Topics in Steady-state MRI Sequences and RF Pulse Optimization.

Small-tip fast recovery (STFR) is a recently proposed rapid steady-state magnetic resonance imaging (MRI) sequence that has the potential to be an alternative to the popular balanced steady-state free precession (bSSFP) imaging sequence, since they have similar signal level and tissue contrast, but STFR has reduced banding artifacts. In this dissertation, an analytic equation of the steady-state signal for the unspoiled version of STFR is first derived. It is shown that unspoiled-STFR is less sensitive to the inaccuracy in excitation than the previous proposed spoiled-STFR. By combining unspoiled-STFR with jointly designed tip-down and tip-up pulses, a 3D STFR acquisition over 3-4 cm thick 3D ROI with single coil and short RF pulses (1.7 ms) is demonstrated. Then, it is demonstrated that STFR can reliably detect functional MRI signal and the contrast is driven mainly from intra-voxel dephasing, not diffusion, using Monte Carlo simulation, human experiments and test-retest reliability. Following that another version of STFR using a spectral pre-winding pulse instead of the spatially tailored pulse is investigated, leading to less T2* weighting, easier implementation. Multidimensional selective RF pulse is a key part for STFR and many other MRI applications. Two novel RF pulse optimization methods are proposed. First, a minimax formulation that directly controls the maximum excitation error, and an effective optimization algorithm using variable splitting and alternating direction method of multipliers (ADMM). The proposed method reduced the maximum excitation by more than half in all the testing cases. Second, a method that jointly optimizes the excitation k-space trajectory and RF pulse is proposed. The k-space trajectory is parametrized using 2nd-order B-splines, and an interior point algorithm is used to explicitly solve the constrained optimization. An effective initialization method is also suggested. The joint design reduced the NRMSE by more than 30 percent compared to existing methods in inner volume excitation and pre-phasing problem. Using the proposed joint design, rapid inner volume STFR imaging with a 4 ms excitation pulse with single transmit coil is demonstrated. Finally, a regularized Bloch-Siegert B1 map reconstruction method is presented that significantly reduces the noise in estimated B1 maps.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111514/1/sunhao_1.pd

Improvements in magnetic resonance imaging excitation pulse design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 241-253).This thesis focuses on the design of magnetic resonance imaging (MRI) radio-frequency (RF) excitation pulses, and its primary contributions are made through connections with the novel multiple-system single-output (MSSO) simultaneous sparse approximation problem. The contributions are both conceptual and algorithmic and are validated with simulations, as well as anthropogenic-object-based and in vivo trials on MRI scanners. Excitation pulses are essential to MRI: they excite nuclear spins within a subject that are detected by a resonant coil and then reconstructed into images. Pulses need to be as short as possible due to spin relaxation, tissue heating, and main field inhomogeneity limitations. When magnetic spins are tilted by only a small amount, pulse transmission may be interpreted as depositing energy in a continuous three-dimensional Fourier-like domain along a one-dimensional contour to form an excitation in the spatial domain. Pulse duration is proportional to the length of the contour and inversely proportional to the rate at which it is traversed, and the rate is limited by system gradient hardware restrictions. Joint design of the contour and a corresponding excitation pulse is a difficult and central problem, while determining near-optimal energy deposition once the contour is fixed is significantly easier. We first pose the NP-Hard MSSO problem and formulate greedy and convex relaxation-based algorithms with which to approximately solve it. We find that second-order-cone programming and iteratively-reweighted least squares approaches are practical techniques for solving the relaxed problem and prove that single-vector sparse approximation of a complex-valued vector is an MSSO problem.(cont.) We then focus on pulse design, first comparing three algorithms for solving linear systems of multi-channel excitation design equations, presenting experimental results from a 3 Tesla scanner with eight excitation channels. Our aim then turns toward the joint design of pulses and trajectories. We take joint design in a novel direction by utilizing MSSO theory and algorithms to design short-duration sparsity-enforced pulses. These pulses are used to mitigate transmit field inhomogeneity in the human brain at 7 Tesla, a significant step towards the clinical use of high-field imaging in the study of cancer, Alzheimer's disease, and Multiple Sclerosis. Pulses generated by the sparsity-enforced method outperform those created via conventional Fourier-based techniques, e.g., when attempting to produce a uniform magnetization in the presence of severe RF inhomogeneity, a 5.7-ms 15-spoke pulse generated by the sparsity-enforced method produces an excitation with 1.28 times lower root-mean-square error than conventionally-designed 15-spoke pulses. To achieve this same level of uniformity, conventional methods must use 29-spoke pulses that are 1.4 times longer. We then confront a subset selection problem that arises when a parallel excitation system has more transmit modes available than hardware transmit channels with which to drive them. MSSO theory and algorithms are again applicable and determine surprising targetspecific mixtures of light and dark modes that yield high-quality excitations. Finally, we study the critical patient safety issue of specific absorption rate (SAR) of multi-channel excitation pulses at high field. We develop a fast SAR calculation algorithm and propose optimizing an individual pulse and time-multiplexing a set of pulses as ways to reduce SAR; the latter is capable of reducing maximum local SAR by 11% with no impact on pulse duration.by Adam Charles Zelinski.Ph.D

Acceleration Methods for MRI

Acceleration methods are a critical area of research for MRI. Two of the most important acceleration techniques involve parallel imaging and compressed sensing. These advanced signal processing techniques have the potential to drastically reduce scan times and provide radiologists with new information for diagnosing disease. However, many of these new techniques require solving difficult optimization problems, which motivates the development of more advanced algorithms to solve them. In addition, acceleration methods have not reached maturity in some applications, which motivates the development of new models tailored to these applications. This dissertation makes advances in three different areas of accelerations. The first is the development of a new algorithm (called B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithm or BARISTA), that solves a parallel MRI optimization problem with compressed sensing assumptions. BARISTA is shown to be 2-3 times faster and more robust to parameter selection than current state-of-the-art variable splitting methods. The second contribution is the extension of BARISTA ideas to non-Cartesian trajectories that also leads to a 2-3 times acceleration over previous methods. The third contribution is the development of a new model for functional MRI that enables a 3-4 factor of acceleration of effective temporal resolution in functional MRI scans. Several variations of the new model are proposed, with an ROC curve analysis showing that a combination low-rank/sparsity model giving the best performance in identifying the resting-state motor network.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120841/1/mmuckley_1.pd

Minimizing the Adverse Effects of Electric Fields in Magnetic Resonance Imaging using Optimized Gradient Encoding and Peripheral Nerve Models

Magnetic Resonance Imaging (MRI) is an important imaging modality in both the clinic and in research. MRI technology has been trending toward increasing field strengths to improve the signal-to-noise ratio of the MR signal and fast excitation/encoding strategies to more flexible target anatomical regions during excitation to reduce the total imaging time. While largely successful, both strategies rely on the application of increasingly strong and rapidly switched magnetic fields: the radio frequency (RF) field for excitation and the gradient field for encoding. The technology for generating these fields (and rapidly switching them) has advanced to the point that we are limited by biological responses to the switching fields. For the gradient field, the electric field generated in the tissue causes peripheral nerve stimulation (PNS) causing mild but bothersome sensations at low levels, up to pain or cardiac malfunction at higher levels. The electric fields created by the much faster time-varying RF cause heat deposition, ultimately denaturing proteins and causing tissue damage. In this thesis, methods are presented to characterize and minimize these two problems associated with the switched magnetic fields in MRI. The deposited RF energy (Specific Absorption Rate, SAR) incurred during shaped excitations can be significantly reduced by optimizing gradient and RF waveforms for inner-volume excitations that allow imaging of a sub-volume of the body without wrapping artifacts. The adverse effects of the switching gradient fields are addressed by designing time-optimal gradient encoding waveforms and by developing a method to predict and characterize PNS using field simulations and a full-body nerve model allowing these critical effects to be addressed at the gradient coil design stage. In the first part, time-optimal gradient trajectories are demonstrated that use the gradient hardware at the maximum available performance. The skeleton of the trajectory is defined by a set of k-space control points. The method optimizes gradient waveforms that traverse the k-space control points in the minimum possible amount of time. By using an analytic representation of the gradients (piece-wise linear), the design process is fast and numerically robust. The resulting trajectories sample k-space efficiently while using the gradient system at maximum performance. Compared to the leading Optimal Control method, the proposed method generates gradient waveforms that are 9.2% shorter. The computation process is ∼100x faster and does not suffer from numerical instabilities such as oscillations. In the second part, a method is developed that jointly optimizes parallel transmission RF and gradient waveforms for fast and robust 3-D inner-volume excitation of the MRI signal in minimal time and with minimal energy deposition. The optimization of the k-space trajectories is based on a small number of shape parameters that are well-suited for joint optimization with the RF waveforms. Within each iteration of the trajectory optimization, a small tip-angle least-squares RF pulse design problem is solved. Using optimized 3-D cross (shells) trajectories, a cube shape (brain shape) region was excited with 3.4% (6.2%) NRMSE in less than 5 ms using a 7 T scanner with 8 Tx channels and a clinical gradient system (Gmax = 40 mT/m, Smax = 150 T/m/s). Incorporation of off-resonance robustness in the pulse design significantly altered the k-space trajectory solutions and improved the practical performance of the pulses. In the final part, a framework is presented that simulates PNS thresholds for realistic gradient coil geometries and thus allows, for the first time, to directly address PNS in the coil design process. The PNS framework consists of an accurate body model for simulation of the induced electric fields, an atlas of peripheral nerves, and a neurodynamic model to predict the nerve responses to imposed electric fields. With this model, measured PNS thresholds of two leg/arm solenoid coils and three commercial actively-shielded MR gradient coils could be reproduced with good accuracy. The proposed method can be used to assess the PNS capability of gradient coils during the design phase, without building expensive prototype coils

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen