Multiscale Quantification of 3D LV geometry from CT

DESCRIPTION (provided by applicant): Traditional cardiac imaging methods have only allowed us to focus on gross features of LV size and shape, due to lack of resolution (e.g., MR and ultrasound). However, heart disease affects the walls and the wall structure is complex (e.g., trabeculae and papillary muscles). These structures are usually ignored, due to lack of resolution, in cardiac shape analysis and cardiac functional assessment. Thus, no one has yet studied the functional and geometric changes between normal and diseased patients at a finer scale than simple gross measures, such as overall ventricular size and wall thickness. Recent developments in multidetector CT technology allow the acquisition of high resolution synchronized cardiac CT data within a breathhold. These new data allows us to assess the structure of the ventricles at an unprecedented level of detail. Given the above new developments, we propose to develop novel computational methods to characterize and quantify the geometry of the LV at levels which were not previously possible. These tools will be based on already collected data at NYU by Dr. Axel's (co-investigator) group. In particular, the specific aims of this proposal are as follows: 1) Develop tools for segmentation and visualization in 3D of the LV structure, including papillary muscles, trabeculae and valve attachments, at different phases of the cardiac cycle, 2) Develop tools for quantification of the architecture of the above structures and statistical analysis on their variations, 3) Perform an initial pilot assessment on normal subjects and some patients with high blood pressure. We will look at the effects of high blood pressure on both the overall structure and the detailed structure;Dr. Axel will provide the groundtruth and Dr. Madigan will provide the statistical analysis, and 4) Development of a database from our analysis results which will be made publicly available, and will be based on the ITK software toolkit (www.itk.org), to which the PI has already contributed many segmentation algorithms.