Quantitative analysis of the lungs on computed tomography.

摘要

Computed tomography (CT) reveals detailed anatomic structure and plays a key role in the diagnosis of many lung diseases. The current gold standard for evaluation of CT is visual assessment, which is limited by high inter-observer variation. The overall goal of proposed research is to develop and validate quantitative methods for evaluating anatomic structure and the appearance of diseases such as cystic fibrosis (CF) and idiopathic pulmonary fibrosis (IPF) on CT of the lungs. The general approach begins with systematic feature extraction, for example geometric measurements or quantitative description of image characteristics like texture. Accumulation of feature data into a training set makes it possible to apply statistical learning methods that can help identify correlation between specific CT features and other metrics describing normal processes or disease states. Project hypotheses are that quantitative image analysis can be used to: (1) distinguish normal lung from characteristic patterns associated with IPF based on texture, (2) facilitate automated, texture-based quantification of extent of fibrosis in IPF and (3) identify group differences in airway morphology using statistical shape modeling.