Abstract
Cardiovascular diseases, like Carotid Artery Disease and Coronary
Artery Disease (CAD) are associated with the narrowing of artery
due to build-up of fatty substances and cholesterol deposits (called
plaque). Carotid Artery Disease increases the chances of brain
stroke. Hence, the main objective of this work is to apply computational
tools to help differentiate between the healthy and unhealthy
artery (with 25% stenosis) using a combination of Computational
Fluid Dynamics (CFD) and data mining tools. In this work, first,
the CFD has been qualitatively shown to provide similar results
as the experimental Phase-Contrast Magnetic Resonance Imaging
(PCMRI) technique. The CFD simulation shows that wall shear
stress is an ideal parameter to identify the location of plaque formation
and the existence of plaque conditions in the body (due to overall
higher spatially averaged wall shear stress in the clogged case at
all times in the cycle). Then data mining tools like Fast Fourier
Transform (FFT) and Proper Orthogonal Decomposition (POD)
have been used to unearth a pattern that can be useful for diagnosis.
FFT shows that the flow constriction induced by plaque leads
to lesser variation in magnitudes of energy of dominant frequencies
at different locations like, wake region, mid-Internal Carotid Artery
(mid-ICA) and mid-Common Carotid Artery (mid-CCA) regions,
while for cleaner artery, there is more variation in the magnitude of
energy of these dominant frequencies when measured at wake, mid
ICA and mid CCA region. POD helps by confirming the location
of regions with high energy in decomposed velocity modes for both
the cases. More studies are required to develop a data mining based
modern 21st century cardio-vascular patient care.
Artery Disease (CAD) are associated with the narrowing of artery
due to build-up of fatty substances and cholesterol deposits (called
plaque). Carotid Artery Disease increases the chances of brain
stroke. Hence, the main objective of this work is to apply computational
tools to help differentiate between the healthy and unhealthy
artery (with 25% stenosis) using a combination of Computational
Fluid Dynamics (CFD) and data mining tools. In this work, first,
the CFD has been qualitatively shown to provide similar results
as the experimental Phase-Contrast Magnetic Resonance Imaging
(PCMRI) technique. The CFD simulation shows that wall shear
stress is an ideal parameter to identify the location of plaque formation
and the existence of plaque conditions in the body (due to overall
higher spatially averaged wall shear stress in the clogged case at
all times in the cycle). Then data mining tools like Fast Fourier
Transform (FFT) and Proper Orthogonal Decomposition (POD)
have been used to unearth a pattern that can be useful for diagnosis.
FFT shows that the flow constriction induced by plaque leads
to lesser variation in magnitudes of energy of dominant frequencies
at different locations like, wake region, mid-Internal Carotid Artery
(mid-ICA) and mid-Common Carotid Artery (mid-CCA) regions,
while for cleaner artery, there is more variation in the magnitude of
energy of these dominant frequencies when measured at wake, mid
ICA and mid CCA region. POD helps by confirming the location
of regions with high energy in decomposed velocity modes for both
the cases. More studies are required to develop a data mining based
modern 21st century cardio-vascular patient care.