Jannis Bodden
Technische Universität München
20 Papers
12 Citations
Jannis Bodden is an academic researcher from Technische Universität München. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 4, co-authored 8 publications.
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Papers
Bone mineral density measurements derived from dual-layer spectral CT enable opportunistic screening for osteoporosis.
Ferdinand Roski,Johannes Hammel,Kai Mei,Thomas Baum,Jan S. Kirschke,Alexis Laugerette,Felix K. Kopp,Jannis Bodden,Daniela Pfeiffer,Franz Pfeiffer,Ernst J. Rummeny,Peter B. Noël,Peter B. Noël,Alexandra S. Gersing,Benedikt J. Schwaiger +14 more
TL;DR: Investigating the in vivo applicability of non-contrast-enhanced hydroxyapatite-specific bone mineral density (BMD) measurements based on dual-layer CT (DLCT) suggests that opportunistic DLCT-based BMD measurements are an alternative to QCT, without requiring phantoms and specific protocols.
Qualitative and Quantitative Assessment of Emphysema Using Dark-Field Chest Radiography.
Theresa Urban,Florian T. Gassert,Manuela Frank,Konstantin Willer,Wolfgang Noichl,Philipp Buchberger,Rafael Schick,Thomas Koehler,Jannis Bodden,Alexander A. Fingerle,Andreas Sauter,M. A. Makowski,Franz Pfeiffer,Daniela Pfeiffer +13 more
TL;DR: Pulmonary emphysema leads to reduced signal intensity on dark-field chest radiographs, showing the technique has potential as a diagnostic tool in the assessment of lung diseases.
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A robust convolutional neural network for lung nodule detection in the presence of foreign bodies.
Manuel Schultheiss,Sebastian A. Schober,Marie Lodde,Jannis Bodden,Juliane Aichele,Christina Müller-Leisse,Bernhard Renger,Franz Pfeiffer,Daniela Pfeiffer +8 more
TL;DR: The trained RetinaNet architecture was found to be only slightly prone to foreign bodies in terms of misclassifications: out of 59 additional radiographs containing foreign bodies, false-positives in two radiographs were falsely detected due to foreign body detection.
Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance
Manuel Schultheiss,Philipp Schmette,Jannis Bodden,Juliane Aichele,Christina Müller-Leisse,Felix G. Gassert,Florian T. Gassert,Joshua Gawlitza,Felix C. Hofmann,Daniel Sasse,Claudio E. von Schacky,Sebastian Ziegelmayer,Fabio De Marco,Bernhard Renger,Marcus R. Makowski,Franz Pfeiffer,Daniela Pfeiffer +16 more
TL;DR: In this paper, the authors presented a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge was obtained by forward-projecting the volume.
CTPA with a conventional CT at 100 kVp vs. a spectral-detector CT at 120 kVp: Comparison of radiation exposure, diagnostic performance and image quality.
Andreas Sauter,Nadav Shapira,Nadav Shapira,Felix K. Kopp,Juliane Aichele,Jannis Bodden,Andreas Knipfer,Ernst J. Rummeny,Peter B. Noël,Peter B. Noël +9 more
TL;DR: In the current study, CTDIvol was lower with SD-CT than with C-CT, even when 100 kVp was used for the latter.
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