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The diffraction contrast modalities accessible by X-ray grating interferometers are not imaged directly but have to be inferred from sine-like signal variations occurring in a series of images acquired at varying relative positions of the interferometer’s gratings. The absolute spatial translations involved in the acquisition of these phase stepping series usually lie in the range of only a few hundred nanometers, wherefore positioning errors as small as 10 nm will already translate into signal uncertainties of 1–10% in the final images if not accounted for. Classically, the relative grating positions in the phase stepping series are considered input parameters to the analysis and are, for the Fast Fourier Transform that is typically employed, required to be equidistantly distributed over multiples of the gratings’ period. In the following, a fast converging optimization scheme is presented simultaneously determining the phase stepping curves’ parameters as well as the actually performed motions of the stepped grating, including also erroneous rotational motions which are commonly neglected. While the correction of solely the translational errors along the stepping direction is found to be sufficient with regard to the reduction of image artifacts, the possibility to also detect minute rotations about all axes proves to be a valuable tool for system calibration and monitoring. The simplicity of the provided algorithm, in particular when only considering translational errors, makes it well suitable as a standard evaluation procedure also for large image series.
Magnetic Particle Imaging (MPI) is a promising new tomographic modality for fast as well as three-dimensional visualization of magnetic material. For anatomical or structural information an additional imaging modality such as computed tomography (CT) is required. In this paper, the first hybrid MPI-CT scanner for multimodal imaging providing simultaneous data acquisition is presented.
Improved radiological examinations with newly developed 3D models may increase understanding of Meniere's disease (MD). The morphology and course of the vestibular aqueduct (VA) in the temporal bone might be related to the severity of MD. The presented study explored, if the VA of MD and non-MD patients can be grouped relative to its angle to the semicircular canals (SCC) and length using a 3D model. Scans of temporal bone specimens (TBS) were performed using micro-CT and micro flat panel volume computed tomography (mfpVCT). Furthermore, scans were carried out in patients and TBS by computed tomography (CT). The angle between the VA and the three SCC, as well as the length of the VA were measured. From these data, a 3D model was constructed to develop the vestibular aqueduct score (VAS). Using different imaging modalities it was demonstrated that angle measurements of the VA are reliable and can be effectively used for detailed diagnostic investigation. To test the clinical relevance, the VAS was applied on MD and on non-MD patients. Length and angle values from MD patients differed from non-MD patients. In MD patients, significantly higher numbers of VAs could be assigned to a distinct group of the VAS. In addition, it was tested, whether the outcome of a treatment option for MD can be correlated to the VAS.
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.