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This book presents the first in-depth introduction to parallel imaging techniques and, in particular, to the application of parallel imaging in clinical MRI. It will provide readers with a broader understanding of the fundamental principles of parallel imaging and of the advantages and disadvantages of specific MR protocols in clinical applications in all parts of the body at 1.5 and 3 Tesla.
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.
Chemical shift imaging is a magnetic resonance imaging technique that separates the signal from two or more chemical species. The cost of chemical shift encoding is increased acquisition time as multiple acquisitions are required at different echo times. Image accelera tion techniques, typically parallel imaging, are often used to improve coverage and resolution. This thesis describes a new technique for estimating the signal to noise ratio for parallel imaging reconstruction s and proposes new image reconstructions for a ccelerated chemical shift imaging using compressed sensing and/or parallel imaging for two applications: water- at separation and metabolic imaging of hyperpolarized [1-13C] pyruvate. Spatially varying noise in parallel imaging reconstructions makes measurements of the signal to noise ratio, a commonly used metric for image for image quality, difficult. Existing approaches have limitations: they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A signal to noise ratio estimation technique is proposed that does not exhibit these limitations. Water-fat imaging of highly undersampled datasets from the liver, calf, knee, and abdominal cavity are demonstrated using a customized IDEAL-SPGR pulse sequence and an integrated compressed sensing, parallel imaging, water-fat reconstruction. This method offer s image quality comparable to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this method offers improved image quality when compared to the current standard of parallel imaging. Accelerated metabolic imaging of hyperpolarized [1-13C] pyruvate and its metabolic by-products lactate, alanine, and bicarbonate is demonstrated using an integrated compressed sensing, metabolite separation reconstruction. Phantoms are used to validate this technique while retrospectively and prospectively accelerated 3D in vivo datasets are used to demonstrate feasibility. An alternative approach to accelerated metabolic imaging is demonstrated using high performance magnetic field gradient set. This thesis addresses the inherently slow acquisition times of chemical shift imaging by examining the role compressed sensing and parallel imaging can play in chemical shift imaging. An approach to SNR assessment for parallel imaging reconstruction is proposed and approaches to accelerated chemical shift imaging are described for applications in water-fat imaging and metabolic imaging of hyperpolarized [1-13C] pyruvate.
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.
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
A fourth gradient channel was implemented to provide slice dependent RF coil phase compensation for arrays in dual-sided or "sandwich" configurations. The use of highly parallel arrays for single echo acquisition magnetic resonance imaging allows both highly accelerated imaging and capture of dynamic and single shot events otherwise inaccessible to MRI. When using RF coils with dimensions on the order of the voxel size, the array coil element phase patterns adversely affect image acquisition, requiring correction. This has previously been accomplished using a pulse of the gradient coil, imparting a linear phase gradient across the sample opposite of that due to the RF coil elements. However, the phase gradient due to the coil elements reverses on opposite sides of the coils, preventing gradient-based phase compensation with sandwich arrays. To utilize such arrays, which extend the imaging field of view of this technique, a fourth gradient channel and coil were implemented to simultaneously provide phase compensation of opposite magnitude to the lower and upper regions of a sample, imparting opposite phase gradients to compensate for the opposite RF coil phase patterns of the arrays. The fourth gradient coil was designed using a target field approach and constructed using printed circuit boards. This coil was integrated with an RF excitation coil, dual-sided receive array, and sample loading platform to form a single imaging probe capable of both ultra-fast and high resolution magnetic resonance imaging. By employing the gradient coil, this probe was shown to simultaneously provide improved phase compensation throughout a sample, enabling simultaneous SEA imaging using arrays placed below and above a sample. The fourth gradient coil also improves the acquisition efficiency of highly accelerated imaging using both arrays for receive. The same imaging probe was shown to facilitate accelerated MR microscopy over the field of view of the entire array with no changes to the hardware configuration. The spatio-temporal imaging capabilities of this system were explored with magnetic resonance elastography. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148045