Biomedical Engineering Reference
In-Depth Information
Fig. 9.3
Lattice Boltmann method for direct numerical simulations of red blood cells in large ves-
sels. (Image adapted from van Wyk et al. 2013).
a
Hematocrit 15 % red blood cell distribution and
velocity cutplane.
b
Hematocrit 5 % red blood cell distribution with platelets
sults based on the system performance and vice versa. For flow validation, there
are well-established velocimetry systems to generate accurate time-resolved vector
fields up to three spatial dimensions. Such flow tracking can be classified as opti-
cal-, magnetic resonance-and ultrasonic-image velocimetry.
Phase-sensitive flow MRI (velocity mapping) (Stahlberg et al. 1995) or velocity-
encoded cine magnetic resonance imaging (VEC-MR) (Hartiala et al. 1993) are
well-established methods for quantifying flow in the cardiovascular system. These
methods form a class of nuclear-based imaging known as magnetic resonance im-
aging velocimetry (MRIV). Optical-based imaging using particle image velocim-
etry (PIV) provides a validation tool for verifying MRIV (Markl et al. 2003b; Elkins
et al. 2004). Technically, phase contrast MRI can also be compared with ultrasonic
imaging in various studies (Jung et al. 2004; Seitz et al. 2006).
9.3.2
Imaging for Flow Analysis
Diagnosis of heart conditions can help to save lives. For this purpose, a concatena-
tion of medical imaging systems and software for extracting medical information
and deciphering can be specifically directed at developing and investigating a novel
framework for post-processing medical images.
High-resolution imaging techniques have been used to demonstrate the loca-
tion and appearance of atherosclerosis in blood vessels. These imaging technologies
include intravascular ultrasound, multi-detector CT and MRI. We can use patient-
specific geometries for the three-dimensional computational models in CFD simu-
lations. Obtaining real-time CFD solutions to biomedical flows in human vascular
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