Collaborative Research: Inverse-Consistent Determination of Effective Young's Modulus for Human Lung

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The research objective of this award is to use a combination of computational tools, radiological imaging and image processing methods to determine consistent effective elastic properties of human lung in a non-invasive manner. Such data, which do not currently exist, will enable accurate simulation of lung dynamics for targeted radiotherapy. Studies conducted under this award will reproduce lung geometry from computed tomography imaging, simulate by means of computational and image processing tools the airflow and spatial deformation of the lungs, and iteratively estimate the effective tissue elasticity for known values of airflow and deformation predicted using the computational tool. The estimated tissue elastic properties will be validated by comparison with manual landmark tracking established by a clinical expert.This research, if successful, will significantly improve knowledge base on biomechanical properties of human tissue structures, and establish a property-performance relationship between the tissues and a wide range of clinical procedures. The approach could be used in a radiotherapy clinical workflow designed to track tumor motion and develop improved radiotherapy treatment that ultimately improves patient's treatment outcome and reduces overall cost. The knowledge gained from this study could be extended to other anatomical sites including biomechanical stress-induced plaque rupture in cardiovascular disease, estimation of cardiac tissue elasticity, and to address variations in imaging modality. This award is multidisciplinary, involving collaboration among scientists, engineers and clinicians at three institutions. The education plan focuses on nurturing the next generation of scientists and engineers by providing them with an opportunity to work on challenging problems of practical importance across disciplines. Undergraduate and graduate students will be engaged in the research, and will benefit from integration of research findings into classroom instruction and the multidisciplinary training in diverse fields.