no code implementations • 10 Mar 2021 • Florian Kofler, Ivan Ezhov, Fabian Isensee, Fabian Balsiger, Christoph Berger, Maximilian Koerner, Beatrice Demiray, Julia Rackerseder, Johannes Paetzold, Hongwei Li, Suprosanna Shit, Richard McKinley, Marie Piraud, Spyridon Bakas, Claus Zimmer, Nassir Navab, Jan Kirschke, Benedikt Wiestler, Bjoern Menze
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
no code implementations • 4 Mar 2021 • Fabian Balsiger, Alain Jungo, Naren Akash R J, Jianan Chen, Ivan Ezhov, Shengnan Liu, Jun Ma, Johannes C. Paetzold, Vishva Saravanan R, Anjany Sekuboyina, Suprosanna Shit, Yannick Suter, Moshood Yekini, Guodong Zeng, Markus Rempfler
With this growth, however, come new challenges for the community.
While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines.
Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs).
Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction.
By synthetic experiments, we further show the capability of our approach in learning an explicit anatomical shape representation.
Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition.