Rapid and accurate determination of surgical margin depth in fluorescence guided surgery has been a difficult issue to overcome, leading to... Show moreRapid and accurate determination of surgical margin depth in fluorescence guided surgery has been a difficult issue to overcome, leading to over- or under-resection of cancerous tissues and follow-up treatments such as ‘call-back’ surgery and chemotherapy. Current techniques utilizing direct measurement of tumor margins in frozen section pathology are slow, which can prevent surgeons from acting on information before a patient is sent home. Other fluorescence techniques require the measurement of margins via captured images that are overlayed with fluorescent data. This method is flawed, as measuring depth from captured images loses spatial information. Intensity-based fluorescence techniques utilizing tumor-to-background ratios do not decouple the effects of concentration from the depth information acquired. Thus, it is necessary to perform an objective measurement to determine depths of surgical margins. This thesis focuses on the theory, device design, simulation development, and overall viability of time-domain fluorescence imaging as an alternative method of determining surgical margin depths. Characteristic regressions were generated using a thresholding method on acquired time-domain fluorescence signals, which were used to convert time-domain data to a depth value. These were applied to an image space to generate a depth map of a modelled tissue sample. All modeling was performed on homogeneous media using Monte Carlo simulations, providing high accuracy at the cost of increased computational time. In practice, the imaging process should be completed within a span of under 20 minutes for a full tissue sample, rather than 20 minutes for a single slice of the sample. This thesis also explores the effects of different thresholding levels on the accuracy of depth determination, as well as the precautions to be taken regarding hardware limitations and signal noise. Show less
