Laser scanning confocal microscopy (LSCM) is an optical imaging technique that provides improved resolution and sensitivity over conventional methods of optical microscopy. However, the cost of most commercial LSCM systems exceeds the financial limitations of many smaller laboratories. The design of a custom LSCM created at a fraction of the cost of a commercial model is discussed in this paper. The increase in the incidence rate of skin cancer in the world today is alarming, as such, it is essential to provide an early, rapid and effective method for in vivo diagnostics of human skin tissue. LSCM is capable of detecting alterations in skin morphology and configuration, as well as providing chemical composition information which may be indicative of the development of skin cancer. If developed successfully, LSCM could replace the current invasive biopsy procedures performed today with a quick, non-invasive optical scanning method that would prove beneficial for both patients and physicians alike.

Fluorescence microscopy has been a valuable tool in the field of biological science as it allows one to study the structure and interaction of protein complexes and organelles in living cells. However, conventional optical microscopy technique has been limited by a trade-off between spatiotemporal resolution, signal contrast, and photodamage to the biological samples. It means that an increase in spatial resolution or signal contrast comes at the cost of higher laser power, serial-scanning, or longer image acquisition time. Unfortunately, this leads to severe photobleaching and photodamage to the samples and/or limited throughput of imaging, which is highly challenging to be circumvented through only optical imaging technique. Therefore, one has turned to artificial intelligence (AI) in image processing, applying deep learning algorithms to different imaging modalities to overcome these traditional limitations in optical microscopy systems. Herein we present multiple strategies on how deep learning can be applied to solve challenging and fundamental problems in different fluorescence microscopy modalities. To do so, we present UNet-RCAN, a two-step deep learning network architecture based on a residual U-Net and residual channel attention network (RCAN) for image restoration. We demonstrate that UNet-RCAN achieves higher prediction accuracy compared to other state-of-the-art deep learning algorithms while maintaining the resolution of an output image compared to ground-truth data acquired with optical microscopes. We applied our method to three fluorescence imaging modalities. Firstly, we successfully demonstrate that UNet-RCAN can achieve up to two orders of magnitude acceleration in stimulated emission depletion (STED) imaging while maintaining super-resolution. This significant acceleration enables mitigation of photobleaching and photodamage by robust restoration of noisy 2D and 3D STED images from multiple targets as well as live-cell STED imaging of inner-mitochondrial dynamics with a ten-fold increase in the number of acquired frames compared to conventional STED microscopy. Secondly, we apply our approach in restoring high-resolution widefield deconvolution images of living cells with low light intensity and low photodamage. We show that the accuracy of deconvolution can significantly improve after image restoration with deep learning. Lastly, we show the application of UNet-RCAN in the resolution enhancement of single-shot volumetric imaging with a low numerical aperture objective lens.

Traditional brain imaging modalities, for example, MRI, CT scan, X-ray, etc. can provide precise and high-resolution images of the brain for diagnosing lesions, tumors or clots inside the brain. However, these modalities require bulky and expensive test setups accessible only at specialized diagnostic centers, and hence may not be suitable or affordable to many patients. Furthermore, the inherent health risks limit the usability of these modalities for frequent monitoring. Microwave imaging is deemed a promising alternative due to its being cost-effective, portable, non-ionizing, non-intrusive. Therefore, this work aims to design an effective microwave head imaging system for the detection of blood clots inside the brain. Two miniaturized antipodal Vivaldi antenna designs are proposed which can provide wideband operation covering the low microwave frequency range (within 1 - 6 GHz) while having electrically small dimensions, directional radiation pattern with reasonable gain, and without requiring immersion in any matching/ coupling liquid. A head imaging system is presented which utilizes a quarter-head scanning approach, to reconstruct four images of the brain by scanning four quarters of the head, using the designed antipodal wideband Vivaldi antenna. A numerical brain model, with and without the presence of blood clot, is simulated using the proposed head-imaging system. At each quarter, the antenna is placed at nine different positions for scanning. The reflected signal at each position is processed and using confocal microwave imaging technique four images of the brain are reconstructed. A comparison is made among the four images in terms of their intensities, for the detection and approximate location of the blood clot inside the brain. The presence of higher intensity regions in any specific quarter of the head demonstrates the presence of a clot and its location and validates the feasibility of the proposed head imaging system using the low frequency wideband Vivaldi antenna.

<p>Mechanical oscillating drills and saws are used in orthopaedic surgery to cut bone and develop screw-holes; however, their use causes friction resulting in significant thermal damage. Ultrashort pulsed lasers appear well-suited to replace traditional tools as they have the ability to efficiently remove bone tissue while causing only minimal collateral damage. Laser ablation also has the added advantages of: (i) no mechanical vibration; (ii) minimal invasiveness; and (iii) small focus spot size. In this thesis work, we experimentally investigated a few key aspects of ultrashort laser ablation of bone tissue.</p> <p>The ablation threshold of unaltered bone was measured using the <em>D</em>²technique and found to range from 1.66 J/cm²± 0.87 J/cm² to 2.37 J/cm²± 0.78 J/cm² depending on incident pulse number. The reduction in ablation threshold with pulse number was an indication of an incubation effect. Using a power law model, the incubation coefficient, ζ, was measured to be 0.89 ± 0.03.</p> <p>The effect of specific laser parameters and drilling protocols on ablation efficiency was also characterized. For ultrashort pulses (≤10 ps), the removal rate was found to be inversely related to the pulse duration; however, irradiation with 5-10 ps pulses were also shown to result in significant tissue removal. With a pulse repetition rate of 1 kHz, the removal rate was observed to be highest when ablating with 50-100 pulses per spot.</p> <p>Larger volumes (>1 mm³) of bone tissue were removed using laser scanning procedures. A series of scanned concentric circles produced a structure ~2.4 mm deep; however, ablated side-lobes were present at oblique angles to the incident beam. A two-layer structure subsequently produced no side-lobes. The ablative precision in trabecular bone was observed to be less than cortical bone. Using mimicked Nd:YAG laser parameters, cylindrical drilling produced craters significantly less deep than those achieved with a typical Ti:Sapphire configuration. The ability to drill large-scale holes using low average pulse energies and optimized scanning procedures will alleviate the stringent requirements for optical components in clinical practice.</p> / Master of Science (MSc)

<p>Current gold standard practices for the diagnosis of tissue disease involve invasive tissue biopsies subjected to a time consuming histopathological examination process. An optical biopsy can offer a non-invasive diagnostic alternative by exploiting the properties of naturally occurring light-tissue interactions. A time-resolved fluorescence spectroscopy instrument (355 nm excitation) has previously been developed by our lab to capture the fluorescence response of gastrointestinal tissue (370-550 nm in 5 nm increments, 25 ns at 1000 ps/pt). Measurements were conducted ex-vivo during routine upper gastrointestinal tract biopsies on duodenum, antrum, stomach body, and esophageal tissue. The work currently presented is focused on protocol development for tissue handling, measurement collection, clinical data management, fluorescent decay modeling using Laguerre based deconvolution, instrument performance evaluation, and k-means based classification.</p> <p>Descriptive parameters derived from spectral (total signal intensity) and temporal (lifetime and Laguerre polynomial coefficients) analysis were used to evaluate the data. It was found that data were only compromised when the total signal intensity for the peak wavelength 455 nm fell blow 19.5 V·ns. The data did not exhibit any signs of photobleaching or pulse width broadening that would have otherwise distorted the lifetime from its true fluorescence response. Data for diseased tissue were limited so the clinical diagnosis was used to classify normal duodenum tissue from normal esophageal tissue. Over 400 pairs of parameters demonstrated k-means can identify duodenum tissue with 87.5 % sensitivity and 87.5 % specificity or better. With some dimensional axis transformations these results could be improved. The lifetimes are not factors here but the relative intensity and decay shape were. Protocols can be applied to diseased or other tissue types with little adaptation. Just a single set of parameters may hold the key to help surgeons choose optimum locations for traditional biopsies or perhaps one day replace them altogether.</p> / Master of Applied Science (MASc)

<p>Quantitatively measuring oxygen saturation is important to characterize the physiological or pathological state of tissue function. In this thesis, we demonstrate the possibility of using susceptibility mapping to noninvasively estimate the venous blood oxygen saturation level. Accurate susceptibility quantification is the key to oxygen saturation quantification. Two approaches are presented in this thesis to generate accurate and artifact free susceptibility maps (SM): a regularized inverse filter and a k-space iterative method. Using the regularized inverse filter, with sufficient resolution, major veins in the brain can be visualized. We found that different sized vessels show a different level of contrast depending on their partial volume effects; larger vessels show a bias toward a reduced susceptibility approaching 90% of the expected value. Also, streaking artifacts associated with high susceptibility structures such as veins are obvious in the reconstructed SM. To further improve susceptibility quantification and reduce the streaking artifacts in the SMs, we proposed a threshold-based k-space iterative approach that used geometric information from the SM itself as a constraint to overcome the ill-posed nature of the inverse filter. Both simulations and in vivo results show that most streaking artifacts inside the SM were suppressed by the iterative approach. In simulated data, the bias toward lower mean susceptibility values inside vessels has been shown to decrease from around 10% to 2% when choosing an appropriate threshold value for the proposed iterative method, which brings us one step closer to a practical means to map out oxygen saturation in the brain.</p> / Doctor of Philosophy (PhD)

<p>The annual incidence rate of tumours in the brain and central nervous system (CNS) was 19.89 per 100,000 persons between 2004 and 2008 in the United States. Surgery is a common treatment option for brain and CNS tumours. Typically, biopsy followed by histological analysis is used to confirm tumour types and margin during neurosurgery as an intraoperative diagnostic tool. However, this biopsy method is invasive, sampling number limited and not in real-time. To overcome these problems, many minimally invasive optical techniques, called optical biopsies, have been developed towards intraoperative diagnosis.</p> <p>The research work carried out in this dissertation focuses on combining the time-resolved fluorescence (TRF) and diffuse reflectance (DR) spectroscopy towards intraoperative tumour margin detection in neurosurgery. Combining these two modalities allows us to obtain additional contrast features, thus potentially improving the diagnostic accuracy. To achieve this goal, first, a clinically compatible integrated TRF-DR spectroscopy instrument was developed for <em>in vivo</em> brain tumour study. An acousto-optical-tunable-filter-based spectrometer was designed to acquire the time-resolved fluorescence signal. A dual-modality fibre optic probe was used to collect the TRF and DR signals in a small volume. The system’s capabilities of resolving fluorescence spectrum and lifetime, and optical properties were characterized and validated using tissue phantoms. Second, in order to retrieve the fluorescence impulse response function accurately from measured fluorescence signals, a robust Laguerre-based deconvolution method was optimized by using the constrained linear least squares fitting and high order Laguerre function basis. This optimized Laguerre-based deconvolution method overcomes the over-fitting problem introduced by low signal-to-noise ratio and complex fitting model. Third, an <em>ex vivo</em> clinical study of brain tumours was carried out using the TRF and DR spectroscopy. Fluorescence spectra and lifetime features were selected to classify various tumour types. The sensitivity and specificity of meningioma grade I differentiated from meningioma grade II are both 100%. Finally, in order to increase the measurement tissue volume and obtain imaging contrast features, a scanning-based hyperspectral fluorescence lifetime imaging system was developed. This setup can provide time-, space-, spectrum- resolved multi-dimensional images for tumour margin detection.</p> / Doctor of Philosophy (PhD)

Indocyanine green (ICG) is a fluorescent dye used as an indicator in medicine and surgery. The maximum absorption wavelength of ICG is at 785 nm, while the maximum emission is around 820 nm. ICG is nontoxic and is rapidly excreted into the bile. Near infrared (NIR) fluorescence imaging or spectroscopy offer new settings for seeing the blood vessels, and also in oncological applications for finding sentinel lymph nodes (SLN) to investigate if the cancer has spread from the tumor to the lymphatic system. Given the aforementioned applications, the aim of this thesis was to develop a hardware control and a user interface in LabVIEW, and to evaluate the software, as well as the instrumentation using phantom measurements.The system consisted of a spectrometer, a laser (785 ± 5 nm) for ICG excitation, optical filters, and a fiber optical probe containing five fibers for light excitation, and one for light collection. The basic LabVIEW program designed for the spectrometer was used, and additional features were added such as the recording functions, online measurements, opening of the recorded files, saving comments, and a loop was created for the laser control. Optical phantoms were prepared to model tissue for measurements using 20 % intralipid that gave μs = 298 mm−¹ at the excitation wavelength. Agar 1% w/v and ICG were added to the phantoms using different fluorophore concentrations of 2 μg/mL, 10 μg/mL, 20 μg/mL, 25 μg/mL, and 40 μg/mL. The objective was to perform controlled measurements of steady state ICG fluorescence, the dynamics of photobleaching at different concentrations, and to find the optimal ICG concentration for obtaining the maximum fluorescence intensity. The light to excite ICG fluorescence emission was provided by using a laser output power of 10.4 mW and 200 ms of integration time in the spectrometer for optimal measurements.Measurements using the different gel phantoms showed maximum fluorescence ICG concentration to be between 16 μg/mL and 20 μg/mL. Moreover, photobleaching measurements showed to be ICG concentration-dependent, where those concentrations higher than the optimal one incrementally photobleached with time after being exposed to light. Higher concentrations presented an incremental photobleaching where they first reached a maximum peak and then the intensity decayed with time. Additionally, laser reflection at 782 nm showed that the reflection increased with time ranging from 130% – 460% as the ICG photobleached to 50% of its initial value. Normalization of ICG by the laser reflection signal was investigated to compensate for the intensity variations due to the measurement parameters including the distance from the light source to the target, and the angle of inclination of the probe. The lowest ICG concentration detectable by the system was 0.05 μg/mL.In conclusion, a LabVIEW hardware control and user interface was developed for controlling the spectrometer and the laser. Several measurements were made using the different phantoms, where the optimal concentration of ICG was estimated. It was shown that ICG fluorescence intensity and photobleaching behavior were dependent on the concentration. The results gave suggestions for future experimental design. / NIRF

This dissertation focuses on the development of computational models and algorithms related to diffuse reflectance spectroscopy. Specifically, this work aims to advance diffuse reflectance spectroscopy to a technique that is capable of measuring depth dependent properties in tissue. First, we introduce the Monte Carlo lookup table (MCLUT) method for extracting optical properties from diffuse reflectance spectra. Next, we extend this method to a two-layer tissue geometry so that it can extract depth dependent properties in tissue. We then develop a computational model that relates photon sampling depth to optical properties and probe geometry. This model can be used to aid in design of application specific diffuse reflectance probes. In order to provide justification for using a two-layer model for extracting tissue properties, we show that the use of a one-layer model can lead to significant errors in the extracted optical properties. Lastly, we use our two-layer MCLUT model and a probe that was designed based on our sampling depth model to extract tissue properties from the skin of 80 subjects at 5 anatomical locations. The results agree with previously published values for skin properties and show that can diffuse reflectance spectroscopy can be used to measured depth dependent properties in tissue. / text

The purpose of this review is to compile preclinical and clinical results on phthalocyanines (Pcs) as photosensitizers (PS) for Photodynamic Therapy (PDT) and contrast agents for fluorescence imaging. Indeed, Pcs are excellent candidates in these fields due to their strong absorbance in the NIR region and high chemical and photo-stability. In particular, this is mostly relevant for their in vivo activation in deeper tissular regions. However, most Pcs present two major limitations, i.e., a strong tendency to aggregate and a low water-solubility. In order to overcome these issues, both chemical tuning and pharmaceutical formulation combined with tumor targeting strategies were applied. These aspects will be developed in this review for the most extensively studied Pcs during the last 25 years, i.e., aluminium-, zinc- and silicon-based Pcs.