A computer-aided method for finding an optimal imaging plane for simultaneous measurement of the arterial blood
inflow through the 4 vessels leading blood to the brain by phase contrast magnetic resonance imaging is presented. The
method performance is compared with manual selection by two observers. The skeletons of the 4 vessels for which
centerlines are generated are first extracted. Then, a global direction of the relatively less curved internal carotid arteries
is calculated to determine the main flow direction. This is then used as a reference direction to identify segments of the
vertebral arteries that strongly deviates from the main flow direction. These segments are then used to identify
anatomical landmarks for improved consistency of the imaging plane selection. An optimal imaging plane is then
identified by finding a plane with the smallest error value, which is defined as the sum of the angles between the plane's
normal and the vessel centerline's direction at the location of the intersections. Error values obtained using the
automated and the manual methods were then compared using 9 magnetic resonance angiography (MRA) data sets. The
automated method considerably outperformed the manual selection. The mean error value with the automated method
was significantly lower than the manual method, 0.09±0.07 vs. 0.53±0.45, respectively (p<.0001, Student's t-test).
Reproducibility of repeated measurements was analyzed using Bland and Altman's test, the mean 95% limits of
agreements for the automated and manual method were 0.01~0.02 and 0.43~0.55 respectively.
Intracranial compliance (ICC) determines the ability of the intracranial space to accommodate increase in volume (e.g.,
brain swelling) without a large increase in intracranial pressure (ICP). Therefore, measurement of ICC is potentially
important for diagnosis and guiding treatment of related neurological problems. Modeling based approach uses an
assumed lumped-parameter model of the craniospinal system (CSS) (e.g., RCL circuit), with either the arterial or the
net transcranial blood flow (arterial inflow minus venous outflow) as input and the cranio-spinal cerebrospinal fluid
(CSF) flow as output. The phase difference between the output and input is then often used as a measure of ICC
However, it is not clear whether there is a predetermined relationship between ICC and the phase difference between
these waveforms. A different approach for estimation of ICC has been recently proposed. This approach estimates ICC
from the ratio of the intracranial volume and pressure changes that occur naturally with each heartbeat. The current study
evaluates the sensitivity of the phase-based and the direct approach to changes in ICC. An RLC circuit model of the
cranio-spinal system is used to simulate the cranio-spinal CSF flow for 3 different ICC states using the transcranial
blood flows measured by MRI phase contrast from healthy human subjects. The effect of the increase in the ICC on the
magnitude and phase response is calculated from the system's transfer function. We observed that within the heart rate
frequency range, changes in ICC predominantly affected the amplitude of CSF pulsation and less so the phases. The
compliance is then obtained for the different ICC states using the direct approach. The measures of compliance
calculated using the direct approach demonstrated the highest sensitivity for changes in ICC. This work explains why
phase shift based measure of ICC is less sensitive than amplitude based measures such as the direct approach method.
The spinal canal contributes to the overall compliance of the craniospinal compartment. Thus it plays an important role in the regulation of craniospinal hydrodynamics and intracranial pressure. Limited information is available concerning the spinal canal compliance and its distribution along the spinal canal. Current methods of compliance measurement require injection of fluid into the spinal canal cerebrospinal fluid (CSF) spaces and thus are associated with morbidity. A noninvasive method of deriving the spinal canal compliance and its distribution is being developed. A motion-sensitive Magnetic Resonance Imaging technique is employed to quantify the oscillating CSF flow at several locations along the spinal canal. The differential equations governing CSF flow are derived using Bond Graph methodology. Flow dynamics satisfying the differential equations is then compared iteratively with actual flow measurements to yield spinal canal compliance, and CSF resistance and inertia. The model was validated using CSF flow measurements obtained from 4 healthy volunteers. The model predicted CSF flow was compared with measured CSF flow waveforms at intermediate locations. Compliance values ranged from 1.7 mL/mmHg to 45.2 mL/mmHg. The model further provides new information about the relative contribution sub segments of the canal to the overall spinal canal compliance.
This paper presents a methodology to construct realistic patient-specific computational fluid dynamics models of the circle of Willis (CoW) using magnetic resonance angiography (MRA) data. Anatomical models are reconstructed from MRA images using tubular deformable models along each arterial segment and a surface-merging algorithm. The resulting models are smoothed and used to generate finite element (FE) grids. The incompressible Navier-Stokes equations are solved using a stabilized FE formulation. Physiologic flow conditions are derived from phase-contrast MR velocity measurements. The methodology was tested on image data of a normal volunteer. A pulsatile flow solution was obtained. Measured flow rates were prescribed in the internal carotid arteries, vertebral arteries, middle cerebral arteries and anterior cerebral arteries. Pressure boundary conditions were imposed in the posterior cerebral arteries. Visualizations of the complex flow patterns and wall shear stress distributions were produced. Potential applications of these FE models include: study the role of the communicating arteries during arterial occlusions and after endovsascular interventions, calculate transport of drugs, evaluate accuracy of 1D flow models, and evaluate vascular bed models used to impose boundary conditions when flow data is unavailable or incomplete.
We are developing an automated method for tracking the entire vascular tree in x-ray angiographic images. The vascular tree information which is obtained by our tracking method can be used for automated analyses of angiographic images such as detection and quantitation of vascular lesions identification of regions related to diseased vessels reconstruction of 3D representations from biplane images and analysis of blood flow. Our tracking method incorporates an efficient way of sampling the image data a connectivity test to assure that the tracking follows paths which are within vascular segments and a guided-search method in which information from nearby regions of the image is used to guide the tracking. Our current tracking method is more robust than a previous method and is capable of accurately tracking complex vascular trees which include tortuous vessels.
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