Our lab is interested in discovering and applying novel image analysis and image-based computational techniques for improved detection, follow-up and treatment of cancer. Much of our work is highly translational, with direct application to the treatment of patients within the Department of Radiation Oncology.
Segmentation and characterization of vascular tree structure/function is being applied to aid in studying growth of lung vessels in premature infants, and disease progression in adults and children with pulmonary arterial hypertension. Novel 3D tumor detection approaches are being applied for lung, brain and breast screening, and in the future for virtual colonoscopy. Finite element modeling and deformable image registration have been used to quantify the deformation during needle biopsy. Quantitative analysis of CT image changes following radiation treatment is being used to assess radiation damage to healthy tissue surrounding lung tumors with eventual use in assaying new drugs to protect normal tissue from radiation. MR diffusion-weighting imaging and MRI spectroscopy is being used in both patient and animal studies to model microscopic tumor spread for improved treatment of aggressive brain cancers.
Cardiac MRI Tagging can be used to quantitatviely assess regional myocradial function and has traditionally been applied to study changes in
contractility in diseases such as coronary artery occlusion ('heart attack') and in understanding the role of conduction and mechanical activiation.
Applications to cancer include studying the detrimental cardiac effects of chemotherapy and radiation in long-term survivors.