Dynamic image reconstruction with motion priors in application to 3D magnetic particle imaging

TL;DR

This paper introduces a method for improving 3D magnetic particle imaging reconstructions by incorporating motion priors through flow-parameter-dependent PDEs, effectively reducing motion artifacts in dynamic imaging.

Contribution

It develops a novel approach for integrating motion priors into 3D+time MPI reconstruction using PDEs, with analytical and numerical validation on simulated and real data.

Findings

Enhanced image quality in dynamic MPI reconstructions

Effective motion artifact reduction in 3D+time imaging

Validated approach on experimental and in-vivo data

Abstract

Various imaging modalities allow for time-dependent image reconstructions from measurements where its acquisition also has a time-dependent nature. Magnetic particle imaging (MPI) falls into this class of imaging modalities and it thus also provides a dynamic inverse problem. Without proper consideration of the dynamic behavior, motion artifacts in the reconstruction become an issue. More sophisticated methods need to be developed and applied to the reconstruction of the time-dependent sequences of images. In this context, we investigate the incorporation of motion priors in terms of certain flow-parameter-dependent PDEs in the reconstruction process of time-dependent 3D images in magnetic particle imaging. The present work comprises the method development for a general 3D+time setting for time-dependent linear forward operators, analytical investigation of necessary properties in the MPI forward operator, modeling aspects in dynamic MPI, and extensive numerical experiments on 3D+time imaging including simulated data as well as measurements from a rotation phantom and in-vivo data from a mouse.