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.

To diagnose and treat patients, clinicians concern themselves with a complex set of events. They need to know when, how long, and in what sequence certain events occur. An automated means of extracting temporal meaning in electronic medical records has been particularly challenging in natural language processing. Currently the Tarsqi Toolkit (TTK) is the only complete software package (open source) freely available for the temporal ordering of events within narrative free text documents. This project focused on the TTKs ability to recognize temporal expressions within Veterans Affairs electronic medical documents. A baseline evaluation of TTKs performance on the Timebank v1.2 corpora of 183 news articles and a set of 100 VA hospital admission and discharge notes showed an F-measure of 0.53 and 0.15, respectively. Project development included the correction of missed and partial recognition of temporal expressions, and the expansion of its coverage of time expressions for medical documents. Post-modification, the TTK achieved an F-measure of 0.71 on a different set of 100 VA hospital admission and discharge notes. Future work will evaluate TTKs recognition of temporal expressions within additional sets of medical documents.

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.

Conventional magnetic resonance imaging (MRI) uses contrast that is weighted by the intrinsic tissue parameters T1, and T2. Contrast may also be generated in the rotating frame with the analogous time constants T1ρ or T2ρ. Traditionally T1ρ measurements have been used to investigate low frequency dipolar interactions in the kHz range. However, other biological processes, such as chemical exchange, also occur on this time scale. Recently it has been shown that these processes dominate R1ρ (1/T1ρ) relaxation at high field, and these interactions are of interest as high field imaging systems become increasingly common. We have developed quantitative spin-locking (SL) techniques to probe rotating frame relaxation on clinical and pre-clinical imaging systems. Experiments were performed with these techniques to generate T1ρ maps of pediatric epiphyseal cartilage and mouse brain. If the power of the SL field is varied, the measured T1ρ values will change in a phenomenon known as T1ρ dispersion. These dispersion profiles vary with tissue properties such as pH and metabolite concentration, and the data may be fit with a model to extract unique parameters such as chemical exchange. Novel methods were developed to generate exchange rate based contrast using the contrast features of T1ρ dispersion profiles. A number of exogenous and endogenous contrast agents were quantitatively compared to chemical exchange saturation contrast (CEST) imaging. CEST and SL techniques were evaluated for their complementary features to determine the experimental conditions where each may be most appropriately used. Diffusion processes were explored as an additional contributor to T1ρ dispersion. Various spherical phantoms of different size and material properties were measured with SL techniques to observe their effects on contrast. Methods were developed to separate the effects of diffusion and chemical exchange. The experiments reported here further elucidate the contributing factors to R1ρ relaxation in a variety of biologically relevant molecules and tissues. Finally, the methods resulting from these experiments are useful for generating novel contrast that is primarily dependent on exchange rates.

With cancer encompassing a range of disease states and phenotypes, assessing treatment efficacy early, accurately, and non-invasively is essential to optimize therapy planning on an individualized basis. This work discusses two types of cancer imaging techniques, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI), and the importance they have to several classes of drug treatment regimens. Additionally, this work optimizes and identifies errors in current protocols used in preclinical MRI studies of anti-cancer therapies. These optimized protocols were then used to assess the efficacy of a novel anti-cancer treatment early in the course of therapy. Finally, the relationship between two MR imaging biomarkers frequently used in monitoring cancer therapy were assessed and compared to histology.