@article{Hamm1999,
  author    = {Hamm, Bernd},
  title     = {Globalisierung und Stadtentwicklung},
  series = {Geographische Revue : Zeitschrift f{\"u}r Literatur und Diskussion},
  volume    = {1},
  journal   = {Geographische Revue : Zeitschrift f{\"u}r Literatur und Diskussion},
  number    = {1},
  issn      = {1438-3039},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-23775},
  pages     = {35 -- 63},
  year      = {1999},
  language  = {de}
}
@article{MichallekGenskeNiehuesetal.2022,
  author    = {Michallek, Florian and Genske, Ulrich and Niehues, Stefan Markus and Hamm, Bernd and Jahnke, Paul},
  title     = {Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging},
  series = {European Radiology},
  volume    = {32},
  journal   = {European Radiology},
  number    = {7},
  publisher = {Springer},
  address   = {New York},
  issn      = {0938-7994},
  doi       = {10.1007/s00330-022-08592-y},
  pages     = {4587 -- 4595},
  year      = {2022},
  abstract  = {Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6\%, 8\%, 6\%, and 22\% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48\%, 82\%, 84\%, and 92\% of features, and 52\%, 20\%, 17\%, and 39\% of features were repeatable, respectively. Only 5\% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13\% with AiCE at doses above 1 mGy and 17\% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features.},
  language  = {en}
}