Shrinking tube mesh: combined mesh generation and smoothing for pathologic vessels

We present a mesh generation algorithm which is able to produce smooth meshes from point clouds derived from histological slices. In this work, the shrinking tube mesh generation is used on histologic images depicting pathologic vessels. Our mesh generation is modeled after the behaviour of a shrinking tube. A start shape is fitted iteratively to the point cloud. The presented algorithm was successfully used to generate meshes of the inner and outer contour from vessels in histologic images. While histologic slices have a high in-plane resolution, the large slice distance and deformations during tissue deformations are challenging for 3D model generation

Floor Map Visualizations of Medical Volume Data

Typically, volumetric medical image data is examined by assessing each slice of an image stack individually. However, this enables observers to assess in-plane spatial relationships between anatomical structures only and requires them to keep track of relationships along the third anatomical plane mentally. Therefore, visualization techniques are researched to support this task by depicting spatial information along the third plane, but they can introduce a high degree of abstraction. To overcome this, we present a novel approach that transforms image stacks with labeled anatomical structures into maps with a three-dimensional layout, namely floor maps. Since this approach increases the visual complexity under certain conditions, some clinical application scenarios, e. g. diagnosis and therapy planning, probably will not benefit. Thus, the approach is mainly aimed to support student training and the generation of clinical reports. We also discuss how to enhance the slice-based exploration of medical image stacks via floor maps and present the results of an informal evaluation with three trained anatomists

Distance and force visualisations for improved simulation of intracranial aneurysm clipping

Purpose!#!The treatment of cerebral aneurysms shifted from microsurgical to endovascular therapy. But for some difficult aneurysm configurations, e.g. wide neck aneurysms, microsurgical clipping is better suited. From this combination of limited interventions and the complexity of these cases, the need for improved training possibilities for young neurosurgeons arises.!##!Method!#!We designed and implemented a clipping simulation that requires only a monoscopic display, mouse and keyboard. After a virtual craniotomy, the user can apply a clip at the aneurysm which is deformed based on a mass-spring model. Additionally, concepts for visualising distances as well as force were implemented. The distance visualisations aim to enhance spatial relations, improving the navigation of the clip. The force visualisations display the force acting on the vessel surface by the applied clip. The developed concepts include colour maps and visualisations based on rays, single objects and glyphs.!##!Results!#!The concepts were quantitatively evaluated via an online survey and qualitatively evaluated by a neurosurgeon. Regarding force visualisations, a colour map is the most appropriate concept. The necessity of distance visualisations became apparent, as the expert was unable to estimate distances and to properly navigate the clip. The distance rays were the only concept supporting the navigation appropriately.!##!Conclusion!#!The easily accessible surgical training simulation for aneurysm clipping benefits from a visualisation of distances and simulated forces

Automatic stent and catheter marker detection in X-ray fluoroscopy using adaptive thresholding and classification

In this study, we propose a method for marker detection in X-ray fluoroscopy sequences based on adaptive thresholding and classification. Adaptive thresholding yields multiple marker candidates. To remove non-marker areas, 24 specific features are extracted from each extracted patch and four supervised classifiers are trained to differentiate non-marker areas from marker areas. Quantitative evaluation was carried out to assess different classifier performance by calculating accuracy, sensitivity, specificity and precision. SVM outperforms other classifiers based on the mean value for accuracy, specificity and precision with 81.56, 91.94 and 84.21%, respectively

Wall enhancement segmentation for intracranial aneurysm

We present a tool for automatic segmentation of wall enhancement of intracranial aneurysms in black blood MRI. The results of the automatic segmentation with several configurations is compared to manual expert segmentations. While the manual segmentation includes some voxels of lower intensity not present in the automatic segmentation, overall the volume of the automatic segmentation is higher

Complex wall modeling for hemodynamic simulations of intracranial aneurysms based on histologic images

Purpose For the evaluation and rupture risk assessment of intracranial aneurysms, clinical, morphological and hemodynamic parameters are analyzed. The reliability of intracranial hemodynamic simulations strongly depends on the underlying models. Due to the missing information about the intracranial vessel wall, the patient-specific wall thickness is often neglected as well as the specific physiological and pathological properties of the vessel wall. Methods In this work, we present a model for structural simulations with patient-specific wall thickness including different tissue types based on postmortem histologic image data. Images of histologic 2D slices from intracranial aneurysms were manually segmented in nine tissue classes. After virtual inflation, they were combined into 3D models. This approach yields multiple 3D models of the inner and outer wall and different tissue parts as a prerequisite for subsequent simulations. Result We presented a pipeline to generate 3D models of aneurysms with respect to the different tissue textures occurring in the wall. First experiments show that including the variance of the tissue in the structural simulation affect the simulation result. Especially at the interfaces between neighboring tissue classes, the larger influence of stiffer components on the stability equilibrium became obvious. Conclusion The presented approach enables the creation of a geometric model with differentiated wall tissue. This information can be used for different applications, like hemodynamic simulations, to increase the modeling accuracy.Peer reviewe

Self-calibration of C-arm imaging system using interventional instruments during an intracranial biplane angiography

PURPOSE: To create an accurate 3D reconstruction of the vascular trees, it is necessary to know the exact geometrical parameters of the angiographic imaging system. Many previous studies used vascular structures to estimate the system’s exact geometry. However, utilizing interventional devices and their relative features may be less challenging, as they are unique in different views. We present a semi-automatic self-calibration approach considering the markers attached to the interventional instruments to estimate the accurate geometry of a biplane X-ray angiography system for neuroradiologic use. METHODS: A novel approach is proposed to detect and segment the markers using machine learning classification, a combination of support vector machine and boosted tree. Then, these markers are considered as reference points to optimize the acquisition geometry iteratively. RESULTS: The method is evaluated on four clinical datasets and three pairs of phantom angiograms. The mean and standard deviation of backprojection error for the catheter or guidewire before and after self-calibration are [Formula: see text]  mm and [Formula: see text]  mm, respectively. The mean and standard deviation of the 3D root-mean-square error (RMSE) for some markers in the phantom reduced from [Formula: see text] to [Formula: see text]  mm. CONCLUSION: A semi-automatic approach to estimate the accurate geometry of the C-arm system was presented. Results show the reduction in the 2D backprojection error as well as the 3D RMSE after using our proposed self-calibration technique. This approach is essential for 3D reconstruction of the vascular trees or post-processing techniques of angiography systems that rely on accurate geometry parameters

Fluid-structure interaction in intracranial vessel walls: The role of patient-specific wall thickness

Computational Fluid Dynamics studies try to support physicians during therapy planning of intracranial aneurysms. However, multiple assumptions (e.g. rigid vessel walls) are required leading to a sparse acceptance of numerical approaches within the medical community. This study incorporates multiple fluid-structural simulations for an intracranial basilar artery bifurcation. Based on a patient-specific dataset, which was acquired using optical coherence tomography, minimum, mean, maximum, and diameter-dependent thicknesses were generated and compared w.r.t. hemodynamic and wall stress parameters. The comparison of different wall thickness models revealed a strong variability among the analyzed parameters depending on the corresponding assumption. Using the patient-specific configuration as a reference, constant thicknesses lead to differences of up to 100 % in the mean wall stresses. Even the diameter-dependent thickness results in deviations of 32 %, demonstrating the wide variability of computational predictions due to inaccurate assumptions. The findings of this study highlight the importance of geometry reconstruction including accurate wall thickness reproduction for fluid-structure simulations. Patient-specific wall thickness seems to be out of alternatives regarding the realistic prediction of wall stress distributions