SUMMARY

Evaluating radioactive aerosol exposure in human airways currently uses generic models assuming uniform particle deposition, omitting variability from realistic Human Respiratory Tract (HRT) geometries. High-fidelity, individualized Particle Deposition Profiles (PDP) can be accomplished by 3D HRT reconstructions using Computed Tomography (CT) scans and Computational Fluid Particle Dynamics (CFPD) simulations. However, these simulations demand substantial computation time and resources, which limits widespread application. Reduced Order Models (ROM) offer a promising approach to delivering individualized, high-fidelity PDP at a reduced computational cost with minimum human intervention and accurate results.

This research work proposes a systematic approach to improve the precision of aerosol deposition modeling within the HRT in a reasonable time and using low computational resources. The methodology begins with the implementation of computer vision algorithms to achieve 3D reconstructions of the HRT, using data from chest and head-neck CT scans. Building upon this methodology, a hybrid automated framework will be developed to produce subject-specific, high-fidelity PDP based on the 3D HRT geometries, using CFPD software. In tandem, a comprehensive database will be created, encompassing a wide range of particle size distributions and subject-specific HRT geometries, factoring in various levels of respiratory activity from rest to heavy exercise. To streamline this complex process, a ROM will be designed to quickly predict these detailed PDPs based on the previously developed database. Finally, the work will leverage these PDPs, using state-of-the-art computational phantoms, to establish a novel framework that integrates particle deposition with radiation transport, facilitated by the Particle and Heavy Ion Transport code System (PHITS).

The anticipated outcome of this research is developing a novel, efficient, and scalable methodology for assessing subject-specific aerosol deposition in the HRT. This advancement will significantly refine the current state-of-the-art in modeling aerosol dynamics within human airways. The work promises to substantially enhance tools utilized in diverse environmental and occupational health contexts, particularly in facilitating personalized health risk assessments in the fields of radiation protection and public health.