An Adaptive Signal Processing Approach to Dynamic Magnetic Resonance Imaging PDF Download
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Author: William Scott Hoge
Publisher:
ISBN:
Category : Magnetic resonance imaging
Languages : en
Pages : 274
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Book Description
Author: William Scott Hoge
Publisher:
ISBN:
Category : Magnetic resonance imaging
Languages : en
Pages : 274
Get Book Here
Book Description
Author: Zhi-Pei Liang
Publisher: Wiley-IEEE Press
ISBN:
Category : Medical
Languages : en
Pages : 442
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Book Description
In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, "father of the MRI", and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: Mathematical fundamentals Signal generation and detection principles Signal characteristics Signal localization principles Image reconstruction techniques Image contrast mechanisms Image resolution, noise, and artifacts Fast-scan imaging Constrained reconstruction Complete with a comprehensive set of examples and homework problems, Principles of Magnetic Resonance Imaging is the must-read book to improve your knowledge of this revolutionary technique.
Author: Hong Yan
Publisher: CRC Press
ISBN: 9780203908785
Category : Technology & Engineering
Languages : en
Pages : 676
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Book Description
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
Author: Luigi Landini
Publisher: CRC Press
ISBN: 1420028669
Category : Technology & Engineering
Languages : en
Pages : 632
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Book Description
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.
Author: Huajun She
Publisher:
ISBN:
Category : Diagnostic imaging
Languages : en
Pages : 115
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Book Description
Author: Jörg Polzehl
Publisher: Springer Nature
ISBN: 3030291847
Category : Medical
Languages : en
Pages : 231
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Book Description
This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. The book concludes with extended appendices providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources.
Author: I. Jung Feng
Publisher:
ISBN:
Category :
Languages : en
Pages : 158
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Book Description
Conventional fMRI image analysis is performed by carrying out a massive number of parallel regression analyses. fMRI signal is known for its low signal-noise-ratio, and its complexity, such as reflected by spatial and temporal autocorrelation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. In real time fMRI, signal processing is carried out while the signal is being observed. This method allows for the dynamic adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (voxel-wise SPRT) approach for dynamically localizing activation associated with stimuli, as well as decision rules for the stopping of experimentation. Stopping is dynamically determined when sufficient statistical evidence is collected to assess the activation status of voxels across regions of interest. Simulation studies show that the number of scan units can be reduced substantially compared to standard fMRI experimental designs that are fixed and predetermined, while still achieving comparably high levels of classification accuracy. An analysis based on actual brain imaging confirms the promise of this approach.An interesting application of dynamic adjustment of fMRI stimuli is in the area of Alzheimer's disease (AD). It is clear that there is a fair amount of heterogeneity in the cognitive course of the disease. This has led to the development of theories related to the notion of cognitive reserve, which posits that neural capacity, efficiency, and plasticity play a role in this heterogeneity. It has been further hypothesized that cognitive reserve levels at pre-symptomatic stage of AD will manifest specific neural activation patterns under carefully designed fMRI experimentation that systematically varies difficulty levels of a targeted task. A sequential testing approach is proposed for efficiently and accurately identifying and classifying such patterns. Methods for characterizing cognitive reserve that are studied here are comprised of two approaches. The first is sequential estimation through monitoring confidence interval lengths over a range of experimental conditions to assess efficiency and capacity. The other is sequential selection of difficulty levels, to detect neural compensation, which is a reflection of plasticity. Both approaches show high efficiencies and high detection accuracies in our fMRI simulation studies. These two approaches open up new possibilities for studying and characterizing cognitive reserve, which will in turn lead to a better understanding of processes in AD.
Author: Santiago Aja-Fernández
Publisher: Springer
ISBN: 9783319399331
Category : Computers
Languages : en
Pages : 0
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Book Description
This unique text presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques. Features: provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques; describes noise and signal estimation for MRI from a statistical signal processing perspective; surveys the different methods to remove noise in MRI acquisitions from a practical point of view; reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions; examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal; includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets.
Author: Bhabesh Deka
Publisher: Springer
ISBN: 9811335974
Category : Technology & Engineering
Languages : en
Pages : 122
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Book Description
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.
Author: Jörg Polzehl
Publisher: Springer Nature
ISBN: 3031389492
Category : Medical
Languages : en
Pages : 268
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Book Description
This book discusses modelling and analysis of Magnetic Resonance Imaging (MRI) data of the human brain. For the data processing pipelines we rely on R, the software environment for statistical computing and graphics. The book is intended for readers from two communities: Statisticians, who are interested in neuroimaging and look for an introduction to the acquired data and typical scientific problems in the field and neuroimaging students, who want to learn about the statistical modeling and analysis of MRI data. Being a practical introduction, the book focuses on those problems in data analysis for which implementations within R are available. By providing full worked-out examples the book thus serves as a tutorial for MRI analysis with R, from which the reader can derive its own data processing scripts. The book starts with a short introduction into MRI. The next chapter considers the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters then cover four common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, Multi-Parameter Mapping and Inversion Recovery MRI. The book concludes with extended Appendices on details of the utilize non-parametric statistics and on resources for R and MRI data. The book also addresses the issues of reproducibility and topics like data organization and description, open data and open science. It completely relies on a dynamic report generation with knitr: The books R-code and intermediate results are available for reproducibility of the examples.
