There is an urgent need for a paradigm shift from group-wise comparisons to individual diagnosis in diffusion MRI (dMRI) to enable the analysis of rare cases and clinically-heterogeneous groups.
Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.
The proposed algorithms herein can estimate both parameters of the noise distribution, are robust to signal leakage artifacts and perform best when used on acquired noise maps.
Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation.
For this purpose, a training database is required, which consist of the same subjects, scanned on different scanners.