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Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation

  • Xia ZhongEmail author
  • Philipp Roser
  • Siming Bayer
  • Nishant Ravikumar
  • Norbert Strobel
  • Annette Birkhold
  • Tim Horz
  • Markus Kowarschik
  • Rebecca Fahrig
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Xia Zhong
    • 1
    Email author
  • Philipp Roser
    • 1
    • 3
  • Siming Bayer
    • 1
  • Nishant Ravikumar
    • 1
  • Norbert Strobel
    • 2
  • Annette Birkhold
    • 2
  • Tim Horz
    • 2
  • Markus Kowarschik
    • 2
  • Rebecca Fahrig
    • 2
  • Andreas Maier
    • 1
    • 3
  1. 1.Pattern Recognition LabFAU Erlangen-NürnbergErlangenDeutschland
  2. 2.Siemens Healthcare GmbHForchheimDeutschland
  3. 3.Erlangen Graduate School in Advanced Optical Technologies (SAOT)ErlangenDeutschland

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