Respiratory motion causes artifacts and blurring of cardiac structures in reconstructed images of SPECT and PET cardiac studies. Hysteresis in respiratory motion causes the organs to move in distinct paths during inspiration and expiration. Current respiratory motion correction methods use a signal generated by tracking the motion of the abdomen during respiration to bin list- mode data as a function of the magnitude of this respiratory signal. They thereby fail to account for hysteretic motion. The goal of this research was to demonstrate the effects of hysteretic respiratory motion and the importance of its correction for different medical imaging techniques particularly SPECT and PET. This study describes a novel approach for detecting and correcting hysteresis in clinical SPECT and PET studies. From the combined use of MRI and a synchronized Visual Tracking System (VTS) in volunteers we developed hysteretic modeling using the Bouc-Wen model with inputs from measurements of both chest and abdomen respiratory motion. With the MRI determined heart motion as the truth in the volunteer studies we determined the Bouc Wen model could match the behavior over a range of hysteretic cycles. The proposed approach was validated through phantom simulations and applied to clinical SPECT studies.

"This dissertation describes the development of a methodology for implementing robotic systems for interventional procedures under intraoperative Magnetic Resonance Imaging (MRI) guidance. MRI is an ideal imaging modality for surgical guidance of diagnostic and therapeutic procedures, thanks to its ability to perform high resolution, real-time, and high soft tissue contrast imaging without ionizing radiation. However, the strong magnetic field and sensitivity to radio frequency signals, as well as tightly confined scanner bore render great challenges to developing robotic systems within MRI environment. Discussed are potential solutions to address engineering topics related to development of MRI-compatible electro-mechanical systems and modeling of steerable needle interventions. A robotic framework is developed based on a modular design approach, supporting varying MRI-guided interventional procedures, with stereotactic neurosurgery and prostate cancer therapy as two driving exemplary applications. A piezoelectrically actuated electro-mechanical system is designed to provide precise needle placement in the bore of the scanner under interactive MRI-guidance, while overcoming the challenges inherent to MRI-guided procedures. This work presents the development of the robotic system in the aspects of requirements definition, clinical work flow development, mechanism optimization, control system design and experimental evaluation. A steerable needle is beneficial for interventional procedures with its capability to produce curved path, avoiding anatomical obstacles or compensating for needle placement errors. Two kinds of steerable needles are discussed, i.e. asymmetric-tip needle and concentric-tube cannula. A novel Gaussian-based ContinUous Rotation and Variable-curvature (CURV) model is proposed to steer asymmetric-tip needle, which enables variable curvature of the needle trajectory with independent control of needle rotation and insertion. While concentric-tube cannula is suitable for clinical applications where a curved trajectory is needed without relying on tissue interaction force. This dissertation addresses fundamental challenges in developing and deploying MRI-compatible robotic systems, and enables the technologies for MRI-guided needle-based interventions. This study applied and evaluated these techniques to a system for prostate biopsy that is currently in clinical trials, developed a neurosurgery robot prototype for interstitial thermal therapy of brain cancer under MRI guidance, and demonstrated needle steering using both asymmetric tip and pre-bent concentric-tube cannula approaches on a testbed."

"This research concentrates on two major engineering areas associated with biomedical instrumentation that have recently gained significant academic and industrial interest: the gradient coil design for Magnetic Resonance Imaging (MRI) and the high frequency full-wave field simulations with the Method of Moments (MoM). A new computational approach to the design of gradient coils for magnetic resonance imaging is introduced. The theoretical formulation involves a constrained cost function between the desired field in a particular region of interest in space and the current-carrying coil plane. Based on Biot-Savart’s integral equation, an appropriate weight function is introduced in conjunction with linear approximation functions. This permits the transformation of the problem formulation into a linear matrix equation whose solution yields discrete current elements in terms of magnitude and direction within a specified coil plane. These current elements can be synthesized into practical wire configuration by suitably combining the individual wire loops. Numerical predictions and measurements underscore the success of this approach in terms of achieving a highly linear field while maintaining low parasitic fields, low inductance and a sufficient degree of shielding. Experimental results confirm the field predictions of the computational approach. Extending the numerical modeling efforts to dynamic phenomena, a novel MoM formulation permits the computation of electromagnetic fields in conductive surfaces and in three-dimensional biological bodies. The excitation can be provided with current loops, voltage sources, or an incident electromagnetic wave. This method enables us to solve a broad spectrum of problems arising in MRI: full-wave RF coil simulations, eddy currents predictions in the magnet bore, and induced currents in the biological body. Surfaces are represented as triangles and the three-dimensional bodies are subdivided into tetrahedra. This numerical discretization methodology makes the approach very flexible to handle a wide range of practical coil geometries. Specifically, in this thesis the MoM is employed to study the effect of switching gradient coils in the presence of a biological load. "

Since the first robotic surgical intervention was performed in 1985 using a PUMA industrial manipulator, development in the field of surgical robotics has been relatively fast paced, despite the tremendous costs involved in developing new robotic interventional devices. This is due to the clear advantages to augmented a clinicians skill and dexterity with the precision and reliability of computer controlled motion. A natural extension of robotic surgical intervention is the integration of image guided interventions, which give the promise of reduced trauma, procedure time and inaccuracies. Despite magnetic resonance imaging (MRI) being one of the most effective imaging modalities for visualizing soft tissue structures within the body, MRI guided surgical robotics has been frustrated by the high magnetic field in the MRI image space and the extreme sensitivity to electromagnetic interference. The primary contributions of this dissertation relate to enabling the use of direct, live MR imaging to guide and assist interventional procedures. These are the two focus areas: creation both of an integrated MRI-guided development platform and of a stereotactic neural intervention system. The integrated series of modules of the development platform represent a significant advancement in the practice of creating MRI guided mechatronic devices, as well as an understanding of design requirements for creating actuated devices to operate within a diagnostic MRI. This knowledge was gained through a systematic approach to understanding, isolating, characterizing, and circumventing difficulties associated with developing MRI-guided interventional systems. These contributions have been validated on the levels of the individual modules, the total development system, and several deployed interventional devices. An overview of this work is presented with a summary of contributions and lessons learned along the way.

Background: Parkinson's disease (PD) is a chronic, progressive, neurodegenerative disorder with currently no known cure. PD has a significant impact on quality of life of the patients, as well as, the caregivers and family members. It is the second most common cause of chronic neurological disability in US and Europe. According to National Parkinson's Foundation, there are almost 1 million patients in the Unites States and 50,000 to 60,000 new cases of PD are diagnosed each year. The total number of cases of PD is predicted to double by 2030. The annual cost associated with this disease is estimated to be $10.8 billion in the United States, including the cost of treatment and the cost of the disability. Although it is primarily thought of as a movement-disorder and is clinically diagnosed based on motor symptoms, non-motor symptoms such as cognitive and emotional deficits are thought to precede the clinical diagnosis by almost 20 years. By the time of clinical diagnosis, there is 80% loss in the dopamine content in the striatum and 50% degeneration of the substantia nigra dopamine cells. The research presented in this thesis was an attempt to develop an animal model of PD in its pre-motor stages. Such a model would allow us to develop pre-clinical markers for PD, and facilitate the development and testing of potential treatment strategies for the non-motor symptoms of the disorder. Specific Aims: There were five specific aims for this research: * The first specific aim dealt with development of a rat model of PD with slow, progressive onset of motor deficits, determination of timeline for future studies, and quantification the dopamine depletion in this model at a pre-motor stage. * The second and the third specific aims focused on testing for emotional (aversion) deficits and cognitive (executive functioning) deficits in this rat model at the 3 week timepoint determined during specific aim 1. * The fourth specific aim was to determine the brain network changes associated with the behavioral changes observed our rat model using resting state connectivity as a measure. * The fifth and the final specific aim was to test sodium butyrate, a drug from the histone deacetylase inhibitor family, as a potential treatment option for cognitive deficits in PD. Results: The 6-hydroxy dopamine based stepwise striatal lesion model of pre-motor PD, developed during this research, exhibits delayed onset of Parkinsonian gait like symptoms by week 4 after the lesions. At 3 weeks post lesion (3WKPD), the rats exhibit 27% reduction in striatal dopamine and 23%reduction in substantia nigra dopamine cells, with lack of any apparent motor deficits. The 3WKPD rats also exhibited changes in aversion. The fMRI study with the aversive scent pointed towards possible amygdala dysfunction sub-serving the aversion deficits. The executive function deficits tested using a rat analog of the Wisconsin card sorting test, divulged an extra-dimensional set shifting deficit in the 3WKPD rats similar to those reported in PD patients. The resting state connectivity study indicated significant changes in the 3WKPD rats compared to age matched controls. We observed increased overall connectivity of the motor cortex and increased CPu connectivity with prefrontal cortex, cingulate cortex, and hypothalamus in the 3WKPD rats compared to the controls. These observations parallel the observations in unmedicated early-stage PD patients. We also observed negative correlation between amygdala and prefrontal cortex as reported in humans. This negative correlation was lost in 3WKPD rats. Sodium butyrate treatment, tested in the cognitive deficit study, was able to ameliorate the extra-dimensional set shifting deficit observed in this model. This treatment also improved the attentional set formation. Conclusion: Taken together, our observations indicate that, the model of pre-motor stage PD developed during this research is a very high face validity rat model of late Braak stage 2 or early Braak stage 3 PD. Sodium butyrate was able to alleviate the cognitive deficits observed in our rat model. Hence, along with the prior reports of anti-depressant and neuroprotective effects of this drug, our results point towards a possible treatment strategy for the non-motor deficits of PD.

Needle-based interventions are pervasive in Minimally Invasive Surgery (MIS), and are often used in a number of diagnostic and therapeutic procedures, including biopsy and brachytherapy seed placement. Magnetic Resonance Imaging (MRI) which can provide high quality, real time and high soft tissue contrast imaging, is an ideal guidance tool for image-guided therapy (IGT). Therefore, a MRI-guided needle-based surgical robot proves to have great potential in the application of percutaneous interventions. Presented here is the design of reconfigurable fiducial-integrated modular needle driver for MRI-guided percutaneous interventions. Further, an MRI-compatible hardware control system has been developed and enhanced to drive piezoelectric ultrasonic motors for a previously developed base robot designed to support the modular needle driver. A further contribution is the development of a fiber optic sensing system to detect robot position and joint limits. A transformer printed circuit board (PCB) and an interface board with integrated fiber optic limit sensing have been developed and tested to integrate the robot with the piezoelectric actuator control system designed by AIM Lab for closed loop control of ultrasonic Shinsei motors. A series of experiments were performed to evaluate the feasibility and accuracy of the modular needle driver. Bench top tests were conducted to validate the transformer board, fiber optic limit sensing and interface board in a lab environment. Finally, the whole robot control system was tested inside the MRI room to evaluate its MRI compatibility and stability.

A new method for the simulation of radio frequency (RF) coils has been developed. This method utilizes the FEM simulation package Ansoft HFSS as a base for the modeling of RF coils with complex biological loading effects. The abilities of this software have been augmented with custom MATLAB code to enable the fast prediction of lumped element values needed to properly tune and match the coil structure as well as to perform the necessary post processing of simulation data in order to quickly generate and evaluate field data of the resonating coil and compare design variations. This method was evaluated for accuracy and implemented in the re-design of an existing four channel breast coil array for clinical imaging of the female breasts. Based on the simulation results, a commercially viable printed circuit board (PCB) implementation was developed and tested in a clinical 1.5 T MR scanner. The new design allows for wide open bilateral access to the breast regions in order to accommodate various interventional procedures. The layout has also increased axillary B1 field coverage with minor penalty to the signal-to-noise ratio of the coil array, enabling high-resolution imaging over a wide field-of-view.

Imaging modalities such as CT, MRI, and SPECT have had a tremendous impact on diagnosis and treatment planning. These imaging techniques have given doctors the capability to visualize 3D anatomy structures of human body and soft tissues while being non-invasive. Unfortunately, the 3D images produced by these modalities often have boundaries between the organs and soft tissues that are difficult to delineate due to low signal to noise ratios and other factors. Image segmentation is employed as a method for differentiating Regions of Interest in these images by creating artificial contours or boundaries in the images. There are many different techniques for performing segmentation and automating these methods is an active area of research, but currently there are no generalized methods for automatic segmentation due to the complexity of the problem. Therefore hand-segmentation is still widely used in the medical community and is the €œGold standard€� by which all other segmentation methods are measured. However, existing manual segmentation techniques have several drawbacks such as being time consuming, introduce slice interpolation errors when segmenting slice-by-slice, and are generally not reproducible. In this thesis, we present a novel semi-automated method for 3D hand-segmentation that uses mesh extraction and surface parameterization to project several 3D meshes to 2D plane . We hypothesize that allowing the user to better view the relationships between neighboring voxels will aid in delineating Regions of Interest resulting in reduced segmentation time, alleviating slice interpolation artifacts, and be more reproducible.

Complex Regional Pain Syndrome (CRPS) is a chronic neuropathic pain condition associated with significant alterations in the somatosensory and motor cortex brain regions. Cognitive-affective alterations have recently been recognized in patients suffering with CRPS, however, relatively little neuroimaging research has been done to examine these dimensions. Moreover, many children and adolescents suffer from CRPS, but very little is known about the impact of this condition on brain states in the pediatric population. The aim of this paper is to assess the structural brain differences between children with CRPS and healthy controls and to examine to what degree fear level influences such differences. This study is part of a larger investigation that integrates functional and structural brain differences to evaluate fear-related brain circuitry in patients with CRPS. Thirty-seven patients with CRPS were age and gender matched with 35 healthy controls. The two groups underwent structural magnetic resonance imaging (MRI) scans as well as completed the Fear of Pain Questionnaire, child report (FOPQ). To examine gray matter differences, voxel-based morphometry (VBM) and cortical thickness (CT) analysis was completed. Patients with CRPS in this study had an average age of 13.2 (SD=2.7) and were predominantly female (73%). Of the 35 patients who completed FOPQ, 49% reported clinically significant pain-related fear. Compared with healthy controls, CRPS patients had significantly less in gray matter (GM) volume in pain- and fear-related brain regions, including the dorsolateral prefrontal gyrus, motor and somatosensory cortex, anterior and posterior cingulate cortex, nucleus accumbens, putamen, amygdala, and hippocampus. Furthermore, gray matter decreases in regions such as anterior midcingulate cortex, nucleus accumbens, and putamen were associated with elevated pain-related fear in patients. Differences in gray matter volume in fear-circuitry areas could potentially be one mechanism by which abnormal fear learning and extinction develops in youth suffering with CRPS. Further research examining brain changes post-treatment is needed to determine if treatments that target improving pain and fear levels are associated with concomitant normalization of brain structures.

The primary aim of this thesis is to investigate the transition between different flow regimes in porous media. The complete transition spectrum of single-phase flow, from creeping flow to inertial, unsteady laminar, and turbulent flow regimes, was examined in sphere packings. Further understanding of the fundamental fluid dynamics was derived based on the pore-scale flow visualisation using magnetic resonance velocimetry (MRV). Spiral imaging was selected as the ultrafast imaging protocol to probe the transient phenomena, and the acquisition was further accelerated by combining subsampling and compressed sensing reconstruction. In a random sphere packing column with column-to-diameter ratio of 3.44, the inertial effect and the onset of unsteady regime were examined with respect to the principal flow characteristics: the inertial core/channeling, backflow, and helical vortices. Helical vortices have been observed experimentally in a random packing for the first time, and the analogy between the swirling flow and helical vortices provides insight into the design and operation of packed bed reactors. Another new observation is that the transition to the unsteady regime is a highly heterogeneous process, where the evolution of the flow instability depends on the pore geometry. Moreover, pixelwise validation was achieved between the experimental and simulation results on three-dimensional velocity fields in the inertial regime; this is enabled by an image-based meshing pipeline, which reproduces the geometry of the random packing in MRV for the numerical simulation. The unsteady regimes were further investigated using a regular sphere packing, the simple cubic packing (SCP). The spectral analysis, in both the random and regular packing, revealed a route to chaos from the steady to periodic, quasi-periodic, and chaotic dynamics, which was only predicted numerically before. During the transition to turbulence, the coherent structures were extracted using proper orthogonal decomposition (POD), which yields a coherent picture regarding the turbulent dynamics, when combined with the skewness, flatness, and quadrant analysis. Furthermore, it was found that the macroscopic properties converged at lower Reynolds number than the microscopic features. In conclusion, the opportunity to measure flow fields at high spatial and temporal resolution will play an increasingly significant role in the advancement of fundamental fluid dynamics. In this thesis, MRV is used, which is particularly advantageous for non-invasive measurements in opaque systems. This thesis provides the experimental and analysis toolkits for such studies and has demonstrated the contribution to characterising and understanding different flow regimes in porous media.