Download or read book Novel Applications of Compressed Sensing to Magnetic Resonance Imaging Spectroscopy written by Sairam Geethanath and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, three novel applications of compressed sensing to MRI have been developed and implemented which accomplish reduction in acquisition time, thereby also enabling increased spatial and/or temporal resolution. The first application is for reducing the acquisition time of conventional 1H magnetic resonance spectroscopic imaging (MRSI), which requires alongeracquisition time than conventional MRI. The implementation involved exploiting the inherent sparsity of the MRSI data in the wavelet domain by the use of Daubechies wavelet. This was demonstrated on an in vitro phantom, 6 healthy human brain MRSI data sets, 2 brain and prostate cancer data sets. The reconstructions were quantified by the use of the root-mean-square-error metric and subsequent statistical comparison of the metabolite intensities based on one-way ANOVA followed by Bonferroni's multiple comparison test. It was found that the implementation resulted in statistically significant differences at an acceleration of 10X and was considered the limit of the implementation. The implementation showed no significant differences until 5X. This indicates that CS has a potential to reduce conventional MRSI acquisition time by ̃80%. This reduction in time could be used to increase the spatial resolution of the scan or acquire harder-to-detect metabolites through increased averaging. Dynamic contrast enhanced MRI (DCE-MRI) is a MRI method that involves serial acquisition of images before and after the injection of a contrast agent. Therefore, it requires both high spatial and temporal resolution. The second application aims at accomplishing these requirements through the use of CS and comparing it with the widely-used method of key-hole imaging with respect to the choice of sampling masks and acceleration. Three sampling masks were designed for both approaches and reconstructions were performed at 2X, 3X, 4X and 5X. A semi-automatic segmentation procedure was followed to obtain regions of well and poorly perfused tissue and the results were compared using the RMSE metric and a voxel-wise paired t-test. The results of these tests showed that CS based masks performed better as compared to their key-hole counterparts and the sampling mask based on data thresholding performed the best. However, the exact implementation of this mask is impractical but an approximate solution was implemented for accelerating 3D gradient echo imaging. The third application that has been developed in this work relates to the acceleration of sweep imaging with Fourier transform (SWIFT) which is a novel MR method facilitating the visualization of short T2 species, which can yield important information about certain tissuessuch as cartilage. In this project, CS was applied to a resolution phantom and 5 human knee data sets acquired using SWIFT based imaging and accelerated up to 5X. The errors of reconstruction were quantified by RMSE and it was found that reconstructions at 5X maintained fidelity. A semi-automatic segmentation procedure was followed to segment the ligaments and adjoining structures and the number of segmented voxels was compared for the full data reconstruction and the accelerated cases. The 5X reconstruction showed a percentage difference of approximately 17% and was considered the limit of the implementation.
Download or read book Novel Compressed Sensing Algorithms with Applications to Magnetic Resonance Imaging written by Yue Hu and published by . This book was released on 2014 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
Download or read book Mobile NMR and MRI written by Mike Johns and published by Royal Society of Chemistry. This book was released on 2015-10-27 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will summarise recent hardware developments, highlight the challenges facing mobile and generally low-field NMR and MRI and describe various emerging applications - some of which have commercial interest.
Download or read book On the Application of Compressed Sensing to Magnetic Resonance Imaging written by André Fischer and published by . This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Discusses different ways to use existing mathematical techniques to solve compressed sensing problems"--Provided by publisher.
Download or read book Quantitative Magnetic Resonance Imaging written by Nicole Seiberlich and published by Academic Press. This book was released on 2020-11-18 with total page 1094 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Magnetic Resonance Imaging is a ‘go-to’ reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion. Each section will start with an explanation of the basic techniques for mapping the tissue property in question, including a description of the challenges that arise when using these basic approaches. For properties which can be measured in multiple ways, each of these basic methods will be described in separate chapters. Following the basics, a chapter in each section presents more advanced and recently proposed techniques for quantitative tissue property mapping, with a concluding chapter on clinical applications. The reader will learn: The basic physics behind tissue property mapping How to implement basic pulse sequences for the quantitative measurement of tissue properties The strengths and limitations to the basic and more rapid methods for mapping the magnetic relaxation properties T1, T2, and T2* The pros and cons for different approaches to mapping perfusion The methods of Diffusion-weighted imaging and how this approach can be used to generate diffusion tensor maps and more complex representations of diffusion How flow, magneto-electric tissue property, fat fraction, exchange, elastography, and temperature mapping are performed How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance Fingerprinting can be used to accelerate or improve tissue property mapping schemes How tissue property mapping is used clinically in different organs Structured to cater for MRI researchers and graduate students with a wide variety of backgrounds Explains basic methods for quantitatively measuring tissue properties with MRI - including T1, T2, perfusion, diffusion, fat and iron fraction, elastography, flow, susceptibility - enabling the implementation of pulse sequences to perform measurements Shows the limitations of the techniques and explains the challenges to the clinical adoption of these traditional methods, presenting the latest research in rapid quantitative imaging which has the possibility to tackle these challenges Each section contains a chapter explaining the basics of novel ideas for quantitative mapping, such as compressed sensing and Magnetic Resonance Fingerprinting-based approaches
Download or read book Application of Compressed Sensing in 3D Magnetic Resonance Imaging written by and published by . This book was released on 2015 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Microscopic Magnetic Resonance Imaging written by Luisa Ciobanu and published by CRC Press. This book was released on 2017-09-08 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past two decades, significant advances in magnetic resonance microscopy (MRM) have been made possible by a combination of higher magnetic fields and more robust data acquisition technologies. This technical progress has enabled a shift in MRM applications from basic anatomical investigations to dynamic and functional studies, boosting the use of MRM in biological and life sciences. This book provides a simple introduction to MRM emphasizing practical aspects relevant to high magnetic fields. It focuses on biological applications and presents a number of selected examples of neuroscience applications. The text is mainly intended for those who are beginning research in the field of MRM or are planning to incorporate high-resolution MRI in their neuroscience studies.
Download or read book Investigation of Compressed sensing for Acceleration of Magnetic Resonance Spectroscopic Imaging written by Lara Angharad Worthington and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Compressed Sensing for Magnetic Resonance Imaging written by Mariya Doneva and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Magnetic Resonance Sensors written by Robert H. Morris and published by MDPI. This book was released on 2018-10-04 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Magnetic Resonance Sensors" that was published in Sensors
Download or read book Compressed Sensing for Functional Magnetic Resonance Imaging Data written by Wattanit Hotrakool and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Approximate Message Passing for Compressed Sensing Magnetic Resonance Imaging written by Charles Millard and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Systematic Evaluation of Compressed Sensing Algorithms Applied to Magnetic Resonance Imaging written by Scott William Fassett and published by . This book was released on 2012 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing is becoming a new paradigm in signal processing by acknowledging that information has a compressible form in some representation. Exploiting the redundant nature of most signals can result in a measurement scheme that intentionally undersamples and is able to extrapolate the remaining important information. Because of long scan times in magnetic resonance imaging, the application of a compressed sensing construct is appealing. The magnetic resonance domain is unique in the compressed sensing framework due to its specialized acquisition system in the k-space. To speed up the acquisition process while obtaining sufficient data to accurately reconstruct the images, multi-channel acquisition under various undersampling schemes and parallel processing to extrapolate data for reconstruction have currently been deployed. This research explores the practicality of using some established CS algorithms to reconstruct images from undersampled multi-channel data. The focus of the evaluation is to see which algorithms, if any, can reconstruct clinically usable images at clinically acceptable speeds
Download or read book Novel Sampling Approaches in Higher Dimensional NMR written by Martin Billeter and published by Springer Science & Business Media. This book was released on 2012-03-15 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concepts in Projection-Reconstruction, by Ray Freeman and Ēriks Kupče.- Automated Projection Spectroscopy and Its Applications, by Sebastian Hiller and Gerhard Wider.- Data Sampling in Multidimensional NMR: Fundamentals and Strategies, by Mark W. Maciejewski, Mehdi Mobli, Adam D. Schuyler, Alan S. Stern and Jeffrey C. Hoch.- Generalized Fourier Transform for Non-Uniform Sampled Data, by Krzysztof Kazimierczuk, Maria Misiak, Jan Stanek, Anna Zawadzka-Kazimierczuk and Wiktor Koźmiński.- Applications of Non-Uniform Sampling and Processing, by Sven G. Hyberts, Haribabu Arthanari and Gerhard Wagner
Download or read book Compressive Sensing with Prior Information Applied to Magnetic Resonance Imaging written by Cristiano Jacques Miosso and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
