@article{UllherrDiezZabler2022,
  author    = {Ullherr, Maximilian and Diez, Matthias and Zabler, Simon},
  title     = {Robust image reconstruction strategy for multiscalar holotomography},
  series = {Journal of Imaging},
  volume    = {8},
  journal   = {Journal of Imaging},
  number    = {2},
  issn      = {2313-433X},
  doi       = {10.3390/jimaging8020037},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262112},
  year      = {2022},
  abstract  = {Holotomography is an extension of computed tomography where samples with low X-ray absorption can be investigated with higher contrast. In order to achieve this, the imaging system must yield an optical resolution of a few micrometers or less, which reduces the measurement area (field of view = FOV) to a few mm at most. If the sample size, however, exceeds the field of view (called local tomography or region of interest = ROI CT), filter problems arise during the CT reconstruction and phase retrieval in holotomography. In this paper, we will first investigate the practical impact of these filter problems and discuss approximate solutions. Secondly, we will investigate the effectiveness of a technique we call "multiscalar holotomography", where, in addition to the ROI CT, a lower resolution non-ROI CT measurement is recorded. This is used to avoid the filter problems while simultaneously reconstructing a larger part of the sample, albeit with a lower resolution in the additional area.},
  language  = {en}
}
@phdthesis{Langejahn2022,
  author    = {Langejahn, Marcus},
  title     = {Hard X-ray Properties of Relativistically Beamed Jets from Radio- and Gamma-Ray-Bright Blazars},
  doi       = {10.25972/OPUS-28200},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-282009},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {In this work I characterize the hard X-ray properties of blazars, active galactic nuclei with highly beamed emission, which are notoriously hard to detect in this energy range. I employ pre-defined samples of beamed AGN: the radio-selected MOJAVE and TANAMI samples, as well as the most recent gamma-ray-selected Fermi/LAT 4LAC catalog. The hard X-ray data is extracted from the 105-month all-sky survey maps of the Swift/BAT (Burst Alert Telescope) in the energy band of 20 keV to 100 keV. A great majority of both the MOJAVE and TANAMI samples are significantly detected, with signal-to noise ratios of the sources often just below the X-ray catalog signal thresholds. All blazar sub-types (FSRQs, BL Lacs) and radio galaxies show characteristic ranges of X-ray flux, luminosity, and photon index. Their properties are correlated with the corresponding SED's shape / peak frequency. The LogN-LogS distributions of the samples show a scarcity of blazars in the middle and lower X-ray flux range, indicating differing evolutionary paths between radio and X-ray emission, which is also suggested by the corresponding luminosity functions. Compared to the radio samples, the 4LAC sources are on average significantly less bright in the BAT band since this range often coincides with the spectral gap region between the two big SED emission bumps. Also, the spectral shapes differ notably, especially for the sub-type of BL Lacs. Using the parameter space of X-ray and gamma-ray photon indices, 35 blazar candidate sources can be assigned to either the FSRQ or BL Lac type with high certainty. The reason why many blazars are weak in this energy band can be traced back to a number of factors: the selection bias of the initial sample, differential evolution of the X-rays and the wavelengths in which the sample is defined, and the limited sensitivity of the observing instruments.},
  subject      = {Aktiver galaktischer Kern},
  language  = {en}
}
@article{VollmerSaraviVollmeretal.2022,
  author    = {Vollmer, Andreas and Saravi, Babak and Vollmer, Michael and Lang, Gernot Michael and Straub, Anton and Brands, Roman C. and K{\"u}bler, Alexander and Gubik, Sebastian and Hartmann, Stefan},
  title     = {Artificial intelligence-based prediction of oroantral communication after tooth extraction utilizing preoperative panoramic radiography},
  series = {Diagnostics},
  volume    = {12},
  journal   = {Diagnostics},
  number    = {6},
  issn      = {2075-4418},
  doi       = {10.3390/diagnostics12061406},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278814},
  year      = {2022},
  abstract  = {Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen's kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100\% for InceptionV3) but low sensitivity (highest sensitivity 42.86\% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1-4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74\% to 95.02\%, whereas sensitivity ranged from 14.14\% to 59.60\%. Cohen's kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen's kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14\% to 95.24\%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.},
  language  = {en}
}