Mathematical Models of Light Transport in Biological Tissues for Quantitative Clinical Diagnostic Applications.

Abstract

This dissertation focuses on the development and implementation of several novel mathematical models of light transport in biological tissue for use as quantitative diagnostic tools to assess tissue viability and detect diseased tissue. This work includes semi-empirical models of reflectance and fluorescence for pancreatic cancer diagnostics, computational models of inelastic (Raman) scattering in layered tissues for non-invasive bone tissue assessment and breast tumor margin detection during surgery, and computational models of light propagation for tissues with irregular geometries. A novel photon-tissue interaction (PTI) model of reflectance and fluorescence was developed and employed to extract biophysically-relevant tissue parameters (mean size of cell nuclei, percentage contribution of collagen to fluorescence) from measured optical spectra of freshly-excised human pancreatic tissues. The mean cellular nuclear size was statistically significant for distinguishing adenocarcinoma sites from non-cancerous (pancreatitis and normal) sites. The percentage contribution of collagen was statistically significant for distinguishing between all three tissue types included in the study (adenocarcinoma, pancreatitis, normal). When these parameters were included in a statistically-rigorous tissue classification algorithm that accounted for intra-patient correlations in the data, adenocarcinoma was distinguished from the non-cancerous tissues with an area of 0.906 under the receiver operating characteristic (ROC) curve and a sensitivity, specificity, positive predictive value, and negative predictive value of 87.5%, 89.0%, 77.8%, and 94.2%, respectively. A novel Monte Carlo (MC) model of inelastic (Raman) scattering in layered tissues was developed and employed to characterize the effects of tissue and fiber-probe properties on the detected Raman signal. This MC model was employed to assist with two biomedical applications: bone tissue diagnostics and breast tumor margin assessment. For the tumor margin assessment application, it was predicted that the smallest detectable tumor thickness using spatially-offset Raman spectroscopy would be 100 microns under a 0.5 mm margin or 1 mm under a 2 mm margin. The models described in this dissertation provide accurate, versatile, and quantitative analysis of the effects of fiber-optic probe design and biophysical tissue properties on the detected optical signal and can be employed in a wide range of tissue diagnostic applications.Ph.D.Applied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91513/1/roberthw_1.pd