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A Framework for Visual Comparison of 4D PC-MRI Aortic Blood Flow Data
Short Paper
Four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) allows for the non-invasive acquisition of in-vivo blood flow, producing a patient-specific blood flow model in selected vascular structures, e.g. the aorta. The resulting three-dimensional time-resolved datasets contain flow speed and direction. In the past, many specialized techniques for the visualization and exploration of such datasets have been developed, yet a tool for the visual comparison of multiple datasets is missing. Due to the complexity of the underlying data, a simple side-by-side comparison of two datasets using traditional visualization techniques can only yield coarse results. In addition, differences in segmentation quality and acquired time frame in these datasets require the use of specialized registration techniques in order for a comparison to produce meaningful results. In this paper, we present a toolkit that allows for an efficient and robust registration of different 4D PC-MRI datasets and offers a variety of both qualitative and quantitative comparison techniques. A coarse registration is performed automatically using the patient coordinate system stored within the datasets. Differences in the segmentation and time frame can be amended semi-automatically using landmarks on the vessel centerline and flow curve of the datasets. Afterwards, a set of measures quantifying the difference between the datasets, such as the flow jet displacement or velocity and flow angle difference, is automatically computed. A visual comparison between the 3D vessel models, blood flow path lines and 2D planes at normalized positions is also available. To support the orientation in the spatio-temporal domain of the flow dataset, we additionally provide bulls-eye plots that highlight potentially interesting regions with respect to both the individual characteristics of the datasets as well as their comparison. In an evaluation with three experienced radiologists, we confirmed the usefulness of our technique. With our application, they were able to discover previously unnoticed artifacts occurring in a dataset acquired with an experimental MRI sequence.
A New Vessel Enhancement Transform on Retinal Blood Vessels Segmentation
Short Paper
The automatic detection of retinal blood vessels is a very important task in computer aided-diagnosis of retinal diseases. In this work a new method is proposed for the automated detection of the retinal vessels. Three new and important contributions are made. Firstly, a new method capable of vessel enhancement is presented. Secondly, a new criterium to remove some false vessels caused by the proximity to bright regions is presented, avoiding the false vessels created by the presence of exudates or bright artifacts. Thirdly, a new method that discards the false vessel regions that usually tends to appear in the border of the optic disc. This is achieved using the derivatives of the gradient magnitude local maxima over different scales. The performance evaluation is made on two publicly available databases, namely, STARE and HRF with state-of-the-art results. Particularly, the described method reveals to be very reliable on retinal images with large pathological signs.
Visual Assessment of Vascular Torsion using Ellipse Fitting
Short paper
Blood vessels are well explored and researched in medicine and visualization. However, the investigation of vascular torsion has yet been unexplored. In order to understand the development and current state of a single blood vessel or even multiple connected ones, properties of vascular structures have to be analyzed. In this paper we assess the torsion of blood vessels in order to better understand their morphology. The aim of this work is to quantitatively gauge blood vessels by using an automated method that assumes an elliptical blood vessel model. This facilitates using state-of-the-art ellipse fitting algorithms from industrial measuring standards. In order to remove outliers, we propose a self-correcting iterative refitting of ellipses using neighboring information. The torsion information is visually conveyed by connecting the major and minor points of adjacent ellipses. Our final visualization comprises a visual representation of the blood vessel including four bands to outline the torsion.
Visual Analysis of Regional Anomalies in Myocardial Motion
Full paper
Regional anomalies in the myocardial motion of the left ventricle (LV) are important biomarkers for several cardiac diseases. Myocardial motion can be captured using a velocity-encoded magnetic resonance imaging method called tissue phase mapping (TPM). The acquired data is pre-processed and represented as regional velocities in cylindrical coordinates at three short-axis slices of the left ventricle over one cardiac cycle. We use a spatio-temporal visualization based on a radial layout where the myocardial regions are laid out in an angular pattern similar to the American Heart Association (AHA) model and the temporal dimension increases with increasing radius. To detect anomalies, we compare patient data against the myocardial motion of a cohort of healthy volunteers. For the healthy volunteer cohort, we compute nested envelopes of central regions for the time series of each region and each of the three velocity directions based on the concept of functional boxplots. A quantitative depiction of deviations from the spatio-temporal pattern of healthy heart motion allows for quick detection of regions of interests, which can then be analyzed in more detail by looking at the actual time series. We evaluated our approach in a qualitative user study with imaging and medical experts. The participants appreciated the proposed encoding and considered it a substantial improvement over the current methods.