Download or read book Accelerated Dynamic Magnetic Resonance Imaging written by Jennifer Steeden and published by Academic Press. This book was released on 2023-09-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging (MRI) scans play a vital role in diagnosis and monitoring of diseases across the body. However, MRI is a relatively slow imaging technology, resulting in long scan times. This is particularly challenging when imaging dynamic processes. Accelerated Dynamic Magnetic Resonance Imaging: Methods and Applications explains the technologies which can speed up MRI imaging and shows how they have been applied to a broad range of application areas, presenting the challenges and giving practical advice on implementation. With this book the reader will be able to: Modify the MRI sequences to speed up acquisition of data (non-Cartesian trajectories and data under sampling); Use the techniques (parallel imaging, compressed sensing and machine learning) which are commonly used to reconstruct under sampled MRI data; Implement fast MRI imaging techniques for their application areas. Accelerated Dynamic Magnetic Resonance Imaging: Methods and Applications is an ideal resource for the technologist, clinical researcher and clinician who want to understand rapid MRI methods and gain practical advice on their implementation.

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 Accelerating Pediatric Magnetic Resonance Imaging Using Deep Learning based Image Reconstruction written by Christopher Michael Sandino and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a powerful diagnostic tool for visualizing soft tissue anatomy, but physical limits on data acquisition speed result in uncomfortably long MRI exams. This is problematic for many patient populations, but especially for pediatric patients, who often require general anesthesia (GA) to reduce anxiety and body motion. Many attempts at accelerating data acquisition have been made to reduce or eliminate use of GA for pediatric MRI. For example, compressed sensing (CS) methods have been used to iteratively reconstruct rapidly acquired measurements into high-quality images by leveraging sparse priors. More recently, deep learning (DL) methods have been used to train deep neural network models to map the rapidly acquired measurements into even higher-quality images. While DL reconstruction approaches may potentially accelerate data acquisition beyond CS, these approaches have several issues which impede their clinical adoption. First, DL reconstructions require large quantities of high-quality ground truth data for supervised training, which can be costly and time-consuming to acquire. Second, memory requirements during network training limit the applicability of DL reconstruction to low-dimensional MRI data, such as static or dynamic 2-D imaging with limited spatiotemporal resolution. In this thesis, a series of projects demonstrating robust DL reconstruction techniques for acceleration of high-dimensional pediatric MRI will be presented. First, physics-based models are incorporated into deep CNN architectures to enforce consistency between intermediate network outputs and the rapidly acquired measurements in a novel method called DL-ESPIRiT. Physics-based modeling allows DL-ESPIRiT to be trained end-to-end in a supervised fashion with relatively little training data compared to non-physics-driven DL reconstruction. DL-ESPIRiT is applied and validated on 12X prospectively accelerated dynamic 2-D MRI scans acquired at Lucile Packard Children's Hospital. Finally, DL-ESPIRiT is extended to leverage subspace methods within the network to address GPU memory limitations during training. This method, known as deep learning-based subspace reconstruction (DL-Subspace), reconstructs a compressed representation of the MRI data instead of the data directly, thereby reducing the memory footprint during training and accelerating DL inference times. DL-Subspace is demonstrated to reconstruct 2-D dynamic MRI data with 4X higher memory efficiency and inference speed.

Download or read book Handbook of Intelligent Computing and Optimization for Sustainable Development written by Mukhdeep Singh Manshahia and published by John Wiley & Sons. This book was released on 2022-02-11 with total page 944 pages. Available in PDF, EPUB and Kindle. Book excerpt: HANDBOOK OF INTELLIGENT COMPUTING AND OPTIMIZATION FOR SUSTAINABLE DEVELOPMENT This book provides a comprehensive overview of the latest breakthroughs and recent progress in sustainable intelligent computing technologies, applications, and optimization techniques across various industries. Optimization has received enormous attention along with the rapidly increasing use of communication technology and the development of user-friendly software and artificial intelligence. In almost all human activities, there is a desire to deliver the highest possible results with the least amount of effort. Moreover, optimization is a very well-known area with a vast number of applications, from route finding problems to medical treatment, construction, finance, accounting, engineering, and maintenance schedules in plants. As far as optimization of real-world problems is concerned, understanding the nature of the problem and grouping it in a proper class may help the designer employ proper techniques which can solve the problem efficiently. Many intelligent optimization techniques can find optimal solutions without the use of objective function and are less prone to local conditions. The 41 chapters comprising the Handbook of Intelligent Computing and Optimization for Sustainable Development by subject specialists, represent diverse disciplines such as mathematics and computer science, electrical and electronics engineering, neuroscience and cognitive sciences, medicine, and social sciences, and provide the reader with an integrated understanding of the importance that intelligent computing has in the sustainable development of current societies. It discusses the emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative intelligent techniques in a variety of sectors, including IoT, manufacturing, optimization, and healthcare. Audience It is a pivotal reference source for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in the field of artificial intelligence in the areas of Internet of Things, renewable energy, optimization, and smart cities.

Download or read book MRI written by Angshul Majumdar and published by CRC Press. This book was released on 2018-09-03 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of magnetic resonance imaging (MRI) has developed rapidly over the past decade, benefiting greatly from the newly developed framework of compressed sensing and its ability to drastically reduce MRI scan times. MRI: Physics, Image Reconstruction, and Analysis presents the latest research in MRI technology, emphasizing compressed sensing-based image reconstruction techniques. The book begins with a succinct introduction to the principles of MRI and then: Discusses the technology and applications of T1rho MRI Details the recovery of highly sampled functional MRIs Explains sparsity-based techniques for quantitative MRIs Describes multi-coil parallel MRI reconstruction techniques Examines off-line techniques in dynamic MRI reconstruction Explores advances in brain connectivity analysis using diffusion and functional MRIs Featuring chapters authored by field experts, MRI: Physics, Image Reconstruction, and Analysis delivers an authoritative and cutting-edge treatment of MRI reconstruction techniques. The book provides engineers, physicists, and graduate students with a comprehensive look at the state of the art of MRI.

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 Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology written by Alan Jackson and published by Springer Science & Business Media. This book was released on 2005-11-02 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic contrast-enhanced MRI is now established as the methodology of choice for the assessment of tumor microcirculation in vivo. The method assists clinical practitioners in the management of patients with solid tumors and is finding prominence in the assessment of tumor treatments, including anti-angiogenics, chemotherapy, and radiotherapy. Here, leading authorities discuss the principles of the methods, their practical implementation, and their application to specific tumor types. The text is an invaluable single-volume reference that covers all the latest developments in contrast-enhanced oncological MRI.

Download or read book Model based Reconstruction of Accelerated Quantitative Magnetic Resonance Imaging MRI written by Wajiha Bano and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improvements to Highly Accelerated Parallel Magnetic Resonance Imaging written by Richard Winkelmann and published by . This book was released on 2007 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support written by M. Jorge Cardoso and published by Springer. This book was released on 2017-09-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Download or read book Improved Time Series Reconstruction for Dynamic Magnetic Resonance Imaging written by Uygar Sümbül and published by . This book was released on 2009 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Novel Adaptive Reconstruction Schemes for Accelerated Myocardial Perfusion Magnetic Resonance Imaging written by Sajan Goud Lingala and published by . This book was released on 2013 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation deals with developing novel image reconstruction methods in conjunction with non-Cartesian sampling for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. The reconstruction methods are based on adaptive signal models to represent the dynamic data using few model coefficients. Three novel adaptive reconstruction methods are developed and validated: (a) low rank and sparsity based modeling, (b) blind compressed sensing, and (c) motion compensated compressed sensing. The developed methods are applicable to a wide range of dynamic imaging problems. In the context of MR-MPI, this dissertation show feasibilities that the developed methods can enable free breathing myocardial perfusion MRI acquisitions with high spatio-temporal resolutions (

Download or read book Reconstruction Methods for Accelerated Magnetic Resonance Imaging written by Tao Zhang and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a powerful medical imaging modality widely used in clinical practice. MRI provides excellent soft-tissue contrast, and does not involve ionizing radiation. In an ideal clinical setting for MRI, several requirements have to be met. First of all, diagnostic image quality has to be achieved. Second, fast image reconstruction is required, so that the radiologists can review the images before releasing the patients. Third, fast data acquisition is desired. Short scan time can not only improve patient comfort, but also reduce many imaging artifacts and improve image quality. While advanced methods, such as parallel imaging and compressed sensing, can accelerate MRI data acquisition to some extent, the achievable scan time is still very limited for several MR applications. Meanwhile, the reconstruction time for these advanced methods can take up to hours, and become clinically infeasible. This dissertation describes approaches to maintain a clinically feasible reconstruction time for advanced reconstructions, and approaches to further accelerate MRI applications, specifically MR parameter mapping and dynamic contrast-enhanced (DCE) MRI. The ultimate goal of this work is to make MRI more clinically practical. To maintain a clinically feasible reconstruction time for advanced reconstructions with large coil arrays, a geometric-decomposition coil compression method is proposed. The proposed method exploits the spatially varying data redundancy of large coil arrays, and can compress the raw data from original coils into very few virtual coils. The advanced reconstruction can be directly performed on the virtual coils instead of the original coils. The reconstruction time for large 3D datasets, acquired with 32-channel coils and reconstructed by a combined parallel imaging compressed sensing method, can be reduced to under a minute. The proposed method has been implemented in Lucile Packard Children's Hospital at Stanford. The clinical evaluation suggests that the proposed method can achieve very fast reconstruction without compromising overall image quality and delineation of anatomical structures. MR parameter mapping is a promising approach to characterize intrinsic tissue-dependent information. To accelerate lengthy MR parameter mapping, which can take up to half an hour or more, a locally low-rank method has been proposed. The proposed method has been combined with parallel imaging to achieve further acceleration. Based on preliminary result, the combined parallel imaging locally low-rank method can accelerate variable flip angle T1 mapping by factor of 6, without obvious imaging artifacts. DCE MRI is a standard component of abdominal MRI exams, most commonly used to detect and characterize mass lesions and assess renal function. 3D DCE MRI is often limited compromised spatiotemporal resolution and motion artifacts. In this work, a combined locally low-rank parallel imaging method with soft gating is proposed. The proposed method can significantly reduce motion artifacts for completely free-breathing acquisition and remove the need for deep anesthesia. The high spatiotemporal resolution achieved by the proposed method can also capture the rapid contrast hemodynamics. The proposed method has been deployed clinically in Lucile Packard Children's Hospital at Stanford. Preliminary clinical evaluation results suggest that the proposed method can achieve an image quality very close to a respiratory-triggered data acquisition, but with much higher spatiotemporal resolution.

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 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expecting the reader to have some basic training in liner algebra and optimization, the book begins with a general discussion on CS techniques and algorithms. It moves on to discussing single channel static MRI, the most common modality in clinical studies. It then takes up multi-channel MRI and the interesting challenges consequently thrown up in signal reconstruction. Off-line and on-line techniques in dynamic MRI reconstruction are visited. Towards the end the book broadens the subject by discussing how CS is being applied to other areas of biomedical signal processing like X-ray, CT and EEG acquisition. The emphasis throughout is on qualitative understanding of the subject rather than on quantitative aspects of mathematical forms. The book is intended for MRI engineers interested in the brass tacks of image formation; medical physicists interested in advanced techniques in image reconstruction; and mathematicians or signal processing engineers.

Download or read book Slice Accelerated Magnetic Resonance Imaging written by Cornelius Eichner and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Introduction to Functional Magnetic Resonance Imaging written by Richard B. Buxton and published by Cambridge University Press. This book was released on 2002-01-07 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional Magnetic Resonance Imaging (fMRI) is now a standard tool for mapping activation patterns in the human brain. This highly interdisciplinary field involves neuroscientists and physicists as well as clinicians who need to understand the rapidly increasing range, flexibility and sophistication of the techniques. In this book, Richard Buxton, a leading authority on fMRI, provides an invaluable introduction for this readership to how fMRI works, from basic principles and the underlying physics and physiology, to newer techniques such as arterial spin labeling and diffusion tensor imaging.

Download or read book Reduced data Magnetic Resonance Imaging Reconstruction Methods written by Lei Hou Hamilton and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Imaging speed is very important in magnetic resonance imaging (MRI), especially in dynamic cardiac applications, which involve respiratory motion and heart motion. With the introduction of reduced-data MR imaging methods, increasing acquisition speed has become possible without requiring a higher gradient system. But these reduced-data imaging methods carry a price for higher imaging speed. This may be a signal-to-noise ratio (SNR) penalty, reduced resolution, or a combination of both. Many methods sacrifice edge information in favor of SNR gain, which is not preferable for applications which require accurate detection of myocardial boundaries. The central goal of this thesis is to develop novel reduced-data imaging methods to improve reconstructed image performance. This thesis presents a novel reduced-data imaging method, PINOT (Parallel Imaging and NOquist in Tandem), to accelerate MR imaging. As illustrated by a variety of computer simulated and real cardiac MRI data experiments, PINOT preserves the edge details, with flexibility of improving SNR by regularization. Another contribution is to exploit the data redundancy from parallel imaging, rFOV and partial Fourier methods. A Gerchberg Reduced Iterative System (GRIS), implemented with the Gerchberg-Papoulis (GP) iterative algorithm is introduced. Under the GRIS, which utilizes a temporal band-limitation constraint in the image reconstruction, a variant of Noquist called iterative implementation iNoquist (iterative Noquist) is proposed. Utilizing a different source of prior information, first combining iNoquist and Partial Fourier technique (phase-constrained iNoquist) and further integrating with parallel imaging methods (PINOT-GRIS) are presented to achieve additional acceleration gains.