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Emerging technologies in parallel magnetic resonance imaging (MRI) with massive receiver arrays have paved the way for ultra-fast imaging at increasingly high frame rates. With the increase in the number of receiver channels used to implement parallel imaging techniques, there is a corresponding increase in the amount of data that needs to be processed, slowing down the process of image reconstruction. To develop a complete reconstruction system which is easy to assemble in a single computer for a real-time rendition of images is a relevant challenge demanding dedicated resources for high speed digital data transfer and computation. We have enhanced a 64 channel parallel receiver system designed for single echo acquisition (SEA) MRI into a real-time imaging system by interfacing it with two commercially available digital signal processor (DSP) boards which are capable of transferring large amounts of digital data via a dedicated bus from two high performance digitizer boards. The resulting system has been used to demodulate raw image data in real-time data and store them at rates of 200 frames per second (fps) and subsequently display the processed data at rates of 26 fps. A further interest in realtime reconstruction techniques is to reduce the data handling issues. Novel ways to minimize the digitized data are presented using reduced sampling rate techniques. The proposed techniques reduce the amount of data generated by a factor of 5 without compromising the SNR and with no additional hardware. Finally, the usability of this tool is demonstrated by investigating fast imaging applications. Of particular interest among them are MR elastography applications. An exploratory study of SEA MRE was done to study the temperature dependency of shear stiffness in an agarose gel and the results correlate well with existing literature. With the ability to make MRE images in a single echo, the SEA MRE technique has an advantage over the conventional MRE techniques.
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.
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 - 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
Magnetic Resonance Imaging (MRI) is considered as a slow imaging technique. Various approaches to accelerate MRI imaging have been explored by researchers in the past decades. Earlier gradient based methods have reached the safety limit. Parallel receiving techniques achieve accelerations by reducing phase encoding steps. Among these methods, SEA Imaging achieved the highest possible acceleration by completely eliminating phase encoding. However, SEA imaging is limited to thin planar slices above the array due to the correction needed for the inherent phase cancellation caused by voxel-sized coils. A phase compensation gradient pulse is used for this correction in SEA imaging. This phase compensation is dependent on slice position and thickness as well as the orientation of the array elements, placing stringent restrictions on SEA imaging, limiting its applications. Converting the SEA system into Transmit / Receive (T/R) mode, which is the main purpose of this study, eliminates the requirement for phase compensation gradient because phase departed during transmit is refocused during receiving. Independent amplitude and phase control of RF pulse for each coil of a SEA array is achieved using a low cost scalable parallel transmit system design. The first 64-channel parallel transmitter for MRI in the world is constructed and tested. Software is also developed to control the phase and amplitude of all the 64 channels of RF excitation pulses independently through National Instruments DAQ system. The system consists of vector modulators controlled by digital controlled potentiometers, two-stage amplifiers and T/R switches on the transmit side. All these are combined with newly designed and constructed preamplifiers and the existing 64-channel parallel receivers on the receive side, leading to the only 64-channel parallel T/R system available for MRI. As a bonus, the system can be easily updated to full Transmit SENSE capability. Furthermore, simulations and images are done to synthesize transmit patterns thanks to the large channel count. Testing results show that the system is capable of 100W per channel simultaneous transmission. Using this system, transmit field can be synthesized by varying the phase and amplitude across channel without traditionally required complicated pulse sequences involving simultaneous RF and gradient fields. Curved slice excitation has conventionally been considered a difficult task for MRI, achievable only through complicated pulses sequences. Using this system and flexible array wrapped around the subject to be imaged, the system is able to excite curved slice using one shot. TR images indicate that the system is capable of high speed surface imaging at 200 frames per second following the surface of a flexible SEA array coil which has not been achieved using other methods in MRI.
Intravoxel incoherent motion (IVIM) refers to translational movements which within a given voxel and during the measurement time present a distribution of speeds in orientation and/or amplitude. The concept was introduced in 1986 together with the foundation of diffusion MRI because it had been realized that flow of blood in capillaries (perfusion) would mimic a diffusion process and impact diffusion MRI measurements. IVIM-based perfusion MRI, which does not require injection of any tracer or contrast agent, has been first investigated in the brain, but is now experiencing a remarkable revival for applications throughout the body, especially for oncologic applications, from diagnosis to treatment monitoring. This book addresses a number of highly topical aspects of the field from leading authorities, introducing the concepts behind IVIM MRI, outlining related methodological issues, and summarizing its current usage and potential for clinical applications. It also presents future research directions, both in terms of methodological development and clinical application fields, extending to new, non-perfusion applications of IVIM MRI, such as virtual MR elastography.
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.
Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.
This book introduces the classical and modern image reconstruction technologies. It covers topics in two-dimensional (2D) parallel-beam and fan-beam imaging, three-dimensional (3D) parallel ray, parallel plane, and cone-beam imaging. Both analytical and iterative methods are presented. The applications in X-ray CT, SPECT (single photon emission computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging) are discussed. Contemporary research results in exact region-of-interest (ROI) reconstruction with truncated projections, Katsevich’s cone-beam filtered backprojection algorithm, and reconstruction with highly under-sampled data are included. The last chapter of the book is devoted to the techniques of using a fast analytical algorithm to reconstruct an image that is equivalent to an iterative reconstruction. These techniques are the author’s most recent research results. This book is intended for students, engineers, and researchers who are interested in medical image reconstruction. Written in a non-mathematical way, this book provides an easy access to modern mathematical methods in medical imaging. Table of Content: Chapter 1 Basic Principles of Tomography 1.1 Tomography 1.2 Projection 1.3 Image Reconstruction 1.4 Backprojection 1.5 Mathematical Expressions Problems References Chapter 2 Parallel-Beam Image Reconstruction 2.1 Fourier Transform 2.2 Central Slice Theorem 2.3 Reconstruction Algorithms 2.4 A Computer Simulation 2.5 ROI Reconstruction with Truncated Projections 2.6 Mathematical Expressions (The Fourier Transform and Convolution , The Hilbert Transform and the Finite Hilbert Transform , Proof of the Central Slice Theorem, Derivation of the Filtered Backprojection Algorithm , Expression of the Convolution Backprojection Algorithm, Expression of the Radon Inversion Formula ,Derivation of the Backprojection-then-Filtering Algorithm Problems References Chapter 3 Fan-Beam Image Reconstruction 3.1 Fan-Beam Geometry and Point Spread Function 3.2 Parallel-Beam to Fan-Beam Algorithm Conversion 3.3 Short Scan 3.4 Mathematical Expressions (Derivation of a Filtered Backprojection Fan-Beam Algorithm, A Fan-Beam Algorithm Using the Derivative and the Hilbert Transform) Problems References Chapter 4 Transmission and Emission Tomography 4.1 X-Ray Computed Tomography 4.2 Positron Emission Tomography and Single Photon Emission Computed Tomography 4.3 Attenuation Correction for Emission Tomography 4.4 Mathematical Expressions Problems References Chapter 5 3D Image Reconstruction 5.1 Parallel Line-Integral Data 5.2 Parallel Plane-Integral Data 5.3 Cone-Beam Data (Feldkamp's Algorithm, Grangeat's Algorithm, Katsevich's Algorithm) 5.4 Mathematical Expressions (Backprojection-then-Filtering for Parallel Line-Integral Data, Filtered Backprojection Algorithm for Parallel Line-Integral Data, 3D Radon Inversion Formula, 3D Backprojection-then-Filtering Algorithm for Radon Data, Feldkamp's Algorithm, Tuy's Relationship, Grangeat's Relationship, Katsevich’s Algorithm) Problems References Chapter 6 Iterative Reconstruction 6.1 Solving a System of Linear Equations 6.2 Algebraic Reconstruction Technique 6.3 Gradient Descent Algorithms 6.4 Maximum-Likelihood Expectation-Maximization Algorithms 6.5 Ordered-Subset Expectation-Maximization Algorithm 6.6 Noise Handling (Analytical Methods, Iterative Methods, Iterative Methods) 6.7 Noise Modeling as a Likelihood Function 6.8 Including Prior Knowledge 6.9 Mathematical Expressions (ART, Conjugate Gradient Algorithm, ML-EM, OS-EM, Green’s One-Step Late Algorithm, Matched and Unmatched Projector/Backprojector Pairs ) 6.10 Reconstruction Using Highly Undersampled Data with l0 Minimization Problems References Chapter 7 MRI Reconstruction 7.1 The 'M' 7.2 The 'R' 7.3 The 'I'; (To Obtain z-Information, x-Information, y-Information) 7.4 Mathematical Expressions Problems References Indexing
Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.