Radiopacity assessment of neurovascular implants

An experimental method for radiopacity evaluation of neurovascular implants is presented. State of the art methods are described for cardiovasular implants. To extend the methods to neurovascular devices, imaging parameters were determined from clinical radiography data sets and protocols. To reduce background noise, a thresholding method is introduced. This method is evaluated on a setup with four different laser cut and braided stents. The results are compared to current evaluation methods without filtering. It is shown that unfiltered methods underestimate the radiopacity by 0.89 mm ± 0.21 mm (aluminum equivalent). The introduced method is highly repeatable and uses modern clinical imaging technology. Thus an image evaluation under realisitc conditions is possible. Slightly visible vascular devices and implants can be compared with high accuracy

Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)-Phase Ib: Effect of morphology on hemodynamics.

BackgroundImage-based blood flow simulations have been increasingly applied to investigate intracranial aneurysm (IA) hemodynamics. However, the acceptance among physicians remains limited due to the high variability in the underlying assumptions and quality of results.MethodsTo evaluate the vessel segmentation as one of the most important sources of error, the international Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH) was announced. 26 research groups from 13 different countries segmented three datasets, which contained five IAs in total. Based on these segmentations, 73 time-dependent blood flow simulations under consistent conditions were carried out. Afterwards, relevant flow and shear parameters (e.g., neck inflow rate, parent vessel flow rate, spatial mean velocity, and wall shear stress) were analyzed both qualitatively and quantitatively.ResultsRegarding the entire vasculature, the variability of the segmented vessel radius is 0.13 mm, consistent and independent of the local vessel radius. However, the centerline velocity shows increased variability in more distal vessels. Focusing on the aneurysms, clear differences in morphological and hemodynamic parameters were observed. The quantification of the segmentation-induced variability showed approximately a 14% difference among the groups for the parent vessel flow rate. Regarding the mean aneurysmal velocity and the neck inflow rate, a variation of 30% and 46% was observed, respectively. Finally, time-averaged wall shear stresses varied between 28% and 51%, depending on the aneurysm in question.ConclusionsMATCH reveals the effect of state-of-the-art segmentation algorithms on subsequent hemodynamic simulations for IA research. The observed variations may lead to an inappropriate interpretation of the simulation results and thus, can lead to inappropriate conclusions by physicians. Therefore, accurate segmentation of the region of interest is necessary to obtain reliable and clinically helpful flow information

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

Variability of intra-aneurysmal hemodynamics caused by stent-induced vessel deformation

Clinical observation revealed deformations of the local vasculature due to implanted devices related to treatment of intracranial aneurysms. Pre- and post-interventional image data is reviewed and segmented. Based on Computational Fluid Dynamics (CFD) the effect of the vessel deformation on the intra-aneurysmal hemodynamics is investigated. The simulations incorporate the vessel trees with aneurysm excluding the device itself in order to consider the isolated effect of geometric modifications. As a result, the aneurysm inflow jet is redirected, changing the local flow quantities. Finally, a neck inflow rate reduction of 52.5 % is archived. Thus, a targeted vessel straightening as a novel tool for treatment planning appear to be an idea worth to discuss in the community

Intravascular optical coherence tomography (OCT) as an additional tool for the assessment of stent structures

Evaluation of the vascular stent position, shape and correct expansion has a high relevance in therapy and diagnosis. Hence, the wall apposition in vessel areas with differing diameters and the appearance of torsions or structural defects of the implant body caused by catheter based device dropping are of special interest. Neurovascular implants like braided flow diverter and laser cut stents consist of metal struts and wires with diameters of about 40 µm. Depending on the implants material composition, visibility is poor with conventional 2D X-ray fluoroscopic and radiographic imaging. The metal structures of the implants also lead to artifacts in 3D X-ray images and can hamper the assessment of the device position. We investigated intravascular optical coherence tomography (OCT) as a new imaging tool for the evaluation of the vascular stent position, its shape and its correct expansion for 3 different vascular implants

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