Hemodynamic information is increasingly inspected to assess cardiovascular disease. Abnormal blood-flow patterns include high-speed jet flow and regurgitant flow. Such pathological blood-flow patterns are nowadays mostly inspected by means of color Doppler ultrasound imaging. To date, Doppler ultrasound has been the prevailing modality for blood-flow analysis, providing non-invasive and cost-effective blood-flow imaging. Since recent years, magnetic resonance imaging (MRI) is increasingly employed to measure time-resolved blood-flow data. Albeit more expensive, MRI enables volumetric velocity encoding, providing true vector-valued data with less noise. Domain experts in the field of ultrasound and MRI have extensive experience in the interpretation of blood-flow information, although they employ different analysis techniques. We devise a visualization framework that extends on common Doppler ultrasound visualizations, exploiting the added value of MRI velocity data, and aiming for synergy between the domain experts. Our framework enables experts to explore the advantages and disadvantages of the current renditions of their imaging data. Furthermore, it facilitates the transition from conventional Doppler ultrasound images to present-day high-dimensional velocity fields. To this end, we present a virtual probe that enables direct exploration of MRI-acquired blood-flow velocity data using user-friendly interactions. Based on the probe, Doppler ultrasound inspired visualizations convey both in-plane and through-plane blood-flow velocities. In a compound view, these two-dimensional visualizations are linked to state-of-the-art three-dimensional blood-flow visualizations. Additionally, we introduce a novel volume rendering of the blood-flow velocity data that emphasizes anomalous blood-flow patterns. The visualization framework was evaluated by domain experts, and we present their feedback.
KEYWORDS: Signal to noise ratio, Sensors, Point spread functions, Computed tomography, Interference (communication), Error analysis, Stochastic processes, 3D image processing, Medical imaging, Image segmentation
The full-width at half-max (FWHM) criterion is often used for both manual and automatic quantification of the
vessel diameter in medical images. The FWHMcriterion is easy to understand and it can be implemented with low
computational cost. However, it is well known that the FWHM criterion can give an over- and underestimation
of the vessel diameter. In this paper, we propose a simple and original method to create an unbiased estimation
of the vessel diameter based on the FWHM criterion and we compared the robustness to noise of several edge
detectors. The quantitative results of our experiments show that the proposed method is more accurate and
precise than other (more complex) edge detectors, even for small vessels.
A cerebral aneurysm is a persistent localized dilatation of the wall of a cerebral vessel. One of the techniques applied to treat cerebral aneurysms is the Guglielmi detachable coil (GDC) embolization. The goal of this technique is to embolize the aneurysm with a mesh of platinum coils to reduce the risk of aneurysm rupture. However, due to the blood pressure it is possible that the platinum wire is deformed. In this case, re-embolization of the aneurysm is necessary.
The aim of this project is to develop a computer program to estimate the volume of cerebral aneurysms from archived laser hard copies of biplane digital subtraction angiography (DSA) images. Our goal is to determine the influence of the packing percentage, i.e., the ratio between the volume of the aneurysm and the volume of the coil mesh, on the stability of the coil mesh in time. The method we apply to estimate the volume of the cerebral aneurysms is based on the generation of a 3-D geometrical model of the aneurysm from two biplane DSA images. This 3-D model can be seen as an stack of 2-D ellipsis. The volume of the aneurysm is the result of performing a numerical integration of this stack. The program was validated using balloons filled with contrast agent. The availability of 3-D data for some of the aneurysms enabled to perform a comparison of the results of this method with techniques based on 3-D data.
KEYWORDS: Visualization, Cardiovascular magnetic resonance imaging, Heart, Volume rendering, Data modeling, Magnetic resonance imaging, Data acquisition, Tissues, 3D modeling, Cardiovascular system
Cardiac MRI is a technique that provides information about morphology and function of the cardiovascular system in the form of four-dimensional (4D) scalar data sets. Visualization and extraction of clinically relevant parameters from these data sets may help to diagnose cardiac diseases and malfunctions. Some of these parameters are left (right) ventricle volume, ejection fraction, flow measurements, and wall motion and thickening. Although cardiac MRI is a rapidly growing technique, it must overcome several problems (such as poor spatial resolution, flow and motion artifacts, and low signal-to-noise ratio) in order to produce images with sufficient quality to be used in clinical applications. Existing approaches to visualize cardiac MRI data sets in 4D are based on rendering a geometrical model extracted from the data. In most cases, these models are polygon meshes describing the epicardial and endocardial surfaces of the heart. A wide range of different techniques can be found in the literature to achieve this geometrical model extraction. Our approach consists of applying an iso-surface volume rendering technique in order to visualize the data sets. This visualization includes shape visualization and functional mapping. With this technique, the medical data itself is rendered instead of rendering an extracted geometrical model. This technique has been successfully applied to 3D MRI and CT data sets. Even though the extension of this technique to 4D data sets is not straightforward, the preliminary results are very promising.
An algorithm for very accurate visualization of an iso- surface in a 3D medical dataset has been developed in the past few years. This technique is extended in this paper to several kinds of measurements in which exact geometric information of a selected iso-surface is used to derive volume, length, curvature, connectivity and similar geometric information from an object of interest. The actual measurement tool described in this paper is fully interactive. The highly accurate iso-surface volume- rendering algorithm is used to describe the actual measurement that should be performed. For instance, objects for which volumes should be calculated, or paths from which the length should be calculated can be selected at sub-voxel resolution. Ratios of these quantities can be used to automatically detect anomalies in the human body with a high degree of confidence. The actual measurement tool uses a polygon-based algorithm that can distinguish object connectivity at sub-voxel resolution, in exactly the same manner as the iso-surface algorithm. Segmentation based on iso-surfaces geometrical topology can be done at this point. The combination of the iso-surface volume-rendering algorithm and the polygon-based algorithm makes it possible to achieve both visual interaction with the dataset and highly accurate measurements. We believe that the proposed method contributes to the integration of visual and geometric information and is helpful in clinical diagnosis.
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