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 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 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 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 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 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 MRI written by Mariya Doneva and published by Sudwestdeutscher Verlag Fur Hochschulschriften AG. This book was released on 2011 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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 Compressed Sensing Applied to Dynamic Cardiac Magnetic Resonance Imaging written by Muhammad Usman and published by . This book was released on 2011 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing (CS) is a data-reduction technique that has been applied to speed up the acquisition in magnetic resonance imaging (MRI). However, the use of this technique in dynamic MR applications has been limited in terms of the maximum achievable reduction factor. In general, noise-like artefacts and bad temporal fidelity are visible in standard CS MRI reconstructions when high reduction factors are employed. Also, due to nonlinear reconstruction algorithms, the CS based reconstructions are generally very slow. In this thesis, for dynamic cardiac MR data, we propose novel CS reconstruction methods with improved performance and better computational efficiency and a novel CS based data acquisition method. A novel CS reconstruction method titled 'K-t group sparse' method is proposed. This method exploits the structure within the sparse representation by enforcing the support components to be in the form of groups. These groups act like a constraint in the CS reconstruction. Results show that this method can achieve high reduction factors with improved spatial and temporal quality compared to the standard CS techniques. Two simple extensions of K-t group sparse method are also presented together with the results. To improve the CS reconstruction times, we propose a computationally efficient orthogonal matching pursuit (OMP) based reconstruction specifically suited to cardiac MR data. Using the energy distribution in the sparse representation, this method achieves significant reduction in the reconstruction time. Furthermore, for CS based data acquisition, we propose a novel method that combines the RF encoding with undersampled gradient encoding (RFuGE). This method has the advantage of avoiding the undesirable gradient switching required for random undersampling with gradient only encoding.

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 Spiral Random Encoding for Compressed Sensing in Magnetic Resonance Imaging MRI written by Gajanan Nagarsekar and published by . This book was released on 2012 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in Electronics Communication and Computing written by Akhtar Kalam and published by Springer. This book was released on 2017-10-27 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compilation of research work in the interdisciplinary areas of electronics, communication, and computing. This book is specifically targeted at students, research scholars and academicians. The book covers the different approaches and techniques for specific applications, such as particle-swarm optimization, Otsu’s function and harmony search optimization algorithm, triple gate silicon on insulator (SOI)MOSFET, micro-Raman and Fourier Transform Infrared Spectroscopy (FTIR) analysis, high-k dielectric gate oxide, spectrum sensing in cognitive radio, microstrip antenna, Ground-penetrating radar (GPR) with conducting surfaces, and digital image forgery detection. The contents of the book will be useful to academic and professional researchers alike.

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 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 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 Compressed Sensing written by Yonina C. Eldar and published by Cambridge University Press. This book was released on 2012-05-17 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. This book provides the first detailed introduction to the subject, highlighting theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this an ideal resource for researchers, graduate students and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing and algorithms for efficient data processing.