Misalignment-Correction in C-arm-based flat-detector CT (FD-CT) is a frequently discussed problem. To avoid artifacts
caused by geometrical instabilities, numerous methods for misalignment correction were investigated. Most of them
make use of a foregoing calibration routine, based on scanning a specific phantom. The aim of this study is to develop
and evaluate an online image-content-based calibration technique without using any kind of marker or calibration
phantom. The introduced method is based on a gradient descent method, minimizing an entropy criterion which is used
to optimize the underlying geometry parameters of the acquisition system. It is formed as multistep approach, including a
global, local and projection wise optimization. This enables the elimination of general system misalignments, as well as a
reduction of streak artifacts and the adjustment of patient motion artifacts. Phantom and patient measurements with the
C-arm FD-CT system Artis Zeego (Siemens AG, Healthcare Sector, Forchheim, Germany) were used to validate the
algorithm for realistic applications. It reduced most of the actual misalignment and increased image quality drastically.
Phantom-studies, starting from the standard system geometry without a foregoing calibration showed very good results.
Online-calibration is possible with our approach and therefore, the limitation to predefined scan-protocols is obsolete.
The evaluation of patient datasets brought out the same conclusions and provides the implication of simultaneous patient