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cs.CV2018
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
Andreas Hess, Raphael Meier, Johannes Kaesmacher +5
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The propo…
cs.CV2018
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
Aline Sindel, Katharina Breininger, Johannes Käßer +3
Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this…