The accurate assessment of the presence and extent of vascular disease, and planning of vascular interventions based on MRA requires the determination of vessel dimensions. The current standard is based on measuring vessel diameters on maximum intensity projections (MIPs) using calipers. In order to increase the accuracy and reproducibility of the method, automated analysis of the 3D MR data is required. A novel method for automatically determining the trajectory of the vessel of interest, the luminal boundaries, and subsequent the vessel dimensions is presented. The automated segmentation in 3D uses deformable models, combined with knowledge of the acquisition protocol. The trajectory determination was tested on 20 in vivo studies of the abdomen and legs. In 93% the detected trajectory followed the vessel. Determination of the vessel morphology along a vessel segment is important in grading the presence and extent of possible vascular stenoses. Until recently, most examinations of vascular stenoses were carried out using x-ray angiography (XA). This technology has been regarded as the gold standard in the evaluation of stenoses. However, several problems are associated with this imaging modality. First, since XA is a projection technique, overprojection of vessels can occur even if the view angle is set optimally. Second, an ionizing nephrotoxic contrast agent has to be administered by means of a catheter, a technique that is associated with a definite (although relatively small) morbidity and mortality risk. Finally, the patient and the personnel of the catheterization laboratory are exposed to x-ray radiation.Magnetic resonance angiography (MRA), on the other hand, is a technique that produces three-dimensional (3D) images. Consequently, there is no risk of overprojection of vessels. MRA can be applied without the use of a contrast agent, although the signal-to-noise ratio (SNR) increases if a contrast agent is used. This contrast agent is normally administered intravenously, which presents minimal risks to the patient (1,2).MRA data sets are generally evaluated on 2D maximum intensity projections (MIP); however, it is known that this leads to underestimation of the vessel width, and a decreased SNR. The interpretation is carried out by either visual inspection or by caliper measurements (3-7).To improve the conventional analysis of MRA, it would be desirable to obtain quantitative morphological information directly from the 3D images and not from the projections. To accomplish this, accurate 3D segmentation tools are required.Vessel segmentation of 3D images has been investigated by many researchers (8 -16). However, the majority of this research focused on enhancing the 3D visualization of the vascular structures in the image, and not on accurate quantification of these structures.In this work a novel approach for quantitative vessel analysis of MRA images is introduced and validated. The approach uses knowledge about the image acquisition procedure to accurately determine the vessel boundaries. The techniq...

The aim of this study was to assess the effect of differences in acquisition technique on whole-brain apparent diffusion coefficient (ADC) histogram parameters, as well as to assess scan-rescan reproducibility. Diffusion-weighted imaging (DWI) was performed in 7 healthy subjects with b-values 0-800, 0-1000, and 0-1500 s/mm(2) and fluid-attenuated inversion recovery (FLAIR) DWI with b-values 0-1000 s/mm(2). All sequences were repeated with and without repositioning. The peak location, peak height, and mean ADC of the ADC histograms and mean ADC of a region of interest (ROI) in the white matter were compared using paired-sample t tests. Scan-rescan reproducibility was assessed using paired-sample t tests, and repeatability coefficients were reported. With increasing maximum b-values, ADC histograms shifted to lower values, with an increase in peak height ( p<0.01). With FLAIR DWI, the ADC histogram shifted to lower values with a significantly higher, narrower peak ( p<0.01), although the ROI mean ADC showed no significant differences. For scan-rescan reproducibility, no significant differences were observed. Different DWI pulse sequences give rise to different ADC histograms. With a given pulse sequence, however, ADC histogram analysis is a robust and reproducible technique. Using FLAIR DWI, the partial-voluming effect of cerebrospinal fluid, and thus its confounding effect on histogram analyses, can be reduced.

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Abstract. This paper describes a new method to segment vascular structures in 3D MRA data, based on the Wavefront Propagation algorithm. The center lumen line and the vessel boundary are detected automatically. Our 3D visualization and interaction platform will be prestended, which is used to aid the phycisian in the analysis of the MRA data. The results are compared to conventional X-ray DSA which is considered the current gold-standard. Provided that the diameter of the vessel is larger than 3 voxels, our method has similar result as X-ray DSA.

We introduce a complete problem solving environment designed for pulsatile flows in 3D complex geometries, especially arteries. Three-dimensional images from arteries, obtained from e.g. Magnetic Resonance Imaging, are segmented to obtain a geometrical description of the arteries of interest. This segmented artery is prepared for blood flow simulations in a 3D editing tool, allowing to define in-and outlets, to filter and crop part of the artery, to add certain structures (e.g. a bypass , or stents), and to generate computational meshes as input to the blood flow simulators. Using dedicated fluid flow solvers the time dependent blood flow in the artery during one systole is computed. The resulting flow, pressure and shear stress fields are then analyzed using a number of visualization techniques. The whole environment can be operated from a desktop virtual reality system, and is embedded in a Grid computing environment.

Wall Shear Stress is a key factor in the development of atherosclerosis. To assess the WSS in-vivo, velocity encoded MRI is combined with geometry measurements by 3D MR-Angiography (MRA) and with blood flow calculations using the Finite Element Method (FEM). The 3D geometry extracted from the MRA data was converted to a mesh suitable for FEM calculations. Aiming at in-vivo studies the goal of t,his study was to quantify the differences between FEM calculations and MRI measurements. Two phantoms, a curved tube and a carotid bifurcation model were used. The geometry and the time-dependent flow-rate (measured by MRI) formed input for the FEhl calculations. For good data quality, 2D velocity profiles were analyzed further by the Kolmogorov-Smirnov method. For the curved tube calculations and measurements matched well (p r o b~~ approximately above 0.20). The carotid needs further invest'igation in segmentation and simulation to obtain similar results. It can be concluded that the error-analysis performs reliably.

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