By sampling the self-diffusion of water molecules, diffusion tensor imaging (DTI) is able to characterize the microstructure of brain white matter. Previous DTI studies in schizophrenia have reported white matter alterations as measured by changes in fractional anisotropy. However, DTI analysis is not capable of distinguishing between possible causes, such as a change in the fiber orientation coherence, a change in the intrinsic diffusivity of the fibers, or both. Compared with DTI, high angular resolution diffusion imaging (HARDI) provides more detailed structural information of underlying tissues. Fiber ORientation Estimated using Continuous Axially Symmetric Tensors (FORECAST) is a spherical deconvolution method to analyze HARDI data.<p> Based on Monte Carlo simulations, as well as bootstrap analysis of in vivo human data, the optimal imaging and processing parameters for conducting the FORECAST analysis within typical clinical constraints were determined, and the accuracy of the model was estimated. <p> In order to compare HARDI measurements between subjects, an algorithm was developed to transform the fiber orientation distribution (FOD) function, based on HARDI data, taking into account not only translation, but also rotation, scaling, and shearing effects of the spatial transformation. The algorithm was tested using simulated data, and intra-subject and inter-subject normalization of in vivo human data. All cases demonstrate reliable transformation of the FOD. <p> A voxel-based group comparison of the radial diffusivity and intravoxel fiber coherence was performed based on FORECAST analysis of the HARDI images from both healthy controls and patients with schizophrenia. Decreased FA and elevated radial diffusivity were found in a number of white matter regions in patients. Our results suggest that increased radial diffusivity is the major contributor to the FA reduction, while decreased intravoxel fiber coherence also plays a role in the white matter alterations. This set of techniques, as a step forward from conventional DTI analysis, will likely be helpful in clinical studies of other white matter diseases as well.

The presence of tumor within 1-2 millimeters of the surgical margin following partial mastectomies is strongly correlated with the risk of local breast tumor recurrence; thus, there is a need to develop a non-invasive, real-time, accurate margin evaluation tool to assure complete tumor removal. We previously showed that both Raman spectroscopy and combined autofluorescence and diffuse reflectance spectroscopy can accurately discriminate normal from malignant breast tissues. In this work, the successful use of fluorescence and reflectance-based spectroscopy and spectral imaging for superficial margin evaluation was demonstrated. To achieve greater depth sampling, spatially offset Raman spectroscopy (SORS) was implemented. The feasibility of SORS for breast surgical margin analysis was established by detecting breast tumor signatures through a maximum of 2 mm of normal human breast tissue. A SORS Monte Carlo simulation model was then developed to investigate the effects of varying both normal and tumor layer thicknesses over a wide range of values. The experimental and theoretical SORS results were then used to design a multi-separation SORS probe capable of detecting tumor signatures from below a maximum of 2 mm of normal breast tissue. This probe was used to acquire Raman spectra from frozen-thawed normal breast and breast tumor samples in the laboratory, and a probabilistic classification scheme was developed to determine whether any tumor signature was present in the first 2 mm of tissue under the probe site. Measurements were then made on a small set of freshly excised breast specimens in the clinic to ensure the feasibility of translating this technique to the operating room.

Neural stimulation using infrared light has recently been characterized as a novel method to stimulate peripheral nerves without touching, causing damage, or inducing an electrical stimulation artifact. Infrared neural stimulation (INS) has not been previously achieved in the brain due to the complexity of the neuronal networks. The purpose of this study was to show feasibility of INS in the central nervous system, and determine the optimal parameters for INS in a thalamocortical brain slice model. Wavelength was the first parameter identified since previous studies showed penetration depth of light in tissue determined the threshold radiant energies needed to evoke an action potential in the peripheral nervous system. The wavelength of 3.65 &181m was determined to be the optimal wavelength. Next repetition rate was investigated using the optimal wavelength of 3.65 &181m. Lower threshold radiant energies were observed for higher repetition rates. The final parameter investigated was spot size using light at 3.65 &181m and 30 Hz, and a third order power fit relationship was observed where a larger spot size required less energy to evoke action potentials in a TC slice. A small set of experiments were performed to show intracellular electrical recordings could be used to detect INS evoked signals. The results from this study prove feasibility of INS in CNS, and provide the basis for future in vivo experiments.

Segmentation, or delineation of the boundaries of a region of interest, is an integral part of implementing intraoperative image guidance for kidney tumor resection. Results are affected by the kidney's physiology and pathology as seen in 3-D image data sets, as well as by the methods guiding contour growth. This work explores the variables involved in using level set methods to segment the kidney from computed tomography (CT) images. Multiple level set classes found in the Insight Toolkit were utilized to build a single, semi-automatic segmentation algorithm. This algorithm takes seed points and the image's contrast state as user input and functions independently thereafter. Comparison of the semi-automatic algorithm to an expert's hand-delineation of boundaries, hereafter "handsegmentation," showed that the algorithm performed well both for the images used in its creation and for new image sets. The algorithm also showed lower variability between raters than did handsegmentation. The automatic method's ability to function in a realistic image guidance situation was also evaluated. For three open kidney surgical cases, intraoperative laser range scans were registered to surfaces generated by both handsegmentation and the semi-automatic algorithm. Mean closest point distances between these registered surfaces as well as visual inspection of the distribution of closest point distances showed that the semi-automatic method provided a surface for registration which was comparable to handsegmentation. The inverse of each resultant transformation from these registrations was applied to CT image points, and variability introduced by the different transformations was found to be low, supporting the comparability of the autosegmentation to handsegmentation.

Modality-independent elastography (MIE) is a method of elastography that reconstructs the elastic properties of tissue using images acquired under different loading conditions and a biomechanical model. Boundary conditions are a critical input to the algorithm, and are often determined by time-consuming point correspondence methods requiring manual user input. Unfortunately, generation of accurate boundary conditions for the biomechanical model is often difficult due to the challenge of accurately matching points between the source and target surfaces and consequently necessitates the use of large numbers of fiducial markers. This study presents a novel method of automatically generating boundary conditions by non-rigidly registering two image sets with a Demons diffusion-based registration algorithm. The use of this method was successfully performed in silico using magnetic resonance and X-ray computed tomography image data with known boundary conditions. These preliminary results produced boundary conditions with accuracy of up to 80% compared to the known conditions. These boundary conditions were utilized within a 3D MIE reconstruction to determine an elasticity contrast ratio between tumor and normal tissue. Two phantom experiments were conducted to further test the accuracy of the demons boundary conditions and the MIE reconstruction arising from the use of these conditions. Preliminary results show a reasonable characterization of the material properties on this first attempt and a significant improvement in the automation level and viability of the method.

This work demonstrates a predictive tool to aid neurosurgeons in planning tumor resection therapies by finding the optimal patient orientation that minimizes lateral brain shift in the field of view. Such orientations facilitate tumor access and removal, can reduce the need for retraction, and minimize the impact of brain shift on image guided procedures. In this study, high resolution preoperative magnetic resonance images were utilized in conjunction with pre- and post-resection laser range scans of the craniotomy to produce patient specific finite element models for 6 cases. The cases included 2 large frontal lobe tumors, 3 temporal lobe tumors, and 1 temporal-parietal tumor. General rules for applying resection and modifying model parameters were developed that were consistent with minimal shift within operating room data. In addition, an objective function is introduced to determine patient presentation such that the impact brain shift is minimized. A comparison of the optimal patient presentations as determined by the model-driven objective function to the surgical presentations selected to be optimal by our practicing neurosurgeon [RCT] is performed for 6 cases and demonstrated differences in head rotation angles ranging on average of 8.2°-13.2° and head tilt angles ranging on average 14.7° - 24.4°.

Laser preconditioning augments incisional wound healing by reducing scar tissue and increasing maximum tensile load of the healed wound. Under the hypothesis that HSP70 plays an active role in reported results and to better understand the downstream effects of laser preconditioning, this study utilized a probe-based Raman spectroscopy system to achieve an in-vivo, spatio-temporal biochemical profile of murine skin incisional wounds as a function of laser preconditioning and the presence of HSP70. Raman spectra yielded significant differences in known biochemical peaks between wild-type (WT) and HSP70 knockout mice. Analysis of peak ratios indicated 1) an increase in protein configuration on and surrounding the wound, and 2) increased cellularity that was prolonged in WT mice due to laser treatment. HSP70 is active in protein configuration and cellularity of early wound healing. Laser preconditioning enhances the effects of HSP70 in ways consistent with findings of previous studies. Raman spectroscopy proved a convenient non-invasive method of obtaining information for evaluating these effects and efficacy of wound healing laser treatment.

BIOMEDICAL ENGINEERING NANOSCALE SURFACE ENGINEERING FOR BIOIMAGING AND DRUG DELIVERY ASHWATH JAYAGOPAL Dissertation under the direction of Professor Frederick R. Haselton Nanoengineering of device interfaces permits the presentation of information at length scales consistent with biological processes. This dissertation describes three distinct nanoscale surface engineering strategies with the aim of expanding the scope of applications of nanotechnology in bioimaging and drug delivery. Specifically, the surface engineering approaches utilized in this work are focused on the enhancement of nanoscale device targeting and the development of multifunctional devices. The first aim of this work was focused on the surface functionalization of quantum dot nanocrystals with chemically-modified antibodies or cell penetrating peptides to enable in vivo multiplexed cellular and biomolecular detection applications in vascular disease. Nonspecific quantum dot-antibody binding to endothelial cell surfaces was markedly reduced using an Fc fragment blockade technique, which enabled the simultaneous detection of up to four cellular and/or molecular mediators of diabetes and uveitis. Functionalization of quantum dots with peptides enabled the long-term tracking of leukocyte subset recruitment to atherosclerotic plaques in animal models. In the second aim, nanoscale imaging agents and therapeutics were simultaneously packaged within a lipid matrix for multimodal applications, and translocation mechanisms of surface-functionalized, multimodal lipid nanoparticles across cellular barriers were investigated. Surface engineering of lipid nanoparticles with an anionic polymer coating enabled the translocation of the carrier across cell membranes in vitro. Functionalization of nanoparticles with a trifunctional coating promoted the transcellular transport of lipid nanoparticles across endothelial cell barriers in vitro. Nanoscale payloads incorporated into lipid matrices included quantum dots, iron oxide nanoparticles, gold colloids, and the chemotherapeutic agent paclitaxel. The multimodality of lipid nanoparticles was demonstrated by the optical and magnetic resonance imaging of 4T1 mammary carcinoma cells loaded with lipid nanoparticles featuring iron oxide nanoparticles and quantum dots.

Cell motility plays an important role in development, wound healing and cancer progression. A fundamental unresolved challenge in the field is to obtain reliable measures of motility metrics from single cells and then derive statistically meaningful data on cell population level motility behavior. Currently available tools are limited, for instance, they track cells as unrelated objects (i.e., do not consider cell division), lack ability for high-throughput dynamic parameter extraction, or employ inaccurate tracking algorithms. To extract dynamic morphology and motility parameters at the single cell level we have developed CellAnimation, an open-source high-throughput microscopy framework written in MATLAB which is currently being used in several labs at Vanderbilt and elsewhere. We have also developed a novel cell tracking algorithm which supports mitotic event detection and ancestry recording and we have shown that it outperforms the current state-of-the-art. We applied CellAnimation to investigate the differences in motility between LNCaP-34 and LNCaP-17 prostate cancer cell lines, selected for difference in the levels of expression of hepsin, a type II transmembrane serine protease. Hepsin is overexpressed in over 90% of prostate cancers and correlates with tumor progression. Our lab has previously shown that hepsin cleaves laminin-332, an important protein component of the basement membrane that curbs cancer invasion and progression. Automated cell tracking and data analysis demonstrated that hepsin overexpression promotes increased cell speed and displacement but path tortuosity stays the same; net speed increase was accompanied by a switch in integrin use and a more mesenchymal morphology.

Raman spectroscopy has the potential for providing differential diagnosis between dysplasia and benign cervix with high sensitivity and specificity. Two in vivo studies where designed to further evaluate the potential and improve the sensitivity of Raman spectroscopy to detect cervical dysplasia in a clinical setting. In the first study, the Raman spectral differences between the low grade dysplasia, high grade dysplasia, and benign cervix were characterized with a focus on low grade dysplasia, and a multi-class algorithm was used to classify spectra. The second study characterized spectral variability of the normal cervix due to factors such as hormonal status and the presence of previous disease. Additionally, Raman micro-spectroscopy was used to evaluate differences among histopathology classes and determine where the signal from in vivo experiments originates. These studies have shown that by taking normal variations of the cervix into consideration, Raman spectroscopy can successfully differentiate low grade dysplasia, high grade dysplasia, and benign cervix with high classification accuracy.