Download or read book An Information Theoretic Approach to Compressed Sensing and Its Utility in Magnetic Resonance Imaging written by Mehmet Akcakaya and published by . This book was released on 2010 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: MRI is a non-invasive and radiation-free imaging modality that is used to evaluate various diseases and guide therapies. One of the disadvantages of MRI is lengthy acquisition. Over the past decade, several methods including parallel imaging, partial acquisition, and rapid data acquisition has been proposed to reduce MRI data acquisition. We investigate the utility of CS to accelerate image acquisition in MRI, in particular in cardiac MRI (CMR). We develop and investigate CS techniques suitable for accelerating coronary artery and pulmonary vein MRA, including methods that utilize a probabilistic model for the dependencies exhibited by the wavelet coefficients of medical images, and distributed CS using multiple-coil information. Our results show the feasibility and potential of CS in CMR.

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 in Information Processing written by Gitta Kutyniok and published by Springer Nature. This book was released on 2022-10-20 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing.

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 Compressed Sensing Magnetic Resonance Image Reconstruction Algorithms written by Sumit Datta and published by . This book was released on 2019 with total page 133 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 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 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 Handbook of Mathematical Methods in Imaging written by Otmar Scherzer and published by Springer Science & Business Media. This book was released on 2010-11-23 with total page 1626 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

Download or read book Proceedings of 3rd International Conference on Computer Vision and Image Processing written by Bidyut B. Chaudhuri and published by Springer Nature. This book was released on 2019-10-31 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of carefully selected works presented at the Third International Conference on Computer Vision & Image Processing (CVIP 2018). The conference was organized by the Department of Computer Science and Engineering of PDPM Indian Institute of Information Technology, Design & Manufacturing, Jabalpur, India during September 29–October 01, 2018. All the papers have been rigorously reviewed by the experts from the domain. This 2 volume proceedings include technical contributions in the areas of Image/Video Processing and Analysis; Image/Video Formation and Display; Image/Video Filtering, Restoration, Enhancement and Super-resolution; Image/Video Coding and Transmission; Image/Video Storage, Retrieval and Authentication; Image/Video Quality; Transform-based and Multi-resolution Image/Video Analysis; Biological and Perceptual Models for Image/Video Processing; Machine Learning in Image/Video Analysis; Probability and uncertainty handling for Image/Video Processing; and Motion and Tracking.

Download or read book Brain and Health Informatics written by Kazayuki Imamura and published by Springer. This book was released on 2013-10-24 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Conference on Brain and Health Informatics, BHI 2013, held in Maebashi, Japan, in October 2013. The 33 revised full papers presented together with 8 workshop papers and 12 special session papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on thinking and perception-centric Investigations of human Information processing system; information technologies for curating, mining, managing and using big brain/health data; information technologies for healthcare; data analytics, data mining, and machine learning; and applications. The topics of the workshop papers are: mental health with ICT; and granular knowledge discovery in biomedical and active-media environments; and the topics of the special sessions are: human centered computing; neuro-robotics; and intelligent healthcare data analytics.

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 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 Magnetic Resonance Microscopy written by Sabina Haber-Pohlmeier and published by John Wiley & Sons. This book was released on 2022-04-20 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Microscopy Explore the interdisciplinary applications of magnetic resonance microscopy in this one-of-a-kind resource In Magnetic Resonance Microscopy: Instrumentation and Applications in Engineering, Life Science and Energy Research, a team of distinguished researchers delivers a comprehensive exploration of the use of magnetic resonance microscopy (MRM) and similar techniques in an interdisciplinary milieux. Opening with a section on hardware and methodology, the book moves on to consider developments in the field of mobile nuclear magnetic resonance. Essential processes, including filtration, multi-phase flow and transport, and a wide range of systems – from biomarkers via single cells to plants and biofilms – are discussed next. After a fulsome treatment of MRM in the field of energy research, the editors conclude the book with a chapter extoling the virtues of a holistic treatment of theory and application in MRM. Magnetic Resonance Microscopy: Instrumentation and Applications in Engineering, Life Science and Energy Research also includes: A thorough introduction to recent developments in magnetic resonance microscopy hardware and methods, including ceramic coils for MR microscopy Comprehensive explorations of applications in chemical engineering, including ultra-fast MR techniques to image multi-phase flow in pipes and reactors Practical discussions of applications in the life sciences, including MRI of single cells labelled with super paramagnetic iron oxide nanoparticles In-depth examinations of new applications in energy research, including spectroscopic imaging of devices for electrochemical storage Perfect for practicing scientists from all fields, Magnetic Resonance Microscopy: Instrumentation and Applications in Engineering, Life Science and Energy Research is an ideal resource for anyone seeking a one-stop guide to magnetic resonance microscopy for engineers, life scientists, and energy researchers.

Download or read book Compressed Sensing and Experiment Design in Magnetic Field Based Imaging written by Mirco Grosser and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work investigates the use of compressed sensing to reduce measurement times in magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). To this end, a flexible yet performant image reconstruction framework is developed. Based on this, an efficient reconstruction scheme for non Cartesian MRI is proposed and a low-rank-based method for the estimation of MPI system matrices is developed. Finally, an experiment design framework is developed to obtain optimized sampling patterns for both MRI and MPI.

Download or read book Compressed Sensing Sparse Filtering written by Avishy Y. Carmi and published by Springer Science & Business Media. This book was released on 2013-09-13 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.

Download or read book A Mathematical Introduction to Compressive Sensing written by Simon Foucart and published by Springer Science & Business Media. This book was released on 2013-08-13 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.

Download or read book Block based Compressed Sensing of Images and Video written by James E. Fowler and published by Foundations and Trends(r) in S. This book was released on 2012 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Block-Based Compressed Sensing of Images and Video overviews the emerging concept of compressed sensing (CS) with a particular focus on recent proposals for its use with a variety of imaging media, including still images, motion video, as well as multiview images and video. Throughout, it considers a variety of CS reconstruction techniques proposed in recent literature and examines relative performance of several prominent reconstruction algorithms for each of the various imagery formats. Particular emphasis is placed on block-based measurement and reconstruction which has the advantages of significantly reduced memory and computation with respect to other approaches relying on full-frame CS measurement operators. Block-Based Compressed Sensing of Images and Video employs extensive experimental comparisons to evaluate various prominent reconstruction algorithms for still-image, motion-video, and multiview scenarios in terms of both reconstruction quality as well as computational complexity. It is not intended to serve as an indepth tutorial on the theory or mathematics of compressed sensing. The coverage of CS theory is brief, while the specifics of the application of block-based compressed sensing (BCS) to natural imagery consume the bulk of the discussion.