This thesis examines some of the challenges involved with using FTIR spectroscopy for cancer detection including sample preparation and correcting for distortion from cell scattering. Sample preparation affects the spectra differently depending on the cell type, and can lead to significant changes in cancer biomarkers for a given cell type. Biomarkers derived from specific cancer types under one sample preparation are not reliable for other cancer types, and may not be suitable for the same cancer type using a different sample preparation. Cell scattering can also significantly affect the cell spectra, and as a result, correcting for the cell scattering distortion leads to changes in the biomarkers. For reliable cancer detection controlling variability is critical, especially in the complex spectra of biological samples. Standard sample preparation methods and scattering correction post-processing could improve comparison of cancer detection methods.

Non-invasive dynamic optical imaging of small animals requires the development of a novel fluorescence imaging modality. Herein, fluorescence imaging is demonstrated with sub-second camera integration times using agents specifically targeted to disease markers, enabling rapid detection of cancerous regions. The continuous-wave fluorescence imaging acquires data with an intensified or an electronmultiplying charge-coupled device. The work presented in this dissertation (i) assessed dose-dependent uptake using dynamic fluorescence imaging and pharmacokinetic (PK) models, (ii) evaluated disease marker availability in two different xenograft tumors, (iii) compared the impact of autofluorescence in fluorescence imaging of near-infrared (NIR) vs. red light excitable fluorescent contrast agents, (iv) demonstrated dual-wavelength fluorescence imaging of angiogenic vessels and lymphatics associated with a xenograft tumor model, and (v) examined dynamic multi-wavelength, whole-body fluorescence imaging with two different fluorescent contrast agents. PK analysis showed that the uptake of Cy5.5-c(KRGDf) in xenograft tumor regions linearly increased with doses of Cy5.5-c(KRGDf) up to 1.5 nmol/mouse. Above 1.5 nmol/mouse, the uptake did not increase with doses, suggesting receptor saturation. Target to background ratio (TBR) and PK analysis for two different tumor cell lines showed that while Kaposi’s sarcoma (KS1767) exhibited early and rapid uptake of Cy5.5-c(KRGDf), human melanoma tumors (M21) had non-significant TBR differences and early uptake rates similar to the contralateral normal tissue regions. The differences may be due to different compartment location of the target. A comparison of fluorescence imaging with NIR vs. red light excitable fluorescent dyes demonstrates that NIR dyes are associated with less background signal, enabling rapid tumor detection. In contrast, animals injected with red light excitable fluorescent dyes showed high autofluorescence. Dual-wavelength fluorescence images were acquired using a targeted 111In- DTPA-K(IRDye800)-c(KRGDf) to selectively detect tumor angiogenesis and an untargeted Cy5.5 to image lymphatics. After acquiring the experimental data, fluorescence image-guided surgery was performed. Dynamic, multi-wavelength fluorescence imaging was accomplished using a liquid crystal tunable filter (LCTF). Excitation light was used for reflectance images with a LCTF transmitting a shorter wavelength than the peak in the excitation light spectrum. Therefore, images can be dynamically acquired alternating frame by frame between emission and excitation light, which should enable image-guided surgery.

The number of new cancer cases is projected to rise to 23.6 million by 2030 according to the National Cancer Institute. Obesity & cardiovascular diseases are among the leading causes of death worldwide according to recent reports. Biomarkers— any molecules found within a human body that can be used to monitor an individual's health — have been shown to play a significant role in the detection of cancer, obesity, and cardiovascular diseases. Recent studies have shown that in the diagnosis and screening of various human diseases, including cancer, obesity and cardiovascular diseases, circulating microRNAs (miRNAs) are important biomarkers. A crucial roadblock to using microRNA in screening applications is the lack of effective and low-cost microRNA detection. To address this issue, in this study, we have developed a viable method that combines the dielectrophoresis and electrical impedance. Results show this approach can measure very small concentrations of label-free microRNAs (1pM).

A resistively loaded bowtie antenna, intended for applications in breast cancer detection, is adaptively modified through modelling and genetic optimization. The required wideband operating characteristic is achieved through manipulation of the resistive loading of the antenna structure, the number of wires, and their angular separation within the equivalent wire assembly. The results show an acceptable impedance bandwidth of 100.75%, with a challenge VSWR <; 2, over the interval from 3.3 GHz to 10.0 GHz. Feasibility studies were made on the antenna sensitivity for operation in a tissue-equivalent dielectric medium. The simulated and measured results are all in close agreement.

Yes / A compact microstrip antenna design to be used in breast cancer detection is presented. The antenna consists of a radiating patch mounted on two vertical plates, fed by coaxial cable. A study is carried out on different parameters of the antenna. Simulation results show that the antenna possesses a wide bandwidth and this is confirmed experimentally. In experiments, a homogeneous dielectric box, having similar properties to human tissue is used to study the interaction of the antenna with tissue. Even without added matching medium or lumped loads there is good matching when the antenna is in contact with the tissue. Finally a two-element antenna array is investigated numerically, with promising results. / MSCRC

Detecting lung cancer is di cult as it is hidden in the body, and current clinical methods are not elective at an early stage; the one-year survival rate after diagnosis in the World is just 29-33%. Acetyl amantadine (AcAm) is recognised as an exogeneous cancer biomarker because it is the product of a metabolic process known to be significantly up-regulated in cancerous cells. After ingestion, the an-tiparkinson and antiviral drug amantadine is acetylated in the body by the enzyme spermidine/spermine N1 acetyltransferase to give AcAm, which can be detected in patient’s urine. However, techniques previously used to quantify AcAm in urine, such as liquid chromatography-mass spectrometry (LC-MS), are undesirable for clin- ical adoption due to high costs and long run times. Further costs and delays result from the requirement for solid phase extraction (SPE). Therefore, it is highly desired to lower the costs and delays in processing by exploring different quantification approaches, ideally without the need for SPE processing. In this thesis, I investigate the use of surface enhanced Raman spectroscopy (SERS) to quantify AcAm in urinalysis. I prepare two kinds of Raman substrates with hydrophobic pocket surface capture agents beta -cyclodextrin (beta -CD) that work to extract the AcAm from the urine, followed by the surface enhanced Raman measurement using two kinds of Raman systems. The detection strategy is more economical than the currently used LC-MS approach, and enables development of an easy-to-use point-of-care tool that should provide a more rapid turnaround to the health care provider. The next step will be to use real samples. If it is achieved, it will be a promising step in early cancer diagnostics. / Graduate

Attenuation and damping in elastography are naturally of great interest as the presence of these effects in biological tissue goes without question and therefore must be addressed if quantitative assessment of tissue elastic properties is to be achieved. Additionally, given the change in the tissue structure present in the diseases that elastographic imaging seeks to detect and diagnose, there is every reason to expect that the resulting lesions will also exhibit a change in their attenuation behaviour, indicating diagnostic value to any description of the damping property distribution elastographic methods are able to provide. This thesis will present the unique contribution of the development of several Elastographic models for MR based reconstructions of soft tissue. A method for the reconstruction of both Viscoelastic and Rayleigh damping based damped elastic properties has been developed for use with MR detected time-harmonic motion data and has been shown to lead to reasonable results in both homogeneous and heterogeneous phantoms of varying material types. A poro-elastic modelling is thought to provide a more accurate description of tissue structure by accounting for, in part, the complex interactions between the solid and fluid phases present in vivo. The foundation for a poro-elastic material behaviour will be explored and presented to support the premise. A meaningful mapping of the orthotropic shear moduli distributions in three directions has demonstrated enough evidence that the orthotropic MRE can be a feasible technique to determine orthotropic elasticity parameters of a biological tissue, noninvasively. The orthotropic achievements throughout this project can be useful for future clinical cancer diagnostics by augmenting the information obtained from the orthotropic MRE reconstructions between normal tissue and tumours.

<p>The prevalence of cancer has increasingly become a significant threat to human health and as such, there exists a strong need for developing novel methods for early detection and effective therapy. Nanotheranostics, a combination of diagnostic and therapeutic functions into a single nanoplatform, has great potential to be used for cancer management by allowing detection, real-time tracking, image-guided therapy and therapeutic response monitoring. Gold nanostars (GNS) with tip-enhanced plasmonics have become one of the most promising platforms for cancer nanotheranostics. This work is aimed at addressing the challenges of sensitive cancer detection, metastasis treatment and recurrence prevention by combining state-of-the-art nanotechnology, molecular imaging and immunotherapy. A multifunctional GNS nanoprobe is developed with capabilities ranging from non-invasive, multi-modality cancer detection using positron emission tomography (PET), magnetic resonance imaging (MRI) and X-ray computed tomography (CT), to intraoperative tumor margin delineation with surface enhanced Raman spectroscopy (SERS) and high-resolution nanoprobe tracking with two-photon photoluminescence (TPL), as well as cancer treatment with photoimmunotherapy. The GNS nanoprobe with PET scans is particularly exceptional in detecting brain malignancies as small as 0.5 mm. To the best of our knowledge, the developed GNS nanoprobe for PET imaging provides the most sensitive means of brain tumor detection reported so far. In addition, the GNS nanoprobe exhibits superior performance as photon-to-heat transducer and can be used for specific photothermal therapy (PTT). More importantly, GNS-mediated PTT combined with checkpoint inhibitor immunotherapy has been found to trigger a memorized immunoresponse to treat cancer metastasis and prevent recurrence in mouse model studies. Furthermore, a 6-month in vivo toxicity study including body weight monitoring, blood chemistry test and histopathology examination demonstrate GNS nanoparticles’ biocompatibility. Therefore, the multifunctional GNS nanoprobe exhibits superior cancer detection and treatment capabilities and has great promise for future clinical translation in cancer management.</p> / Dissertation

Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.

The first project focused on the preparation, characterization, and application of dual emission quantum dot encoded mesoporous silica microparticles. The quantum dots were added in precisely controlled ratios and were stably encapsulated within the pores of the silica. Several experiments were performed to test the superior stability of the quantum dot-silica composites over dye-loaded silica particles. The composite particles exhibited very high fluorescence, were functionalized with antibodies, and were used as signal transducers for the detection of a protein expressed by breast cancer cells. The second project focused in more detail on the detection capabilities of the quantum dot-silica composites. Three different types of quantum dot-silica composites were prepared. Each type was loaded with a separate type of quantum dot with distinct emission wavelengths and was functionalized with separate antibodies for detection of three different breast cancer biomarkers. These three composite sensors were used together for the simultaneous detection of each of the breast cancer markers. The initial strategy utilized the direct detection method in which the antigen is nonspecifically adsorbed to a glass plate. An improved second strategy was more sensitive and used a capture antibody which was covalently bound to a glass plate to immobilize the antigen. The third project focused on the preparation and application of magnetic, fluorescent human serum albumin nanoparticle composites. A fluorescent drug analogue and iron oxide nanoparticles were encapsulated into 100 nm human serum albumin nanoparticles. The advantage of these composite particles is that they could be used as a theranostic tool which could target, detect, and treat diseased tissue in a single application. Release of the drug analogue from the nanocomposites was achieved by addition of proteolytic enzymes that are expressed or overexpressed in cancer cells. The temporal release of the fluorescent drug analogue was measured as a function of enzyme concentration. The amount of drug released was directly proportional to enzyme concentration.