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Objective: Accurate and high-quality reconstructions of vascular structures are essential for vascular disease diagnosis and blood flow simulations.These applications necessitate a trade-off between accuracy and smoothness. An additional requirement for the volume grid generation for Computational Fluid Dynamics (CFD) simulations is a high triangle quality. We propose a method that produces an accurate reconstruction of the vessel surface with satisfactory surface quality. Methods: A point cloud representing the vascular boundary is generated based on a segmentation result. Thin vessels are subsampled to enable an accurate reconstruction. A signed distance field is generated using Multi-level Partition of Unity Implicits and subsequently polygonized using a surface tracking approach. To guarantee a high triangle quality, the surface is remeshed. Results: Compared to other methods, our approach represents a good trade-off between accuracy and smoothness. For the tested data, the average surface deviation to the segmentation results is 0.19 voxel diagonals and the maximum equi-angle skewness values are below 0.75. Conclusions: The generated surfaces are considerably more accurate than those obtained using model-based approaches. Compared to other model-free approaches, the proposed method produces smoother results and thus better supports the perception and interpretation of the vascular topology.Moreover, the triangle quality of the generated surfaces is suitable for CFD simulations.

Liver disease and hepatocellular carcinoma (HCC) have become a global health burden. For this reason, the determination of liver function plays a central role in the monitoring of patients with chronic liver disease or HCC. Furthermore, assessment of liver function is important, e.g., before surgery to prevent liver failure after hepatectomy or to monitor the course of treatment. Liver function and disease severity are usually assessed clinically based on clinical symptoms, biopsy, and blood parameters. These are rather static tests that reflect the current state of the liver without considering changes in liver function. With the development of liver-specific contrast agents for MRI, noninvasive dynamic determination of liver function based on signal intensity or using T1 relaxometry has become possible. The advantage of this imaging modality is that it provides additional information about the vascular structure, anatomy, and heterogeneous distribution of liver function. In this review, we summarized and discussed the results published in recent years on this technique. Indeed, recent data show that the T1 reduction rate seems to be the most appropriate value for determining liver function by MRI. Furthermore, attention has been paid to the development of automated tools for image analysis in order to uncover the steps necessary to obtain a complete process flow from image segmentation to image registration to image analysis. In conclusion, the published data show that liver function values obtained from contrast-enhanced MRI images correlate significantly with the global liver function parameters, making it possible to obtain both functional and anatomic information with a single modality.

Abstract. We describe scripting facilities to create medical animations for intervention planning based on medical volume data and derived segmentation information. A data independent scripting language has been developed to separate animation scripts from imaging data. The scripting facilities are adaptive and allow to reuse one script to create animations for many different patients. With expressive animations, we support the individual planning process, the preoperative documentation as well as discussions between medical doctors, for example in a tumor board. We also discuss the enhancement of interactive explorations with animations generated on the fly.