Investigation of light propagation and detection in human head under healthy and clinical settings

Abstract

Near infrared spectroscopy (NIRS) is a neuroimaging modality that allows investigation of brain tissue oxygenation non-invasively. It is widely used to measure changes in the concentration of oxy-hemoglobin and deoxy-hemoglobin in tissue. Infrared light emitted from a source placed over scalp propagates through the tissue and eventually part of it is back-scattered and can be collected by a photodetector. The attenuated light received at the detector encodes the information about brain activity as a consequence of absorption and scattering dominated light tissue interaction. Understanding and modeling light tissue interaction is critical for developing next generation NIRS systems. Several photon migration models have been proposed to investigate light tissue interaction through computerized Monte Carlo (MC) simulations. Using these, a set of NIRS system parameters have already been explored, such as wavelength selection, source-detector separation (SDS), depth of penetration, and effect of layers' thickness. Among those simulation studies, most have not declared the detector or fiber size clearly, also the selection of core system parameters remains controversial, like SD separation. More importantly, all these studies were performed only under healthy settings, no clinical conditions were taken into consideration. With numerous applications of NIRS technology in the assessment of brain function under various clinical conditions caused by traumatic brain injury (TBI) or stroke indicate the importance of study and evaluation of light tissue interaction under such conditions. In this thesis, we developed a reconfigurable and adaptive digital head model for healthy and clinical conditions that can be used to study diverse NIRS parameters for optimization. The thesis provided several novel contributions to the knowledge base that can further optical neuroimaging research applications, technology and algorithm development. First, it investigated new sensor parameters within digital head phantom, such as detector surface area and SDS, which are potential sources of systematic error in calculating hemoglobin concentrations. Secondly, several clinical conditions such as cerebral hematoma and edema development were modeled in silico, their effect on optical parameters and NIRS measurements were demonstrated with modeling for the first time. Such modeling and evaluation of neurological conditions and their effect on optical parameters and measurements can further help in the development of advanced algorithms for NIRS to provide more accurate hematoma and edema detection. Furthermore, virtual measurements from MC simulation on head models for different age groups were extracted and compared to actual measurements on equivalent physical models. The findings of this research can be used to optimize NIRS sensors and provide guidance for the design of next generation optical brain imaging systems for the monitoring of brain activity under healthy and clinical conditions.Ph.D., Biomedical Engineering -- Drexel University, 201