The leading cause of death in the Western World is heart disease and consequently study of normal and pathological heart behavior has become the topic of rigorous research. In particular the study of the shape and motion of the heart is important because many heart diseases are thought to be strongly correlated to the shape and motion of the heart. Important examples of such heart diseases include ischemia and right ventricle (RV) hypertrophy.
The heart is actually two separate pumps: a right heart that pumps the blood through the lungs and a left heart that pumps the blood through the peripheral organs. In turn, each of these “hearts” is a two-chamber pump composed of an atrium and a ventricle. Special mechanisms in the heart provide cardiac rhythm and transmit action potentials throughout the heart muscle to cause the heart’s rhythmic relaxation and contraction, known as diastole and systole respectively.
The understanding of the heart mechanics is crucial in clinical research for diagnosis and patient study. The imaging techniques, such as Magnetic Resonance Imaging (MRI), Ultrasound, CT, X-ray provide noninvasive methods to study internal organs in vivo. Typically 2-D slices are combined to generate a 3-D volumetric model. Furthermore the images taken over time make 4-D (3-D + time) analysis possible. Accurate and expedient interpretation of this data is difficult to achieve. These modalities provide a tremendous amount of data and when presented as 2D images typically require an expert anatomist to interpret. Moreover, comprehensive understanding of diastole and systole is difficult because the heart moves some structures become invisible and then visible again as they move through the image planes acquired by the scanner. Image interpretation is further confounded by motion artifacts from the subject. The cost incurred in manual interpretation of the data by a cardiology/radiology specialist is prohibitive for routine data analysis; however an automated analysis system holds the promise of reducing interpretation costs. It also paves the way for objective, quantitative analysis rather than a subjective, qualitative analysis. In addition an automated analysis system may be used to give evidence of a correlation between particular diseases and the regional changes in the shape and motion of the heart.
This course will cover the most recent efforts by several research groups towards improved data acquisition, data analysis, 3D shape and material modeling, hemodynamics and electrophysiology. In particular our proposed course will cover:
The topics will be covered by various experts in this field carefully selected to address both the underlying mathematics and the related applications. Speakers will include mathematicians, computer scientists, bioengineers and physicians.
(New York University, Radiology)
James Duncan (Yale University, Image Processing and Analysis Group)
Dimitris Metaxas, Chair (Rutgers University, Computer Science)
Jerry Prince (Johns Hopkins University, Image Analysis and Communications Lab)