In vivo Magnetic Resonance Microscopy and Hypothermic Anaesthesia of a Disease Model in Medaka

In medical and pharmacological research, various human disease models in small fish, such as medaka (Oryzias latipes), have been created. To investigate these disease models noninvasively, magnetic resonance imaging (MRI) is suitable because these small fish are no longer transparent as adults. However, their small body size requires a high spatial resolution, and a water pool should be avoided to maximize the strength of MRI. We developed in vivo magnetic resonance microscopy (MR microscopy) without a water pool by combining hypothermic anaesthesia and a 14.1 T MR microscope. Using in vivo MR microscopy, we noninvasively evaluated the hepatic steatosis level of a non-alcoholic fatty liver disease model in medaka and followed the individual disease progression. The steatosis level was quantified by the MRI-estimated proton density fat-fraction (MRI-PDFF), which estimates the triglyceride fat concentration in liver tissue and is recognized as an imaging biomarker. The MRI-PDFF results agreed with a histological analysis. Moreover, we optimized the hypothermic anaesthesia procedure to obtain a recovery proportion of 1 in the experiment involving MR microscopy. Recovered medaka could not be distinguished from naïve medaka after the experiment. Therefore, the in vivo MR microscopy will expand the possibilities of a human disease model in fish

Acquisition and Reconstruction Techniques for Fat Quantification Using Magnetic Resonance Imaging

Quantifying the tissue fat concentration is important for several diseases in various organs including liver, heart, skeletal muscle and kidney. Uniquely, MRI can separate the signal from water and fat in-vivo, rendering it the most suitable imaging modality for non-invasive fat quantification. Chemical-shift-encoded MRI is commonly used for quantitative fat measurement due to its unique ability to generate a separate image for water and fat. The tissue fat concentration can be consequently estimated from the two images. However, several confounding factors can hinder the water/fat separation process, leading to incorrect estimation of fat concentration. The inhomogeneities of the main magnetic field represent the main obstacle to water/fat separation. Most existing techniques rely mainly on imposing spatial smoothness constraints to address this problem; however, these often fail to resolve large and abrupt variations in the magnetic field. A novel convex relaxation approach to water/fat separation is proposed. The technique is compared to existing methods, demonstrating its robustness to resolve abrupt magnetic field inhomogeneities. Water/fat separation requires the acquisition of multiple images with different echo-times, which prolongs the acquisition time. Bipolar acquisitions can efficiently acquire the required data in shorter time. However, they induce phase errors that significantly distort the fat measurements. A new bipolar acquisition strategy that overcomes the phase errors and provides accurate fat measurements is proposed. The technique is compared to the current clinical sequence, demonstrating its efficiency in phantoms and in-vivo experiments. The proposed acquisition technique is also applied on animal models to achieve higher spatial resolution than the current sequence. In conclusion, this dissertation describes a complete framework for accurate and precise MRI fat quantification. Novel acquisitions and reconstruction techniques that address the current challenges for fat quantification are proposed

Accelerated Computation of Regularized Estimates in Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is a non-invasive medical imaging modality that uses magnetic fields. Accurate estimates of these fields are often used to improve the quality of MR imaging techniques. Regularized estimators for such fields are robust and can provide high quality estimates but often at a significant computational cost. In this work, we investigate several of these estimators with a focus on developing novel minimization methods that reduce their computation times. First, we explore regularized receive coil sensitivity estimation by demonstrating the improved performance of regularized methods over existing, heuristic approaches and by presenting several algorithms, based on augmented Lagrangian methods, that minimize the quadratic cost function in half the time required by a preconditioned conjugate gradient (CG) method. Second, we present a general cost function that combines the regularized estimation of the main magnetic field inhomogeneity for both multiple echo time field map estimation and chemical shift based water-fat imaging. We present two methods, both based on optimization transfer principles, that reduce the computation time of this estimator by a factor of 30 compared to the existing separable quadratic surrogates method. We also evaluate the effectiveness of edge preserving regularization for field inhomogeneity estimation near tissue interfaces. Third, we present a novel alternating minimization method that uses augmented Lagrangian methods to accelerate the computation of the compressed sensing based water-fat image reconstruction problem by at least ten times compared to the existing nonlinear CG method. The algorithms presented in this thesis may also be applicable to other MRI topics including B1+ estimation, T1 estimation from variable flip angles, and R2* corrected or parallel imaging extensions of compressed sensing based water-fat imaging.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107096/1/mjalliso_1.pd

Efficient Model-Based Reconstruction for Dynamic MRI

Dynamic magnetic resonance imaging (MRI) has important clinical and neuro- science applications (e.g., cardiac disease diagnosis, neurological behavior studies). It captures an object in motion by acquiring data across time, then reconstructing a sequence of images from them. This dissertation considers efficient dynamic MRI reconstruction using handcrafted models, to achieve fast imaging with high spatial and temporal resolution. Our modeling framework considers data acquisition process, image properties, and artifact correction. The reconstruction model expressed as a large-scale inverse problem requires optimization algorithms to solve, and we consider efficient implementations that make use of underlying problem structures. In the context of dynamic MRI reconstruction, we investigate efficient updates in two frameworks of algorithms for solving a nonsmooth composite convex optimization problem for the low-rank plus sparse (L+S) model. In the proximal gradient framework, current algorithms for the L+S model involve the classical iterative soft thresholding algorithm (ISTA); we consider two accelerated alternatives, one based on the fast iterative shrinkage-thresholding algorithm (FISTA), and the other with the recent proximal optimized gradient method (POGM). In the augmented Lagrangian (AL) framework, we propose an efficient variable splitting scheme based on the form of the data acquisition operator, leading to simpler computation than the conjugate gradient (CG) approach required by existing AL methods. Numerical results suggest faster convergence of our efficient implementations in both frameworks, with POGM providing the fastest convergence overall and the practical benefit of being free of algorithm tuning parameters. In the context of magnetic field inhomogeneity correction, we present an efficient algorithm for a regularized field inhomogeneity estimation problem. Most existing minimization techniques are computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. We consider 3D MRI with optional consideration of coil sensitivity and a generalized expression that addresses both multi-echo field map estimation and water-fat imaging. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state- of-the-art methods with similar memory requirements. In the context of task-based functional MRI (fMRI) reconstruction, we introduce a space-time model that represents an fMRI timeseries as a sum of task-correlated signal and non-task background. Our model consists of a spatiotemporal decomposition based on assumptions of the activation waveform shape, with spatial and temporal smoothness regularization on the magnitude and phase of the timeseries. Compared with two contemporary task fMRI decomposition models, our proposed model yields better timeseries and activation maps on simulated and human subject fMRI datasets with multiple tasks. The above examples are part of a larger framework for model-based dynamic MRI reconstruction. This dissertation concludes by presenting a general framework with flexibility on model assumptions and artifact compensation options (e.g., field inhomogeneity, head motion), and proposing future work ideas on both the framework and its connection to data acquisition.PHDApplied and Interdisciplinary MathematicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168081/1/yilinlin_1.pd