This thesis describes the development, characterization, and application of new biomedical technologies developed around the photoacoustic effect. The photoacoustic effect is defined as optical absorption-based generation of ultrasound and provides the foundation for a unique method of imaging and molecular detection. The range of applications of the photoacoustic effect have not yet been fully explored. Photoacoustic endoscopy (PAE) has emerged as a minimally invasive tool for imaging internal organs and tissues. One of the main themes of this dissertation involves the first reported dual-intrauterine photoacoustic and ultrasound deep-tissue imaging endoscope. This device was designed to enable physicians at the point-of-care to better elucidate overall gynecological health, by imaging the lining of the human uterus. Intrauterine photoacoustic endoscopy is made possible due to the small diameter of the endoscope (3mm), which allows for complete, 360-degree organ analysis from within the uterine cavity. In certain biomedical applications, however, further minimization is necessary. Sufficiently small diameter endoscopes may allow for the possibility of applying PAE in new areas. To further miniaturize the diameter of our endoscopes, alternative imaging probe designs were investigated. The proposed PAE architecture utilizes a hollow optical waveguide to allow for concentric guiding of both light and sound. This enables imaging depths of up to several millimeters into animal tissue while maintaining an outer diameter of roughly 1mm. In the final focus of this dissertation, these waveguides are further investigated for use in micropipette electrodes, common in the field of single cell electrophysiology. Pulsed light is coupled with these electrodes providing real-time photoacoustic feedback, useful in navigation towards intended targets. Lastly, fluorescence can be generated and collected at the micropipette aperture by utilizing an intra-electrode tapered optical fiber. This allows for a targeted robotic approach to labeled neurons that is independent of microscopy.

Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained.

The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.

In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.

In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.

In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.

Owing to the surge in development of endovascular devices such as coils and flow diverter stents, doctors are inclined to approach surgical cases non-invasively more often than before. Treating brain aneurysms as a bulging of a weakened area of a blood vessel is no exception. Therefore, promoting techniques that can help surgeons have a better idea of treatment outcomes are of invaluable importance.

In order to investigate the effects of these devices on intra-aneurysmal hemodynamics, the conventional computational fluid dynamics (CFD) approach uses the explicit geometry of the device within an aneurysm and discretizes the fluid domain to solve the Navier-Stokes equations. However, since the devices are made of small struts, the number of mesh elements in the boundary layer region would be considerable. This cumbersome task led to the implementation of the porous medium assumption. In this approach, the explicit geometry of the device is eliminated, and relevant porous medium assumptions are applied. Unfortunately, as it will be shown in this research, some of the porous medium approaches used in the literature are over-simplified. For example, considering the porous domain to be homogeneous is one major drawback which leads to significant errors in capturing the intra-aneurysmal flow features. Specifically, since the devices must comply with the complex geometry of an aneurysm, the homogeneity assumption is not valid.

In this research, a novel heterogeneous porous medium approach is introduced. This results in a substantial reduction in the total number of mesh elements required to discretize the flow domain while not sacrificing the accuracy of the method by over-simplifying the utilized assumptions.

Over the past three decades, particle image velocimetry (PIV) has been continuously growing to become an informative and robust experimental tool for fluid mechanics research. Compared to the early stage of PIV development, the dynamic range of PIV has been improved by about an order of magnitude (Adrian, 2005; Westerweel et al., 2013). Further improvement requires a breakthrough innovation, which constitutes the main motivation of this dissertation. N-pulse particle image velocimetry-accelerometry (N-pulse PIVA, where N>=3) is a promising technique to this regard. It employs bursts of N pulses to gain advantages in both spatial and temporal resolution. The performance improvement by N-pulse PIVA is studied using particle tracking (i.e. N-pulse PTVA), and it is shown that an enhancement of at least another order of magnitude is achievable. Furthermore, the capability of N-pulse PIVA to measure unsteady acceleration and force is demonstrated in the context of an oscillating cylinder interacting with surrounding fluid. The cylinder motion, the fluid velocity and acceleration, and the fluid force exerted on the cylinder are successfully measured. On the other hand, a key issue of multi-camera registration for the implementation of N-pulse PIVA is addressed with an accuracy of 0.001 pixel. Subsequently, two applications of N-pulse PTVA to complex flows and turbulence are presented. A novel 8-pulse PTVA analysis was developed and validated to accurately resolve particle unsteady drag in post-shock flows. It is found that the particle drag is substantially elevated from the standard drag due to flow unsteadiness, and a new drag correlation incorporating particle Reynolds number and unsteadiness is desired upon removal of the uncertainty arising from non-uniform particle size. Next, the estimation of turbulence statistics utilizes the ensemble average of 4-pulse PTV data within a small domain of an optimally determined size. The estimation of mean velocity, mean velocity gradient and isotropic dissipation rate are presented and discussed by means of synthetic turbulence, as well as a tomographic measurement of turbulent boundary layer. The results indicate the superior capability of the N-pulse PTV based method to extract high-spatial-resolution high-accuracy turbulence statistics.

Transcranial electrical stimulation (tES) is a non-invasive brain stimulation therapy that has shown potential in improving motor, physiological and cognitive functions in healthy and diseased population. Typical tES procedures involve application of weak current (< 2 mA) to the brain via a pair of large electrodes placed on the scalp. While the therapeutic benefits of tES are promising, the efficacy of tES treatments is limited by the knowledge of how current travels in the brain. It has been assumed that the current density and electric fields are the largest, and thus have the most effect, in brain structures nearby the electrodes. Recent studies using finite element modeling (FEM) have suggested that current patterns in the brain are diffuse and not concentrated in any particular brain structure. Although current flow modeling is useful means of informing tES target optimization, few studies have validated tES FEM models against experimental measurements. MREIT-CDI can be used to recover magnetic flux density caused by current flow in a conducting object. This dissertation reports the first comparisons between experimental data from in-vivo human MREIT-CDI during tES and results from tES FEM using head models derived from the same subjects. First, tES FEM pipelines were verified by confirming FEM predictions agreed with analytic results at the mesh sizes used and that a sufficiently large head extent was modeled to approximate results on human subjects. Second, models were used to predict magnetic flux density, and predicted and MREIT-CDI results were compared to validate and refine modeling outcomes. Finally, models were used to investigate inter-subject variability and biological side effects reported by tES subjects. The study demonstrated good agreements in patterns between magnetic flux distributions from experimental and simulation data. However, the discrepancy in scales between simulation and experimental data suggested that tissue conductivities typically used in tES FEM might be incorrect, and thus performing in-vivo conductivity measurements in humans is desirable. Overall, in-vivo MREIT-CDI in human heads has been established as a validation tool for tES predictions and to study the underlying mechanisms of tES therapies.

Introduction: There are 350 to 400 pediatric heart transplants annually according to the Pediatric Heart Transplant Database (Dipchand et al. 2014). Finding appropriate donors can be challenging especially for the pediatric population. The current standard of care is a donor-to-recipient weight ratio. This ratio is not necessarily a parameter directly indicative of the size of a heart, potentially leading to ill-fitting allografts (Tang et al. 2010). In this paper, a regression model is presented - developed by correlating total cardiac volume to non-invasive imaging parameters and patient characteristics – for use in determining ideal allograft fit with respect to total cardiac volume.
Methods: A virtual, 3D library of clinically-defined normal hearts was compiled from reconstructed CT and MR scans. Non-invasive imaging parameters and patient characteristics were collected and subjected to backward elimination linear regression to define a model relating patient parameters to the total cardiac volume. This regression model was then used to retrospectively accept or reject an ‘ideal’ donor graft from the library for 3 patients that had undergone heart transplantation. Oversized and undersized grafts were also transplanted to qualitatively analyze virtual transplantation specificity.
Results: The backward elimination approach of the data for the 20 patients rejected the factors of BMI, BSA, sex and both end-systolic and end-diastolic left ventricular measurements from echocardiography. Height and weight were included in the linear regression model yielding an adjusted R-squared of 82.5%. Height and weight showed statistical significance with p-values of 0.005 and 0.02 respectively. The final equation for the linear regression model was TCV = -169.320+ 2.874h + 3.578w ± 73 (h=height, w=weight, TCV= total cardiac volume).
Discussion: With the current regression model, height and weight significantly correlate to total cardiac volume. This regression model and virtual normal heart library provide for the possibility of virtual transplant and size-matching for transplantation. The study and regression model is, however, limited due to a small sample size. Additionally, the lack of volumetric resolution from the MR datasets is a potentially limiting factor. Despite these limitations the virtual library has the potential to be a critical tool for clinical care that will continue to grow as normal hearts are added to the virtual library.

Cerebral aneurysms are pathological bulges in blood vessels of the brain that can rupture and cause brain damage or death. Treating aneurysms by isolating them from circulation can prevent aneurysm rupture. Endovascular techniques for cerebral aneurysm treatment are preferred because they are minimally invasive and have a shorter recovery time, and endovascular coiling is considered the gold standard as a result. The coils used in endovascular treatment come in standard shapes and sizes, mass-manufactured by medical device companies. Clinicians select the coils for treatment based on the aneurysm volume. However, cerebral aneurysms have unique shapes and dimensions, and vary on a patient-specific basis. Therefore, customizing the coils to fit a unique aneurysm morphology by using shape memory alloys could potentially improve endovascular treatment outcomes. In order to shape set a shape memory alloy into a customized coil configuration a fixture based on the aneurysm morphology must first be developed. Digital surface models of aneurysm patient cases were collected from an online repository and isolated from surrounding vasculature. Anchors used to assist in winding coils around these models were then added to create a computational fixture model. These fixtures were 3D printed in stainless steel, and tested on their ability to maintain their shape after being exposed to high temperatures needed in shape setting processes. The study demonstrated that customized fixtures can be created from patient-specific images or models, and manufactured with high levels of accuracy without deformation at high temperatures. The results suggest that 3D printed stainless steel fixtures could be used to develop customized endovascular coils for cerebral aneurysm treatment.

Development of a rapid and label-free Electrochemical Impedance Spectroscopy (EIS) biosensor for Cardiovascular Disease (CVD) detection based on Inerluekin-18 (IL-18) sensitivity was proposed to fill the technology gap between rapid and portable CVD point-of-care diagnosis. IL-18 was chosen for this CVD biosensor due to its ability to detect plaque vulnerability of the heart. Custom (hand) made sensors, which utilized a three electrode configuration with a gold disk working electrode, were created to run EIS using both IL-18 and anti-IL-18 molecules in both purified and blood solutions. The EIS results for IL-18 indicated the optimal detection frequency to be 371Hz. Blood interaction on the working electrode increased the dynamic range of impedance values for the biosensor. Future work includes Developing and testing prototypes of the biosensor along with determining if a Nafion based coating on the working electrode will reduce the dynamic range of impedance values caused by blood interference.

In 1937 Canadian neurosurgeon Wilder Penfield made the first to attempt to map the sensorimotor cortex of the human brain in his paper entitled Somatic Motor and Sensory Representation in the Cerebral Cortex of Man as Studied by Electrical Stimulation. While analogous experimentation had been carried out previously using animal subjects, Penfield sought to understand the delicate and complex neuronal pathways that served as the hidden control mechanisms for human activity. The motor homunculus that followed from his findings has been widely accepted as the standard model for the relative spatial representation of the functionality of the motor cortex, and has been virtually unaltered since its inception. While Penfield took measures to collect cortical data in a manner as accurately as scientifically possible for the time period, his original model is deserving of further analysis using modern techniques. This study uses functional magnetic resonance imaging (fMRI) to quantitatively determine motor function volumes and spatial relationships for four motor tasks: toe, finger, eyebrow, and tongue. Although Penfield's general representation of the superior-to-inferior spatial distribution of the motor cortex was replicated with reasonable accuracy, relative mean task volumes seem to differ from Penfield's original model. The data was first analyzed in each individual patient's native anatomical space for task comparison within a single subject. The volumes of the motor cortex devoted to the eyebrow and toe tasks, which comprise only small portions of the Penfield homunculus, are shown to be relatively large in their fMRI representation compared to finger and tongue. However, these tasks have large deviation values, indicating a lack of consistency in task volume size among patients. Behaviorally, toe movement may include whole foot movement in some individuals, and eyebrows may include face movement, causing distributions that are more widespread. The data was then analyzed in the Montreal Neurological Institute (MNI) space, which is mathematically normalized for task comparison between different subjects. Tongue and finger tasks were the largest in volume, much like Penfield's model. However, they also had substantial deviation, again indicating task volume size inconsistencies. Since the Penfield model is only a qualitative spatial evaluation of motor function along the precentral gyrus, numerical deviation from the model cannot necessarily be quantified. Hence, the results of this study can be interpreted standalone without a current comparison. While future research will serve to further validate these distances and volumes, this quantitative model of the functionality of the motor cortex will be of great utility for future neurological research and during preoperative evaluations of neurosurgical patients.

The purpose of this thesis was to explore how changes in the geometry of a bifurcating cerebral aneurysm will affect the hemodynamics in idealized models after stent treatment. This thesis explores the use of a computationally modeled Enterprise Vascular Reconstruction Device (Cordis, East Bridgewater, NJ), a high porosity and closed cell design. The models represent idealized cases of saccular aneurysms with dome sizes of either 4mm or 6mm and a dome to neck ratio of either 3:2 or 2:1. Two aneurysm contact angles are studied, one at 45 degrees and the other at 90 degrees. The stent was characterized and deployed with the use of Finite Element Analysis into each model. Computational Fluid Dynamic principles were applied in series of simulations on treated and untreated models. Data was gathered in the neck plane for the average velocity magnitude, root mean squared velocity, average flow vector angle of deflection, and the cross neck flow rate. Within the aneurysm, the average velocity magnitude, root mean squared velocity, and average pressure were calculated. Additionally, the mass flow rate at each outlet was recorded. The results of this study indicate that the Enterprise Stent was most effective in the sharper, 90 degree geometry of Model 3. Additionally, the stent had an adverse effect on the Models 1 and 4, which had the smallest neck sizes. Conclusions are that the Enterprise Stent, as a stand-alone treatment method is only reliable in situations that take advantage of its design.