Poster 41

In this work a UNet-based normalization method by paired image-to-image translation of Chest CT images was developed. Due to different noise-levels, emphysema quantification shows sincere subordination to the choice of the filterkernel. Images for training and testing of 71 patients were available, reconstructed using the smooth Siemens B20f filterkernel and the sharp B80f filterkernel. Results were evaluated in regard to the image quality, including a visual assessment by two imaging experts, the L1 distance, the emphysema quantification (emphysema index and Dice overlap of emphysema segmentations). Emphysema quantification was compared to classical normalization methods. Our approach lead to very good image quality in which the mean B20f L1 distance to the B80f could be reduced by about 88:5% and the mean Dice was raised by 189% after normalization. Classical methods were outperformed. Even though small differences between B20f and normalized B80f images were noticed, the normalized images were found to be overall of diagnostic quality.

Postersession 3, U-Net Applications