Download or read book Combining Data Acquisition and Post processing Techniques for Magnetic Resonance Imaging of Moving Objects written by Kate McLeish and published by . This book was released on 2003 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 Fundamentals of Magnetic Resonance Imaging written by Jintong Mao and published by Independently Published. This book was released on 2019-10-25 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is in black and white printing. It was revised on 05/30/2020. Starting from complex free induction decay (FID), this book establishes a logical framework for the discussion of the principles of MRI. Based on the framework, traditional topics and some new topics are described in detail. Every formula is derived step by step at length. Essence of MRI is thoroughly discussed. It is emphasized that Fourier transform (FT) in MRI is a natural result from data acquisition if with a linear field gradient. Each concept, particularly the concept of echo, is explained in detail. For example, it is indicated that the popular drawing of an echo following a single FID (note this "single") in time axis is misleading in MRI (but may not in NMR). An echo cannot be considered as two back to back FID, etc. If you cannot accept these statements immediately, you may need to refresh your basic knowledge of MRI. The procedure from FID to MR image is accomplished by a pair of FT. The first FT is established naturally and automatically from echo acquisition. Analog digital converter leads to discrete FID. Using Nyquist sampling and quadrature phase sensitive detection (PSD), formula FOV*dk = 2pi is derived. From FOV*dk=2pi, discrete FT is derived by the summation of discrete FID directly, without relying on continuous FT. Thus, discrete FID leads to discrete FT. On other side, a discrete echo is the summation of acquired discrete FID, if re-phasing linear gradient field follows de-phasing gradient field. Thus, discrete FID also leads to discrete echo. We have the result that the discrete echo is a discrete FT (one dimensional). A series of echoes is obtained by phase encoding (raw data in two-dimensional k-space). The k-space, therefore, is a two-dimensional discrete FT (first FT). The reconstructed image is obtained by applying inverse FT (second FT) to the series of discrete echoes (k-space). Continuous FT is used as a heuristic step. But it is not necessary for the discussion of MRI. As example from FID to MR image, simulated images are obtained for graphical phantoms by using MATLAB. In appendix, MATLAB codes for image reconstruction and for some frequency selective pulses are included. Based on the framework, the topics include basic pulse sequences; pulse train; image contrasts; signal to noise ratio; ringing artifacts; aliasing artifacts; improvement of slice profile of selective pulses (Bloch equation is solved numerically using Runge-Kutta method); fat suppression; magnetization transfer; diffusion; flow image; functional MRI (fMRI for a perceptual alternation is presented), etc. Inside of the framework, emphasized topics include pulsatile ghost artifact for flow that is simulated by MATLAB and explained by interleaved zero data in k-space; experiments show that traditional explanation of flow mis-registration is not correct; the experiment also shows that the profile of laminar flow looks like a long needle, instead of ellipsoid; Stejskal-Tanner formula for b-value can be obtained by a wrong derivation, thus, the correctness of the formula may be in question; the strength of refocusing gradient for 90d selective pulse is-0.515, instead of commonly used -0.5 (small difference in refocusing strength leads to a large difference in refocusing effects due to non-linearity of Bloch equation); etc. In addition to above topics, Bloch equation with the terms T1, T2, diffusion, flow, etc. is derived by adding independent contributions to dM/dt with the assumption that T2 functions only in x-y plane. It is the hope this book is readable. It is the hope that the journey through the book might be a joy. This book will be of value to beginners. Perhaps it is valuable to a more extensive readership as well.

Download or read book Data Acquisition and Processing Techniques in Magnetic Resonance Spectroscopic Imaging written by Shantanu Sarkar and published by . This book was released on 2002 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN written by Alfonso Nieto-Castanon and published by Hilbert Press. This book was released on 2020-01-31 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook describes methods for processing and analyzing functional connectivity Magnetic Resonance Imaging (fcMRI) data using the CONN toolbox, a popular freely-available functional connectivity analysis software. Content description [excerpt from introduction] The first section (fMRI minimal preprocessing pipeline) describes standard and advanced preprocessing steps in fcMRI. These steps are aimed at correcting or minimizing the influence of well-known factors affecting the quality of functional and anatomical MRI data, including effects arising from subject motion within the scanner, temporal and spatial image distortions due to the sequential nature of the scanning acquisition protocol, and inhomogeneities in the scanner magnetic field, as well as anatomical differences among subjects. Even after these conventional preprocessing steps, the measured blood-oxygen-level-dependent (BOLD) signal often still contains a considerable amount of noise from a combination of physiological effects, outliers, and residual subject-motion factors. If unaccounted for, these factors would introduce very strong and noticeable biases in all functional connectivity measures. The second section (fMRI denoising pipeline) describes standard and advanced denoising procedures in CONN that are used to characterize and remove the effect of these residual non-neural noise sources. Functional connectivity Magnetic Resonance Imaging studies attempt to quantify the level of functional integration across different brain areas. The third section (functional connectivity measures) describes a representative set of functional connectivity measures available in CONN, each focusing on different indicators of functional integration, including seed-based connectivity measures, ROI-to-ROI measures, graph theoretical approaches, network-based measures, and dynamic connectivity measures. Second-level analyses allow researchers to make inferences about properties of groups or populations, by generalizing from the observations of only a subset of subjects in a study. The fourth section (General Linear Model) describes the mathematics behind the General Linear Model (GLM), the approach used in CONN for all second-level analyses of functional connectivity measures. The description includes GLM model definition, parameter estimation, and hypothesis testing framework, as well as several practical examples and general guidelines aimed at helping researchers use this method to answer their specific research questions. The last section (cluster-level inferences) details several approaches implemented in CONN that allow researchers to make meaningful inferences from their second-level analysis results while providing appropriate family-wise error control (FWEC), whether in the context of voxel-based measures, such as when studying properties of seed-based maps across multiple subjects, or in the context of ROI-to-ROI measures, such as when studying properties of ROI-to-ROI connectivity matrices across multiple subjects.

Download or read book Application Tailored Accelerated Magnetic Resonance Imaging Methods written by Ziwu Zhou and published by . This book was released on 2018 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a powerful diagnostic medical imaging technique that provides very high spatial resolution. By manipulating the signal evolution through careful imaging sequence design, MRI can generate a wide range of soft-tissue contrast unique to individual application. However, imaging speed remains an issue for many applications. In order to increase scan output without compromising the image quality, the data acquisition and image reconstruction methods need to be designed to fit each application to achieve maximum efficiency. This dissertation concerns several application-tailored accelerated imaging methods through improved sequence design, efficient k-space traverse, as well as tailored image reconstruction algorithm, all together aiming to exploit the full potential of data acquisition and image reconstruction in each application. The first application is ferumoxtyol-enhanced 4D multi-phase cardiovascular MRI on pediatric patients with congenital heart disease. By taking advantage of the high signal-to-noise ratio (SNR) results from contrast enhancement, we introduced two methods to improve the scan efficiency with maintained clinical utility: one with reduced scan time and one with improved temporal resolution. The first method used prospective Poisson-disc under-sampling in combination with graphics processing unit accelerated parallel imaging and compressed sensing combined reconstruction algorithm to reduce scan time by approximately 50% while maintaining highly comparable image quality to un-accelerated acquisition in a clinically practical reconstruction time. The second method utilized a motion weighted reconstruction technique to increase temporal resolution of acquired data, and thus permits improved cardiac functional assessment. Compared with existing acceleration method, the proposed method has nearly three times lower computation burden and six times faster reconstruction speed, all with equal image quality. The second application is noncontrast-enhanced 4D intracranial MR angiography with arterial spin labeling (ASL). Considering the inherently low SNR of ASL signal, we proposed to sample k-space with the efficient golden-angle stack-of-stars trajectory and reconstruct images using compressed sensing with magnitude subtraction as regularization. The acquisition and reconstruction strategy in combination produces images with detailed vascular structures and clean background. At the same time, it allows a reduced temporal blurring delineation of the fine distal arteries when compared with the conventional k-space weighted image contrast (KWIC) reconstruction. Stands upon on this, we further developed an improved stack-of-stars radial sampling strategy for reducing streaking artifacts in general volumetric MRI. By rotating the radial spokes in a golden angle manner along the partition-encoding direction, the aliasing pattern due to under-sampling is modified, resulting in improved image quality for gridding and more advanced reconstruction methods. The third application is low-latency real-time imaging. To achieve sufficient frame rate, real-time MRI typically requires significant k-space under-sampling to accelerate the data acquisition. At the same time, many real-time application, such as interventional MRI, requires user interaction or decision making based on image feedback. Therefore, low-latency on-the-fly reconstruction is highly desirable. We proposed a parallel imaging and convolutional neural network combined image reconstruction framework for low-latency and high quality reconstruction. This is achieved by compacting gradient descent steps resolved from conventional parallel imaging reconstruction as network layers and interleaved with convolutional layers in a general convolutional neural network. Once all parameters of the network are determined during the off-line training process, it can be applied to unseen data with less than 100ms reconstruction time per frame, while more than 1s is usually needed for conventional parallel imaging and compressed sensing combined reconstruction.

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 Acquisition and Processing Techniques for Image Based Prospective Motion Correction in Magnetic Resonance Imaging written by Daniel Christopher Hoinkiss and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Data Acquisition Transmission and Post Processing for Quality Spiral Magnetic Resonance Imaging written by Jean-David Jutras and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Mathematics and Physics of Emerging Biomedical Imaging written by Committee on the Mathematics and Physics of Emerging Dynamic Biomedical Imaging and published by National Academies Press. This book was released on 1996-03-13 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This cross-disciplinary book documents the key research challenges in the mathematical sciences and physics that could enable the economical development of novel biomedical imaging devices. It is hoped that the infusion of new insights from mathematical scientists and physicists will accelerate progress in imaging. Incorporating input from dozens of biomedical researchers who described what they perceived as key open problems of imaging that are amenable to attack by mathematical scientists and physicists, this book introduces the frontiers of biomedical imaging, especially the imaging of dynamic physiological functions, to the educated nonspecialist. Ten imaging modalities are covered, from the well-established (e.g., CAT scanning, MRI) to the more speculative (e.g., electrical and magnetic source imaging). For each modality, mathematics and physics research challenges are identified and a short list of suggested reading offered. Two additional chapters offer visions of the next generation of surgical and interventional techniques and of image processing. A final chapter provides an overview of mathematical issues that cut across the various modalities.

Download or read book Fast Data Acquisition and Post Processing Techniques for Functional MRI written by Xiaoping Ding and published by . This book was released on 1996 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2011 written by Gabor Fichtinger and published by Springer Science & Business Media. This book was released on 2011-09-02 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 819 submissions for presentation in three volumes. The second volume includes 83 papers organized in topical sections on diffusion weighted imaging, fMRI, statistical analysis and shape modeling, and registration.

Download or read book Accelerating Magnetic Resonance Imaging by Unifying Sparse Models and Multiple Receivers written by Daniel (Daniel Stuart) Weller and published by . This book was released on 2012 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is an increasingly versatile diagnostic tool for a variety of medical purposes. During a conventional MRI scan, samples are acquired along a trajectory in the spatial Fourier transform domain (called k-space) and the image is reconstructed using an inverse discrete Fourier transform. The affordability, availability, and applications of MRI remain limited by the time required to sample enough points of k-space for the desired field of view (FOV), resolution, and signal-to-noise ratio (SNR). GRAPPA, an accelerated parallel imaging method, and compressed sensing (CS) have been successfully employed to accelerate the acquisition process by reducing the number of k-space samples required. GRAPPA leverages the different spatial weightings of each receiver coil to undo the aliasing from the reduction in FOV induced by undersampling k-space. However, accelerated parallel imaging reconstruction methods like GRAPPA amplify the noise present in the data, reducing the SNR by a factor greater than that due to only the level of undersampling. Completely separate from accelerated parallel imaging, which capitalizes on observing data with multiple receivers, CS leverages the sparsity of the object along with incoherent sampling and nonlinear reconstruction algorithms to recover the image from fewer samples. In contrast to parallel imaging, CS actually denoises the result, because noise typically is not sparse. When reconstructing brain images, the discrete wavelet transform and finite differences are effective in producing an approximately sparse representation of the image. Because parallel imaging utilizes the multiple receiver coils and CS takes advantage of the sparsity of the image itself, these methods are complementary, and a combination of these methods would be expected to enable further acceleration beyond what is achievable using parallel imaging or CS alone. This thesis investigates three approaches to leveraging both multiple receiver coils and image sparsity. The first approach involves an optimization framework for jointly optimizing the fidelity to the GRAPPA result and the sparsity of the image. This technique operates in the nullspace of the data observation matrix, preserving the acquired data without resorting to techniques for constrained optimization. While this framework is presented generally, the effectiveness of the implementation depends on the choice of sparsifying transform, sparsity penalty function, and undersampling pattern. The second approach involves modifying the kernel estimation step of GRAPPA to promote sparsity in the reconstructed image and mitigate the noise amplification typically encountered with parallel imaging. The third approach involves imposing a sparsity prior on the coil images and estimating the full k-space from the observations using Bayesian techniques. This third method is extended to jointly estimate the GRAPPA kernel weights and the full k-space together. These approaches represent different frameworks for accelerating MRI imaging beyond current methods. The results presented suggest that these practical reconstruction and post-processing methods allow for greater acceleration with conventional Cartesian acquisitions.

Download or read book Advanced Data Acquisition and Reconstruction Techniques for Fast Radial Magnetic Resonance Imaging written by Ali Ersoz and published by . This book was released on 2016 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Motion Correction in Orbital Imaging Using MRI Compatible Eye Tracker written by Anita Dushyanth and published by . This book was released on 2014 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: The body motion of patients during magnetic resonance imaging (MRI) causes significant artifacts in the reconstructed image. Artifacts are manifested as a motion induced blur and ghost repetitions of the moving structures, which obscure vital anatomical and pathological detail. The techniques that have been proposed for suppressing motion artifacts fall into two major categories. Realtime techniques that attempt to prevent the motion from corrupting the data by restricting the data acquisition times or motion of the patients, and post-processing techniques that use information embedded in the corrupted data to restore the image. The post-processing techniques usually demand an appropriate model of the motion that requires the parameters be determined in order to invert the data degradation process. However, motion is manifested differently depending on the time and duration it occurred during Magnetic Resonance (MR) data acquisition. Estimating motion parameters from such cases are heavily based on assumptions and the reconstructed image is compromised on either contrast or resolution. A major challenge in high resolution MR imaging of the orbit (eyeball and associated tissues in the eye socket) is image degradation by artifacts resulting from eye movements and eyelid blinks. In this thesis a novel method for motion correction has been developed by incorporating an optical sensor that detects these eye movements during MR scan acquisition without generating signal artifacts, and which is not affected by either the strong static magnetic field or the pulsed field gradients. Detection of the subjects eye movements and blinks is essential for determining the exact times during the MR scan when each such movement occurred. This thesis presents a method for refining orbital MRI techniques to compensate for the effects of blinking and fixation instability. It employs an eye tracker system to track eye/eyelid movements in the MRI studies of strabismus in humans that is based on infrared (IR) light reflection. It incorporates custom-fabricated optical fiber probes that illuminate the eye with low intensity infrared light, while eye/eyelid movements are detected by changes in ocular surface reflectance transmitted by another optical fiber cable coupled to a photodiode. Additionally, there is another light source that serves as a visible point target for ocular fixation during MRI scanning. The volunteer's eye movements are recorded simultaneously while the orbit is scanned using MRI. The output signal from the detector is amplified and synchronized in time with the MR acquired data. Image data corrupted by motion is flagged so that the affected data can be removed during image reconstruction. The purpose of this experiment is to outline experimental protocols for acquiring and correcting the above mentioned images in high quality, discuss these protocols from a wide range of perspectives, and finally present some observations on pilot data from volunteer subjects as well as patient with pathology. The MRI methodology developed was able to suppress motion artifacts considerably to provide interpretable MRI images.