Improved mathematical and computational tools for modeling photon propagation in tissue

Thesis (Ph.D.)--Boston UniversityLight interacts with biological tissue through two predominant mechanisms: scattering and absorption, which are sensitive to the size and density of cellular organelles, and to biochemical composition (ex. hemoglobin), respectively. During the progression of disease, tissues undergo a predictable set of changes in cell morphology and vascularization, which directly affect their scattering and absorption properties. Hence, quantification of these optical property differences can be used to identify the physiological biomarkers of disease with interest often focused on cancer. Diffuse reflectance spectroscopy is a diagnostic tool, wherein broadband visible light is transmitted through a fiber optic probe into a turbid medium, and after propagating through the sample, a fraction of the light is collected at the surface as reflectance. The measured reflectance spectrum can be analyzed with appropriate mathematical models to extract the optical properties of the tissue, and from these, a set of physiological properties. A number of models have been developed for this purpose using a variety of approaches -- from diffusion theory, to computational simulations, and empirical observations. However, these models are generally limited to narrow ranges of tissue and probe geometries. In this thesis, reflectance models were developed for a much wider range of measurement parameters, and influences such as the scattering phase function and probe design were investigated rigorously for the first time. The results provide a comprehensive understanding of the factors that influence reflectance, with novel insights that, in some cases, challenge current assumptions in the field. An improved Monte Carlo simulation program, designed to run on a graphics processing unit (GPU), was built to simulate the data used in the development of the reflectance models. Rigorous error analysis was performed to identify how inaccuracies in modeling assumptions can be expected to affect the accuracy of extracted optical property values from experimentallyacquired reflectance spectra. From this analysis, probe geometries that offer the best robustness against error in estimation of physiological properties from tissue, are presented. Finally, several in vivo studies demonstrating the use of reflectance spectroscopy for both research and clinical applications are presented

A MEMS BASED MICROWAVE PIXEL FOR UWB RADAR BASED 3-D DIAGNOSTIC IMAGING

A MEMS-based microwave Pixel has been developed for use with an Ultra-wideband (UWB) radar probe for high-resolution 3-D non-contact, non-ionizing tomographic diagnostic imaging of the thorax. In the proposed system, an UWB radar transmits a 400 ps duration pulse in the frequency range of 3.1 GHz to 5.1 GHz. The transmitted pulse penetrates through the tissues and is partially reflected at each tissue interface characterized by a complex permittivity change. A suitable microwave lens focuses the reflected wavefront on a 2-D array of MEMS-based microwave Pixels to illuminate each Pixel to a tiny 2-D section of the reflected wavefront. Each Pixel with a footprint area of 595 x 595 μm2 is designed to have 144 parallel connected microfabricated inductors, each with an inductance of 12.439 nH, and a single 150 μm×150 μm microfabricated deformable diaphragm based variable capacitor to generate a voltage which is the dielectric signature of the respective tissue section. A 2-D array of such Pixels can be used to generate a voltage map that corresponds to the dielectric property distribution of the target area. The high dielectric contrast between the healthy and diseased tissues, enable a high precision diagnostics of medical conditions in a non-invasive non-contact manner. This thesis presents the analytical design, 3-D finite element simulation results, and a fabrication process to realize the proposed microwave imaging Pixel. The proposed Pixel with total inductance of 86.329 pH and capacitance tuning range of 1.68:1, achieved a sensitivity of 4.5 aF/0.8 μA.m-1 to generate tomographic coronal imaging slices of human thorax deep upto 4.2 cm enabling a theoritical lateral resolution of 0.59 mm

Computational Framework For Neuro-Optics Simulation And Deep Learning Denoising

The application of machine learning techniques in microscopic image restoration has shown superior performance. However, the development of such techniques has been hindered by the demand for large datasets and the lack of ground truth. To address these challenges, this study introduces a computer simulation model that accurately captures the neural anatomic volume, fluorescence light transportation within the tissue volume, and the photon collection process of microscopic imaging sensors. The primary goal of this simulation is to generate realistic image data for training and validating machine learning models. One notable aspect of this study is the incorporation of a machine learning denoiser into the simulation, which accelerates the computational efficiency of the entire process. By reducing noise levels in the generated images, the denoiser significantly enhances the simulation\u27s performance, allowing for faster and more accurate modeling and analysis of microscopy images. This approach addresses the limitations of data availability and ground truth annotation, offering a practical and efficient solution for microscopic image restoration. The integration of a machine learning denoiser within the simulation significantly accelerates the overall simulation process, while improving the quality of the generated images. This advancement opens new possibilities for training and validating machine learning models in microscopic image restoration, overcoming the challenges of large datasets and the lack of ground truth