MR images with both compressed sensing and parallel imaging. In addition to the temporal signal, which usually provides more data redundancy than spatial signals, the strong correlations among signals from different coils are utilized to form joint sparse constraints. A general optimization framework is presented to solve the problem under different types of temporal sparse constraints efficiently. We then apply the sparse constraint to the cardiac muscle motion tracking. The 3D deformable heart model is built by simulating its motion in a cardiac cycle based on tagged MRI. The tagged MR data is widely used to reveal the internal myocardial motion. However, the automated tagging line detection results are very noisy due to the poor image quality. To alleviate this issue, we introduce a new family of sparse deformable models based on the sparseness of the detection noise. Our new models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.