Towards an Early Detection of Coronary Artery Bypass Graft Failure
A Computational Fluid Dynamics Approach Based on CT and 4-D flow MRI
Thanks to a 2017 Agfa HealthCare/RSNA Research Scholar Grant, Laura Jimenez-Juan, MD, will investigate the use of a computational fluid dynamics method based on CT angiography and 4-D flow MRI data for the early detection of coronary artery bypass graft (CABG) failure.
“Graft failure is a leading surgical complication of coronary artery bypass graft surgery,” said Dr. Jimenez-Juan, assistant professor, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. “The long-term goal is to develop and translate into clinical practice a non-invasive approach to assess graft hemodynamics that could be used to obtain biomarkers of future graft failure.”
Dr. Jimenez-Juan and her team will investigate the correlation between flow parameters obtained from computational simulations and graft failure 12 months after CABG surgery. Researchers will study the correlation between competitive flow and CABG failure to determine whether a direct estimation of competitive flow correlates to graft failure better than the degree of stenosis.
“If our method is successful, it may result in the improvement of targeted preventive measures to decrease graft failure, including better surgical techniques, intensification of medical therapy, and inclusion of patient-specific quantitative parameters in the clinical follow-up,” Dr. Jimenez-Juan said.
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