Quantitative CT Image Analysis for Emphysema Lung Treatment Planning

Date: 
Wednesday, February 25, 2009 - 17:30
Location: 
TH 331
Presenter: 
Matthew Brown, PhD, UCLA School of Medicine
Abstract: 
Emphysema is a disease that causes destruction of lung tissue and an overall reduction in lung compliance as air becomes trapped in the diseased regions. Emphysema affects an estimated 60 million people worldwide and is a major cause of mortality and morbidity. New biomedical valves and stents are being developed that can be implanted in target airways to improve airflow and lung function. These minimally-invasive techniques have the potential to greatly improve the quality of life for emphysema patients, but are dependant on the accurate identification of diseased lung lobes for treatment. A new role for radiology and computer-aided image analysis systems is being pioneered in the treatment of emphysema. Computed tomography (CT) imaging is being used to perform regional assessment of the lung, and an image analysis system to make decisions on which airway should be targeted with a valve or stent for maximal improvement in the patient’s lung function. The computer system quantitatively assesses the degree of emphysema present in the lobes of the lungs to determine which is most severely affected and then computes the optimal airway location to enable air to flow out of this compromised lobe. The system also provides airway measurements (branch lengths and diameters) to assist in determining the best site for stent or valve placement. The treating physician acts directly based on the computer output when placing the device. Thus the computer analysis is critical to the success of this treatment.
Bio: 

After receiving his doctorate in Computer Science from the University of New South Wales in his native Australia, Dr. Matthew Brown joined the UCLA Department of Radiological Sciences Faculty where he is currently an Associate Professor in the Section of Thoracic Imaging. Dr. Brown’s research focuses include computer-aided diagnosis and computer vision.