Theranostics 2015; 5(5):443-455. doi:10.7150/thno.11107

Research Paper

CFD Modeling and Image Analysis of Exhaled Aerosols due to a Growing Bronchial Tumor: towards Non-Invasive Diagnosis and Treatment of Respiratory Obstructive Diseases

Jinxiang Xi1, ✉, JongWon Kim1, Xiuhua A. Si2, Richard A. Corley3, Senthil Kabilan3, Shengyu Wang4,5

1. School of Engineering and Technology, Central Michigan University, Mt Pleasant, MI, 48858, USA.
2. Department of Mechanical Engineering, California Baptist University, Riverside, CA, 92504, USA.
3. Systems Toxicology & Exposure Science, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
4. Department of Pulmonary & Critical Care Medicine, First Affiliated Hospital of Xi'an Medical University, Shaanxi, China 710077.
5. Department of Anesthesiology, Mayo Clinic, Rochester, MN, 55905, USA.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) License. See for full terms and conditions.
Xi J, Kim J, Si XA, Corley RA, Kabilan S, Wang S. CFD Modeling and Image Analysis of Exhaled Aerosols due to a Growing Bronchial Tumor: towards Non-Invasive Diagnosis and Treatment of Respiratory Obstructive Diseases. Theranostics 2015; 5(5):443-455. doi:10.7150/thno.11107. Available from

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Diagnosis and prognosis of tumorigenesis are generally performed with CT, PET, or biopsy. Such methods are accurate, but have the limitations of high cost and posing additional health risks to patients. In this study, we introduce an alternative computer aided diagnostic tool that can locate malignant sites caused by tumorigenesis in a non-invasive and low-cost way. Our hypothesis is that exhaled aerosol distribution is unique to lung structure and is sensitive to airway structure variations. With appropriate approaches, it is possible to locate the disease site, determine the disease severity, and subsequently formulate a targeted drug delivery plan to treat the disease. This study numerically evaluated the feasibility of the proposed breath test in an image-based lung model with varying pathological stages of a bronchial squamous tumor. Large eddy simulations and a Lagrangian tracking approach were used to model respiratory airflows and aerosol dynamics. Respirations of tracer aerosols of 1 µm at a flow rate of 20 L/min were simulated, with the distributions of exhaled aerosols recorded on a filter at the mouth exit. Aerosol patterns were quantified with multiple analytical techniques such as concentration disparity, spatial scanning and fractal analysis. We demonstrated that a growing bronchial tumor induced notable variations in both the airflow and exhaled aerosol distribution. These variations became more apparent with increasing tumor severity. The exhaled aerosols exhibited distinctive pattern parameters such as spatial probability, fractal dimension, and multifractal spectrum. Results of this study show that morphometric measures of the exhaled aerosol pattern can be used to detect and monitor the pathological states of respiratory diseases in the upper airway. The proposed breath test also has the potential to locate the site of the disease, which is critical in developing a personalized, site-specific drug delivery protocol.

Keywords: Aerosol breath test, computer aided diagnosis, theranostics, aerosol fingerprint, fractal dimension, obstructive respiratory diseases.