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Tobias Würfl M. Sc.

Researcher in the Precision Learning (PL) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Improve image quality in CT with new calibration-free algorithms
Application of Raw Data Consistency Conditions in Cone-Beam CT
  • X-ray Computed Tomography scanning is a standard procedure in medical imaging. Though the fundamentals of reconstructing the density of an object from lower dimensional projection images are well understood, real systems have to deal with additional sources of corruption. These can either be of geometric nature, from imperfect calibration or patient movement or physical effects like beam hardening and scatter. Many methods have been proposed to correct for these problems. Most of them rely on prior knowledge or additional equipment.

    However the inconsistency introduced by those corruptions can be quantified using redundancies in CT raw data. By numerically minimizing this inconsistency using appropriate compensation models, artifact reduction can be achieved without using prior knowledge or external equipment.

    The goal of this project is to develop novel artifact compensation algorithms for cone-beam CT, based on raw- data consistency conditions and to extend and improve existing compensation algorithms for calibration and motion compensation. A particular sub-goal for extension is, to extend the applicability of consistency-condition-based algorithms to trajectories required for theoretically exact reconstruction, like helix or circle-line trajectories.