Medical Robotics for use in MRI Guided Endoscopy

Interventional Magnetic Resonance Imaging (MRI) is a developing field that aims to provide intra-operative MRI to a clinician to guide diagnostic or therapeutic medical procedures. MRI provides excellent soft tissue contrast at sub-millimetre resolution in both 2D and 3D without the need for ionizing radiation. Images can be acquired in near real-time for guidance purposes. Operating in the MR environment brings challenges due to the high static magnetic field, switching magnetic field gradients and RF excitation pulses. In addition high field closed bore scanners have spatial constraints that severely limit access to the patient. This thesis presents a system for MRI-guided Endoscopic Retrograde Cholangio-pancreatography (ERCP). This includes a remote actuation system that enables an MRI-compatible endoscope to be controlled whilst the patient is inside the MRI scanner, overcoming the spatial and procedural constraints imposed by the closed scanner bore. The modular system utilises non-magnetic ultrasonic motors and is designed for image-guided user-in-the-loop control. A novel miniature MRI compatible clutch has been incorporated into the design to reduce the need for multiple parallel motors. The actuation system is MRI compatible does not degrade the MR images below acceptable levels. User testing showed that the actuation system requires some degree of training but enables completion of a simulated ERCP procedure with no loss of performance. This was demonstrated using a tailored ERCP simulator and kinematic assessment tool, which was validated with users from a range of skill levels to ensure that it provides an objective measurement of endoscopic skill. Methods of tracking the endoscope in real-time using the MRI scanner are explored and presented here. Use of the MRI-guided ERCP system was shown to improve the operator’s ability to position the endoscope in an experimental environment compared with a standard fluoroscopic-guided system.Open Acces

Experimental Techniques and Image Reconstruction for Magnetic Resonance Imaging with Inhomogeneous Fields

University of Minnesota Ph.D. dissertation. August 2019. Major: Physics. Advisors: Michael Garwood, Geoffrey Ghose. 1 computer file (PDF); x, 107 pages.Magnetic resonance imaging is quite sensitive to experimental imperfections, necessitating extremely expensive electrical infrastructure and design requirements to permit high-quality experiments to be performed. By relaxing the sensitivity to imperfection, the entire system can be made less expensive and more accessible by shrinking the magnet generating the polarizing field. Decreasing the magnet size relative to the bore increases the polarizing field inhomogeneity. Moreover, current progress in MRI at ultra-high field (greater than or equal to 7T) is pushing the limits of conventional MRI methods, as field inhomogeneity increases with field strength. Hence, while many of the methods herein were developed with a small magnet in mind, they also apply at ultra-high field. The appeal of ultra-high field is increased detection sensitivity such that ever-smaller structures may be imaged in animals and humans. The primary goal of this work is to extend the current ability of magnetic resonance imaging to tolerate a large degree of spatial variation in both the transmit and polarizing fields involved. A novel method of decreasing radiofrequency pulse duration for multidimensional pulses is presented, rendering them more robust to field inhomogeneity. Furthermore, this method is leveraged to accelerate data acquisition. A new imaging sequence for quantitative determination of transverse relaxation rates is presented, which tolerates large variations in both the transmit and polarizing magnetic fields, as is often found when imaging with iron-oxide nanoparticles and/or at ultrahigh field. Finally, a computationally efficient approach for spatiotemporally-encoded image reconstruction is presented, which is inherently robust to field inhomogeneity

Monte Carlo Simulation of Diffusion Magnetic Resonance Imaging

The goal of this thesis is to describe, implement and analyse Monte Carlo (MC) algorithms for simulating the mechanism of diffusion magnetic resonance imaging (dMRI). As the inverse problem of mapping the sub-voxel micro-structure remains challenging, MC methods provide an important numerical approach for creating ground-truth data. The main idea of such simulations is first generating a large sample of independent random trajectories in a prescribed geometry and then synthesizing the imaging signals according to given imaging sequences. The thesis starts by providing a concise introduction of the mathematical background for understanding dMRI. It then proceeds to describe the workflow and implementation of the most basic Monte Carlo method with experiments performed on simple geometries. A theoretical framework for error analysis is introduced, which to the best of the author's knowledge, has been absent in the literature. In an effort to mitigate the costly nature of MC algorithms, the geometrically adaptive fast random walk algorithm (GAFRW) is implemented, first invented by D.Grebenkov. Additional mathematical justification is provided in the appendix should the reader find details in the original paper by Grebenkov lacking. The result suggests that the GAFRW algorithm only provides moderate accuracy improvement over the crude MC method in the geometry modeled after white matter fibers. Overall, both approaches are shown to be flexible for a variety of geometries and pulse sequences

Flow Imaging Using MRI: Quantification and Analysis

A complex and challenging problem in flow study is to obtain quantitative flow information in opaque systems, for example, blood flow in biological systems and flow channels in chemical reactors. In this regard, MRI is superior to the conventional optical flow imaging or ultrasonic Doppler imaging. However, for high speed flows, complex flow behaviors and turbulences make it difficult to image and analyze the flows. In MR flow imaging, MR tagging technique has demonstrated its ability to simultaneously visualize motion in a sequence of images. Moreover, a quantification method, namely HARmonic Phase (HARP) analysis, can extract a dense velocity field from tagged MR image sequence with minimal manual intervention. In this work, we developed and validated two new MRI methods for quantification of very rapid flows. First, HARP was integrated with a fast MRI imaging method called SEA (Single Echo Acquisition) to image and analyze high velocity flows. Second, an improved HARP method was developed to deal with tag fading and data noise in the raw MRI data. Specifically, a regularization method that incorporates the law of flow dynamics in the HARP analysis was developed. Finally, the methods were validated using results from the computational fluid dynamics (CFD) and the conventional optimal flow imaging based on particle image velocimetry (PIV). The results demonstrated the improvement from the quantification using solely the conventional HARP method

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method

Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data

EMPLOYING DIELECTRIC-BASED MEDIA FOR CONTROLLING FIELD PATTERNS AND WAVE PROPAGATION IN ADVANCED ELECTROMAGNETIC DEVICES

Rapid progress in developing electromagnetic devices and in governing the wave propagation during last years caused renewed interest to dielectric materials. First, engineered dielectric structures with spatial dispersion of their parameters came to replace uniform substrates in antennas and other resonance devices. Then additional boom of dielectric applications was caused by the possibility to employ dielectrics as materials of artificial media. Later, attention of researchers was attracted to properties of the media composed of dielectric resonators (DRs). Currently DRs are used to create metamaterials – the media with unprecedented properties, which cannot be found in nature. Dielectric photonic crystals and metamaterials are considered as the most perspective materials for photonics, since they can be integrated in devices without loss problems, which characterize, for example, plasmonic techniques. Recently, a booming interest emerged to employing in photonics directional light scattering from dielectric particles, since the wavelengths of this light could be controlled by dimensions of particles and their dielectric permittivity. Our work followed basic innovations, which defined contemporary employment of dielectrics in electromagnetics and photonics. In particular, we started from working out new engineering approaches to developing dielectric substrates in patch structures inspired by microstrip patch antennas, which are proposed to serve as MRI RF probes (Chapter 2). Then we redirected our attention to the problems, which restricted employment of dielectrics in left-handed media. In particular, we have shown that negative refraction in all-dielectric metamaterials is irrelevant to Mie resonances in dielectric elements (Chapter 3). Next, we turned to analysis of problems defining directional scattering from dielectric metasurfaces and have demonstrated that the nature of observed phenomena cannot be correctly understood without accounting for strong interaction between “atoms” of metasurafces (Chapter 4). Finally we discussed selected problems met at implementation of photonic crystals in the media of transformation optics based devices and have shown that some of the problems can be solved at employing the phenomenon of self-collimation, characteristic for periodic photonic structures (Chapter 5)