An augmentation strategy to mimic multi-scanner variability in MRI

TL;DR

This paper introduces a Gaussian Mixture Model-based data augmentation method to simulate multi-scanner variability in MRI, enhancing model generalization across different scanners and protocols.

Contribution

A novel augmentation strategy that mimics real-world scanner variability to improve MRI model robustness across multiple centers.

Findings

Improved generalization of MRI models to unseen scanners.

Effective simulation of scanner variability through tissue intensity adjustments.

Enhancement of model performance on multi-center datasets.

Abstract

Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation approach with the aim of approximating the variability in terms of intensities and contrasts present in real world clinical data. We use a Gaussian Mixture Model based approach to change tissue intensities individually, producing new contrasts while preserving anatomical information. We train a deep learning model on a single scanner dataset and evaluate it on a multi-center and multi-scanner dataset. The proposed approach improves the generalization capability of the model to other scanners not present in the training data.