Figure 1
microTorch package structure. Top level (green) includes the package source code (src/microtorch) alongside example notebooks, simulated data, scripts, tests and documentation. The source code (blue) contains configuration files, loss functions, neural networks, signal model classes, helper functions (in utils) and package data (in resources).
Figure 2
Overview of the microTorch fitting procedure. Acquired dMRI data are fed into the network at the input layer (green nodes) and passed through multiple hidden layers (blue nodes). The final layer implements the desired compartment model signal equation in PyTorch, taking the model parameters and associated acquisition protocol parameters as input to generate a synthetic signal. The loss between this synthetic signal and the measured signal is then minimised, ensuring that the network (yellow output layer) learns the underlying model parameters.