Model-based simulation of dynamic magnetic resonance imaging signals

Abstract

This paper presents a model-based method to efficiently simulate dynamic magnetic resonance imaging signals. Using an analytical spatiotemporal object model, the method can approximate time-varying k-space signals such as those from objects in motion and/or during dynamic contrast enhancement. Both rigid-body and non-rigid-body motions can be simulated using the proposed method. In addition, it can simulate data with arbitrary data sampling order and/or non-uniform k-space trajectory. A set of simulated images were compared with real data acquired from a rat model on a 4.7 T scanner to verify the model. The efficient simulation method is expected to be useful for rapid testing of various imaging and image analysis algorithms such as image reconstruction, image registration, motion compensation, and kinetic parameter mapping.