Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.

In susceptibility-weighted MRI, ignoring the magnetic field inhomogeneity can lead to severe reconstruction artifacts. Correcting for the effects of magnetic field inhomogeneity requires accurate fieldmaps. Especially in functional MRI, dynamic updates are desirable, since the fieldmap may change in time. Also, susceptibility effects that induce field inhomogeneity often have non-zero through-plane gradients, which, if uncorrected, can cause signal loss in the reconstructed images. Most image reconstruction methods that compensate for field inhomogeneity, even using dynamic fieldmap updates, ignore through-plane fieldmap gradients. Furthermore, standard optimization methods, like CG-based algorithms, may be slow to converge and recently proposed algorithms based on the Augmented Lagrangian (AL) framework have shown the potential to lead to more efficient optimization algorithms, especially in MRI reconstruction problems with non-quadratic regularization. In this work, we propose a computationally efficient, model-based iterative method for joint reconstruction of dynamic images and fieldmaps in single coil and parallel MRI, using single-shot trajectories. We first exploit the fieldmap smoothness to perform joint estimation using less than two full data sets and then we exploit the sensitivity encoding from parallel imaging to reduce the acquisition length and perform joint reconstruction using just one full k-space dataset. Subsequently, we extend the proposed method to account for the through-plane gradients of the field inhomogeneity. To improve the efficiency of the reconstruction algorithm we use a linearization technique for fieldmap estimation, which allows the use of the conjugate gradient algorithm. The resulting method allows for efficient reconstruction by applying fast approximations that allow the use of the conjugate gradient algorithm along with FFTs. Our proposed method can be computationally efficient for quadratic regularizers, but the CG-based algorithm is not directly applicable to non-quadratic regularization. To improve the efficiency of our method for non-quadratic regularization we propose an algorithm based on the augmented Lagrangian (AL) framework with variable splitting. This new algorithm can also be used for the non-linear optimization problem of fieldmap estimation without the need for the linearization approximation.PhDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102449/1/amatakos_1.pd

Joint B0 and image estimation integrated with model based reconstruction for field map update and distortion correction in prostate diffusion MRI

In prostate Diffusion Weighted MRI, differences in susceptibility values exist at the interface between the prostate and rectal-air. This can result in off-resonance magnetic field leading to geometric distortions including signal stretching and signal pile-up in the reconstructed images. Using a set of EPI data acquired with blip-up and blip-down phase encoding gradient directions, model based reconstruction has recently been proposed that can correct these distortions by using a B0 field estimated from a separate B0 scan. However, change in the size of the rectal air region across time can occur that can result in a mismatch of the B0 field to the EPI scan. Also, the measured B0 field itself can be erroneous in regions of low Signal to Noise ratio around the prostate rectal air interface. In this work, using a set of single shot EPI data acquired with blip-up and blip-down phase encoding gradient directions, a novel joint model based reconstruction is proposed that can account for changes in the off resonance effects between the B0 and EPI scans. For ten prostate patients, using a measured B0 field as an initial B0 estimate, on a 5-point scale (1-5) image quality scores evaluated by an experienced radiologist, the proposed framework achieved scores of 3.50+/-0.85 and 3.40+/-0.51 for bvalues of 0 and 500 s/mm2, respectively compared to 3.40+/-0.70 and 3.30+/-0.67 for model based reconstruction. The proposed framework is also capable of estimating a distortion corrected EPI image even without an initial B0 field estimate in situations where a separate B0 scan cannot be obtained due to time constraint

Towards efficient neurosurgery: Image analysis for interventional MRI

Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions

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

Constrained and Spectral-Spatial RF Pulse Design for Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) provides a non-invasive glimpse inside the human body, generates excellent soft tissue contrast, uses non-ionizing radiation, and has become a critical tool in diagnosis of disease in medicine. Radio Frequency (RF) pulses are an integral component of MRI pulse sequences and can be tailored to particular applications. This dissertation explores the MRI physics, convex optimization problems, and experimental methodologies required for the design of tailored RF pulses First, we introduce constrained RF pulse design, a process that incorporates meaningful, physical constraints, such as peak RF amplitude and integrated RF power, and enables efficient RF pulse design. With this process we explore simultaneous multislice (SMS) imaging, a method used to accelerate MRI and combat notoriously long acquisition times. Compared to an SMS pulse designed without constraints, our constrained pulses achieved lower magnitude normalized root mean squared error (NRMSE) for an equivalent RF pulse length, or alternatively, the same NRMSE for a shorter pulse length. Constrained RF pulse design forms a basis for the rest of the dissertation. Second, we show that prewinding pulses, a special class of RF pulses, help reduce signal loss due to intravoxel dephasing generated by magnetic field inhomogeneities. We propose a spectral-spatial prewinding pulse that leverages a larger effective recovery bandwidth than equivalent, purely spectral pulses. In an in vivo experiment imaging the brain of a human volunteer, we designed spectral-spatial pulses with a complex NRMSE of 0.18, which is significantly improved from the complex NRMSE of 0.54 in the purely spectral pulse for the same experiment. Finally, we consider a slab-selective prewinding pulse, that extends spectral and spectral-spatial prewinding pulses to a common 3D imaging method. Here we integrate optimal control optimization to further improve the slab-selective spectral pulse design and see an in vivo improvement of excitation NRMSE from 0.40 to 0.37. In the context of a steady-state sequence small-tip fast recovery (STFR), we also show a major reduction in mean residual transverse magnetization magnitude after the STFR “tip-up” recovery pulse from 0.18 to 0.02 when adding optimal control. This method has the potential to connect prewinding pulse design from the MRI physicist engineering workspace to a clinical application. In summary, we show that constrained RF pulse design provides an efficient way of improving MRI in terms of acquisition speed (via multislice imaging) and image quality (via signal recovery).PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147647/1/sydneynw_1.pd

Numerical Simulation

Nowadays mathematical modeling and numerical simulations play an important role in life and natural science. Numerous researchers are working in developing different methods and techniques to help understand the behavior of very complex systems, from the brain activity with real importance in medicine to the turbulent flows with important applications in physics and engineering. This book presents an overview of some models, methods, and numerical computations that are useful for the applied research scientists and mathematicians, fluid tech engineers, and postgraduate students

Acquisition strategies for fat/water separated MRI

This thesis focuses on new ways to more efficiently acquire the signal for fat/water separated MRI, also known as Dixon methods. In paper I, the concept of dual bandwidths was introduced to improve SNR efficiency by removing dead times in a spin echo PROPELLER sequence. By correcting for the displacement of fat, we were able to improve the motion correction. This required additional considerations during reconstruction in order to avoid noise amplification, which was solved with a noise-whitening Tikhonov regularization. Paper II explores the combination of fat/water separation in k-space with partially acquired data, i.e. partial Fourier sampling. With reduced sampling coverage comes the ability of increased spatial resolution, which is often limited in fat/water imaging, particularly in gradient echo sequences. A modified POCS routine was also developed with real-valued estimates, exploiting Hermitian symmetry to improve the inverse problem conditioning in the fully sampled region. A single-TR dual-bandwidth RARE (fast/turbo spin echo) sequence without dead times was developed in Paper III, which uses partial Fourier sampling with late and early echoes to improve the chemical shift encoding. The proposed sequence can acquire images with 0.5 mm in-plane resolution without dead times, with image quality exceeding current state-of-the-art techniques. An automated selection of gradient waveforms based on Cramér-Rao bounds was implemented on the scanner. In Paper IV, the dual-bandwidth concept was generalized to continuous bandwidths. Instead of the conventional shift of a trapezoidal readout gradient, we describe a new method of encoding chemical shift by using asymmetric readout waveforms. Asymmetric readouts were implemented in a RARE sequence to completely remove dead times from multi-TR acquisitions, with typical scan time reductions of 25 %. The developed methods enable fat/water imaging with reduced scan times and increased spatial resolution, which has previously limited their use

Time-efficient and flexible design of optimized multishell HARDI diffusion

Purpose: Advanced diffusion magnetic resonance imaging benefits from collecting as much data as is feasible but is highly sensitive to subject motion and the risk of data loss increases with longer acquisition times. Our purpose was to create a maximally time-efficient and flexible diffusion acquisition capability with built-in robustness to partially acquired or interrupted scans. Our framework has been developed for the developing Human Connectome Project, but different application domains are equally possible. Methods: Complete flexibility in the sampling of diffusion space combined with free choice of phase-encode-direction and the temporal ordering of the sampling scheme was developed taking into account motion robustness, internal consistency, and hardware limits. A split-diffusion-gradient preparation, multiband acceleration, and a restart capacity were added. Results: The framework was used to explore different parameters choices for the desired high angular resolution diffusion imaging diffusion sampling. For the developing Human Connectome Project, a high-angular resolution, maximally time-efficient (20 min) multishell protocol with 300 diffusion-weighted volumes was acquired in >400 neonates. An optimal design of a high-resolution (1.2 × 1.2 mm2) two-shell acquisition with 54 diffusion weighted volumes was obtained using a split-gradient design. Conclusion: The presented framework provides flexibility to generate time-efficient and motion-robust diffusion magnetic resonance imaging acquisitions taking into account hardware constraints that might otherwise result in sub-optimal choices. Magn Reson Med, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes

HYPERPOLARIZED CARBON-13 MAGNETIC RESONANCE MEASUREMENTS OF TISSUE PERFUSION AND METABOLISM

Hyperpolarized Magnetic Resonance Imaging (HP MRI) is an emerging modality that enables non-invasive interrogation of cells and tissues with unprecedented biochemical detail. This technology provides rapid imaging measurements of the activity of a small quantity of molecules with a strongly polarized nuclear magnetic moment. This polarization is created in a polarizer separate from the imaging magnet, and decays continuously towards a non-detectable thermal equilibrium once the imaging agent is removed from the polarizer and administered by intravenous injection. Specialized imaging strategies are therefore needed to extract as much information as possible from the HP signal during its limited lifetime. In this work, we present innovative strategies for measurement of tissue perfusion and metabolism with HP MRI. These techniques include the capacity to sensitize the imaging signal to the diffusive motion of HP molecules, providing improved accuracy and reproducibility for assessment of agent uptake in tissue. The proposed methods were evaluated in numerical simulations, implemented on a preclinical MRI system and demonstrated in vivo in rodents through imaging of HP 13C urea. Using the simulation and imaging infrastructure developed in this work, established methods for encoding HP chemical signals were compared quantitatively. Lastly, our method was adapted for imaging of [2-13C]dihydroxyacetone, a novel HP agent that probes enzymatic flux through multiple biochemical pathways in vivo. Our results demonstrate the capacity of HP MRI to measure tissue perfusion and metabolism in ways not possible with the imaging modalities currently available in the clinic. As the use of HP MRI advances in clinical investigations of human disease, these imaging measurements can offer real-time and individualized information on disease states for early detection and therapeutic guidance

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing