Code for Model_Informed_Machine_Learning
Thomas Yu, Erick Jorge Canales-Rodríguez, Marco Pizzolato, Gian Franco Piredda, Tom Hilbert, Elda Fischi-Gomez, Matthias Weigel, Muhamed Barakovic, Meritxell Bach Cuadra, Cristina Granziera, Tobias Kober, Jean-Philippe Thiran, Model-informed machine learning for multi-component T2 relaxometry, Medical Image Analysis, Volume 69, 2021, 101940, ISSN 1361-8415, https://doi.org/10.1016/j.media.2020.101940. (http://www.sciencedirect.com/science/article/pii/S1361841520303042)
We have uploaded a notebook which shows the training of the network/loss functions on a sample dataset.
A full tutorial for usage + code available after publication
We note that the code for generating EPG datasets are based on https://github.com/kelvinlayton/T2estimation
Tested with Python 3.6, using Tensorflow 2.0