3 credits
Fall 2026 Lecture Upper Division
Image-based computational hemodynamics is a newly-emerged computational technique for non-invasive and patient-specific assessment of cardiovascular diseases based on medical imaging data. In this course, students will learn (1) concepts and principles of cardiovascular circulation in the human body and imaging modalities for cardiovascular diseases; (2) image-based computational modeling methods for quantification of hemodynamics (velocity, pressure, and wall-shear stress) in human vessels based on CT/MRI and Doppler ultrasound imaging data; and (3) computational analysis to assess the severity of cardiovascular diseases. Team projects to non-invasively assess the severity of arterial stenosis in renal, iliac, and coronary arteries via quantification of trans-stenotic pressure gradient and/or fractional flow reserve will provide first-hand experience of how computational modeling and analysis can contribute to medical innovation and advanced precision medicine.
Learning Outcomes1Correlate hemodynamic abnormalities and cardiovascular diseases.
2Construct models of blood flows and boundary conditions based on vascular anatomy and hemodynamic physiology.
3Solve Navier-Stokes equations for Womersley flows based on pulsatile nature of blood flows.
4Assemble 3-D reconstructions of vessels from CT/MRI imaging data implementing appropriate modeling methods.
5Critique image-based computational hemodynamics using open sources.
6Formulate non-invasive assessments of the severity of arterial stenosis based on patient-imaging data.
7Investigate the important role of computational modeling and analysis in modern clinical practice for non-invasive and patient-specific assessment of cardiovascular diseases.
made by Purdue students.