Simulation of digital diaphanoscopy signal changes in an improved cross-sectional model (Conference Presentation)

The digital diaphanoscopy method seems promising for solving one of the urgent problems of modern otolaryngology, which is associated with providing accurate, painless and timely diagnosis of pathologies of the maxillary sinuses. Optical properties of the study area and their changes for different anatomical and gender features and pathologies presence determine the results of digital diaphanoscopy. Adjusting the parameters of the probing and measuring parts of digital diaphanoscopy devices taking into account these factors is important task to obtain similar light scattering patterns for different patients and the possibility of their subsequent comparison. This paper presents the improved numerical model of the maxillary sinuses. The developed model considered the maxillary sinuses sizes, their location and asymmetry, various thicknesses of bone and soft tissues, size and localization of pathology (cystic fluid and tumor). The cross-sectional face model was more detailed and considered the curvature of the face. Simulation was carried out at various positioning of radiation source relative to the study area and detector. Additionally, the various rotation angles of the study area with radiation source relative to the detector were considered. The attenuation of probe radiation intensity at the detector was estimated using the Monte Carlo method in the TracePro (Lambda Software) for visible and near-IR wavelengths at the different intensity values. The correlations between model signal and anatomical features of the study area, and the changes of the study area position were identified. It was established that registration of light scattering patterns at different position is important parameter for more accurate evaluation of the maxillary sinuses state and the localization of pathologies. The threshold values of the probe radiation intensity and the optimal study positions, which provide the optimal signal-to-noise ratio of the detectable signals, were identify.