Welcome to an interactive Jupyter Book for the Magnetic Resonance in Medicine paper by Sebastiano Barbieri et al. entitled "Deep learning how to fit an intravoxel incoherent motion model to diffusion weighted MRI". It reports a prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion‐weighted MRI (DW‐MRI) data and evaluates its performance.

This Jupyter Book lets you interact with a demo of their code to test their phase unwrapping method. You can change the code by inline on the pages or launch a MyBinder session to run your code in a Jupyter Notebook in another tab. The figures are made interactive using Plotly.

All of the analyssis code is contained inside one Jupyter notebook that recomputes eatch of the 3 considered algorithms and the average of each algorithm is recorded. The algorithms in the example are implemented using Python 3.6 and the library Pytorch 0.4.1 (without CUDA drivers).