Sven Jaeschke1, 2, Robin Martin Heidemann2, Aleksandar Petrovic2
1HAW Hamburg, Hamburg, Germany; 2Healthcare Sector, Siemens AG, Erlangen, Germany
Ultra high field (UHF) MRI offers improved image contrast and higher SNR enabling isotropic, high-resolution in vivo anatomical imaging. However, image quality is affected by bias fields, which can be a hindrance for tissue segmentation or classification. In this study, we propose a novel, fast and fully automated image inhomogeneity correction method, which is well suited for UHF applications. In comparison to former methods, we employ a bounded Nelder-Mead simplex optimizer and use a joint intensity-gradient histogram to calculate entropy.