Deriving Neural Network Architectures Using Precision Learning: Parallel-to-Fan Beam Conversion - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (40th German Conference on Pattern Recognition, GCPR 2018, 2019)
Keywords: Hybrid MRI/X-ray imaging; Precision learning; Machine learning

Fan-to-Parallel Beam Conversion: Deriving Neural Network Architectures Using Precision Learning - Syben-Leisner, Christopher; Stimpel, Bernhard; Lommen, Jonathan; Würfl, Tobias; Dörfler, Arnd; Maier, Andreas (Informatik aktuell, 2019)
CRIS-Team Scopus Importer:2019-05-14

MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications - Lommen, Jonathan; Syben-Leisner, Christopher; Stimpel, Bernhard; Bayer, Siming; Nagel, Armin Michael; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications, 2018)

Projection image-to-image translation in hybrid x-ray/MR imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Lommen, Jonathan; Dörfler, Arnd; Maier, Andreas (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding x-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating x-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
CRIS-Team Scopus Importer:2019-07-12