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Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen (N = 43) and clinical practice (N = 9). The model robustness was further evaluated on three independent open-source datasets (N = 23 + 7 + 17 scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of 0.97 and 0.94, intersection-over-union scores of 0.94 and 0.89 and average Hausdorf distances of 0.065 and 0.14 voxel units were achieved. The landmark localization task was performed automatically with an average localization error of 3.3 and 5.2 voxel units. A robust, albeit reduced performance could be
attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance beneft of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.
Minimally invasive endovascular interventions have become an important tool for the treatment of cardiovascular diseases such as ischemic heart disease, peripheral artery disease, and stroke. X-ray fluoroscopy and digital subtraction angiography are used to precisely guide these procedures, but they are associated with radiation exposure for patients and clinical staff. Magnetic Particle Imaging (MPI) is an emerging imaging technology using time-varying magnetic fields combined with magnetic nanoparticle tracers for fast and highly sensitive imaging. In recent years, basic experiments have shown that MPI has great potential for cardiovascular applications. However, commercially available MPI scanners were too large and expensive and had a small field of view (FOV) designed for rodents, which limited further translational research. The first human-sized MPI scanner designed specifically for brain imaging showed promising results but had limitations in gradient strength, acquisition time and portability. Here, we present a portable interventional MPI (iMPI) system dedicated for real-time endovascular interventions free of ionizing radiation. It uses a novel field generator approach with a very large FOV and an application-oriented open design enabling hybrid approaches with conventional X-ray-based angiography. The feasibility of a real-time iMPI-guided percutaneous transluminal angioplasty (PTA) is shown in a realistic dynamic human-sized leg model.
Positional plagiocephaly (PP) is the most common skull deformity in infants. Different classification systems exist for graduating the degree of PP, but all of these systems are based on two-dimensional (2D) parameters. This limitation leads to several problems stemming from the fact that 2D parameters are used to classify the three-dimensional (3D) shape of the head. We therefore evaluate existing measurement parameters and validate a newly developed 3D parameter for quantifying PP. Additionally, we present a new classification of PP based on a 3D parameter. 210 patients with PP and 50 patients without PP were included in this study. Existing parameters (2D and 3D) and newly developed volume parameters based on a 3D stereophotogrammetry scan were validated using ROC curves. Additionally, thresholds for the new 3D parameter of a 3D asymmetry index were assessed. The volume parameter 3D asymmetry index quantifies PP equally as well as the gold standard of 30° diagonal difference. Moreover, a 3D asymmetry index allows for a 3D-based classification of PP. The 3D asymmetry index can be used to define the degree of PP. It is easily applicable in stereophotogrammetric datasets and allows for comparability both intra- and inter-individually as well as for scientific analysis.