PySimPace v2.0: An Easy-to-Use Simulation Tool with Machine Learning Pipelines for Realistic MRI Motion Artifact Generation

摘要

Motion artifacts in structural and functional magnetic resonance imaging (MRI) pose a significant challenge for both clinical use and machine learning (ML)-based image analysis. Existing ML approaches for artifact correction require paired clean and corrupted datasets, which are difficult to acquire. We present py-simpace, an open-source, pip-installable MRI motion artifact simulation toolkit with native ML integration. py-simpace supports structural MRI and functional MRI (fMRI) simulation, offering configurable k-space and image-space motion, ghosting, Gibbs ringing, and physiological noise. It provides an end-to-end pipeline with a ready-to-use PyTorch Dataset interface for ML training. We describe the design of py-simpace v2.0, compare it with existing tools, and demonstrate its utility for robust artifact correction model development.