Real-time High-resolution Functional Magnetic Resonance Imaging with GPU Parallel Computations PDF Download

Author: Zhongnan Fang
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Functional magnetic resonance imaging (fMRI) is a technique that enables non-invasive monitoring of brain activity by detecting changes in blood oxygenation levels. With recent advancements in high performance computing and MRI hardware, real-time fMRI has become possible and the spatiotemporal resolution of fMRI has been significantly improved. However, there are still many challenges for fMRI to achieve its full potential. First, because many basic real-time fMRI modules still uses a large portion of the available processing time, there is insufficient time for the integration of advanced real-time fMRI techniques. Second, current high-resolution fMRI techniques do not provide the resolution needed for imaging activity of small but critical brain regions, such as cortical layers and hippocampal sub-regions. Third, it is still not trivial to achieve the high-resolution and real-time fMRI at once because significant higher computation power is needed. To address these challenges, three projects were conducted and illustrated in this dissertation. In the first project, a high-throughput real-time fMRI system is designed on the graphics processing unit (GPU) to overcome computation barriers associated with reconstruction of non-uniformly sampled image, motion correction and statistical analysis. This system achieves an overall processing speed of 15.01 ms per 3D image, which is more than 49-fold faster than widely used software packages. The high processing speed also enables sliding window reconstruction, which improves the temporal resolution. With this ultra high speed fMRI system, integration of CS reconstruction for real-time and high spatiotemporal resolution fMRI becomes possible. The second project explores the feasibility of CS fMRI and demonstrates a High SPAtial Resolution compressed SEnsing (HSPARSE) fMRI method. HSPARSE fMRI enables a 6-fold spatial resolution improvement with contrast to noise ratio (CNR) increase and no loss of temporal resolution. A novel randomly under-sampled, variable density spiral data acquisition trajectory is designed to achieve an imaging speed acceleration factor of 5.3, which is 32 \% higher than previously reported CS fMRI methods. HSPARSE fMRI also achieves high sensitivity and low false positive rate. Importantly, its high spatial resolution enables localization of brain regions that cannot be resolved using the highest spatial resolution fully-sampled reconstruction. The third project combines the methods in the previous two into a real-time high-resolution CS fMRI system. A random stack of variable density spiral trajectory is first designed to achieve highly incoherent CS sampling and 3.2 times imaging speed acceleration. An optimized CS reconstruction algorithm using wavelet regularization is then implemented on GPU, which achieves a reconstruction speed of 605 ms per 3D image. This method also achieves a 4-fold spatial resolution improvement, with increased CNR, high sensitivity, low false positive rate and no loss of temporal resolution. Notably, this is the first system that achieves the real-time 3D non-uniformly sampled image CS fMRI reconstruction.

Author: Nguyen Van Le
Publisher:
ISBN:
Category :
Languages : en
Pages : 65

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Optogenetic functional magnetic resonance imaging (ofMRI) [1] is a powerful new technology that enables precise control of brain circuit elements while monitoring their causal outputs. To bring ofMRI to its full potential, it is essential to achieve high-spatial resolution with minimal distortions. With our proposed compressed sensing (CS) enabled method, high-spatial resolution ofMRI images can be obtained with a large field of view (FOV) without increasing spatial distortions and the amount of acquired data. The ofMRI data were sampled with passband balanced steady-state free precession (b-SSFP) [8, 17] fast stack-of-spiral sequence in order to achieve ultra-high-spatial resolution images in a short amount of time. Interleaves of data were randomly collected. The images were recovered from the undersampled k-space data by solving an unconstrained convex optimization problem, which balances the trade-off between data consistency and sparsity. The optimization problem can be solved by gradient descent combined with backtracking line search algorithms. Discrete cosine transform (DCT) were chosen as a sparsifying transform. The ofMRI image reconstruction was processed in parallel on a graphics processing unit (GPU) using C/C++ language supported by NVIDIA CUDA engine in order to achieve short reconstruction time. An existing nonequispaced fast Fourier transform (NFFT) algorithm [13, 14] was modified for our GPU parallel processing purpose. The results demonstrate that the compressed sensing reconstructed image has higher resolution while maintaining a precise activation map, compared to a fully sampled low-resolution image with the same amount of data and scan time. A 4-D image can be reconstructed in less than fifteen minutes, which allows compressed sensing ofMRI to become a practical application.

Author: Michelle Hampson
Publisher: Academic Press
ISBN: 0128224363
Category : Computers
Languages : en
Pages : 366

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Book Description
fMRI Neurofeedback provides a perspective on how the field of functional magnetic resonance imaging (fMRI) neurofeedback has evolved, an introduction to state-of-the-art methods used for fMRI neurofeedback, a review of published neuroscientific and clinical applications, and a discussion of relevant ethical considerations. It gives a view of the ongoing research challenges throughout and provides guidance for researchers new to the field on the practical implementation and design of fMRI neurofeedback protocols. This book is designed to be accessible to all scientists and clinicians interested in conducting fMRI neurofeedback research, addressing the variety of different knowledge gaps that readers may have given their varied backgrounds and avoiding field-specific jargon. The book, therefore, will be suitable for engineers, computer scientists, neuroscientists, psychologists, and physicians working in fMRI neurofeedback. - Provides a reference on fMRI neurofeedback covering history, methods, mechanisms, clinical applications, and basic research, as well as ethical considerations - Offers contributions from international experts—leading research groups are represented, including from Europe, Japan, Israel, and the United States - Includes coverage of data analytic methods, study design, neuroscience mechanisms, and clinical considerations - Presents a perspective on future translational development

Author: David B. Kirk
Publisher: Newnes
ISBN: 0123914183
Category : Computers
Languages : en
Pages : 519

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Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

Author: Lori A. Whitten
Publisher: RTI Press
ISBN:
Category : Medical
Languages : en
Pages : 36

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Book Description
The sophisticated methods of neuroscience—including molecular genetics, structural and functional neuroimaging, animal models, and experimental tasks that approximate real-world behaviors in human research—have yielded important insights about typical functioning and neurobehavioral disorders. Translational neuroscience endeavors to use this knowledge to improve the human condition by developing and improving interventions for these disorders. This paper reviews the literature on the contribution of functional magnetic resonance imaging (fMRI) and two related techniques, resting-state fMRI (rs-fMRI) and real-time fMRI (rt-fMRI), to the diagnosis and treatment of behavioral problems and psychiatric disorders. It also explains how incorporating neuroscience principles and techniques into research on the prevention of substance misuse and antisocial behavior may spur advances and innovations in this important area. This article argues that fMRI’s potential contribution to these prevention efforts has yet to be fully realized, explores new ways in which the technique could be adapted to that end, highlights some of the work by researchers in the vanguard of this effort, and notes limitations of fMRI and ethical concerns the technique raises.

Author: Ziwu Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages : 180

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Book Description
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.

Author: Heidi Johansen-Berg
Publisher: Academic Press
ISBN: 0124055095
Category : Medical
Languages : en
Pages : 627

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Book Description
Diffusion MRI remains the most comprehensive reference for understanding this rapidly evolving and powerful technology and is an essential handbook for designing, analyzing, and interpreting diffusion MR experiments. Diffusion imaging provides a unique window on human brain anatomy. This non-invasive technique continues to grow in popularity as a way to study brain pathways that could never before be investigated in vivo. This book covers the fundamental theory of diffusion imaging, discusses its most promising applications to basic and clinical neuroscience, and introduces cutting-edge methodological developments that will shape the field in coming years. Written by leading experts in the field, it places the exciting new results emerging from diffusion imaging in the context of classical anatomical techniques to show where diffusion studies might offer unique insights and where potential limitations lie. - Fully revised and updated edition of the first comprehensive reference on a powerful technique in brain imaging - Covers all aspects of a diffusion MRI study from acquisition through analysis to interpretation, and from fundamental theory to cutting-edge developments - New chapters covering connectomics, advanced diffusion acquisition, artifact removal, and applications to the neonatal brain - Provides practical advice on running an experiment - Includes discussion of applications in psychiatry, neurology, neurosurgery, and basic neuroscience - Full color throughout

Author: Francesco S. Pavone
Publisher: CRC Press
ISBN: 0429530900
Category : Science
Languages : en
Pages : 439

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Book Description
The Handbook of Neurophotonics provides a dedicated overview of neurophotonics, covering the use of advanced optical technologies to record, stimulate, and control the activity of the brain, yielding new insight and advantages over conventional tools due to the adaptability and non-invasive nature of light. Including 32 colour figures, this book addresses functional studies of neurovascular signaling, metabolism, electrical excitation, and hemodynamics, as well as clinical applications for imaging and manipulating brain structure and function. The unifying theme throughout is not only to highlight the technology, but to show how these novel methods are becoming critical to breakthroughs that will lead to advances in our ability to manage and treat human diseases of the brain. Key Features: Provides the first dedicated book on state-of-the-art optical techniques for sensing and imaging across at the cellular, molecular, network, and whole brain levels. Highlights how the methods are used for measurement, control, and tracking of molecular events in live neuronal cells, both in basic research and clinical practice. Covers the entire spectrum of approaches, from optogenetics to functional methods, photostimulation, optical dissection, multiscale imaging, microscopy, and structural imaging. Includes chapters that show use of voltage-sensitive dye imaging, hemodynamic imaging, multiphoton imaging, temporal multiplexing, multiplane microscopy, optoacoustic imaging, near-infrared spectroscopy, and miniature neuroimaging devices to track cortical brain activity.

Author: Ignacio Rojas
Publisher: Springer Nature
ISBN: 3030453855
Category : Science
Languages : en
Pages : 843

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Book Description
This volume constitutes the proceedings of the 8th International Work-Conference on IWBBIO 2020, held in Granada, Spain, in May 2020. The total of 73papers presented in the proceedings, was carefully reviewed and selected from 241 submissions. The papers are organized in topical sections as follows: Biomarker Identification; Biomedical Engineering; Biomedical Signal Analysis; Bio-Nanotechnology; Computational Approaches for Drug Design and Personalized Medicine; Computational Proteomics and Protein-Protein Interactions; Data Mining from UV/VIS/NIR Imaging and Spectrophotometry; E-Health Technology, Services and Applications; Evolving Towards Digital Twins in Healthcare (EDITH); High Performance in Bioinformatics; High-Throughput Genomics: Bioinformatic Tools and Medical Applications; Machine Learning in Bioinformatics; Medical Image Processing; Simulation and Visualization of Biological Systems.

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.