Numerical Reconstruction in Magnetic Particle Imaging
For researchers in MPI, this provides incremental algorithmic improvements that are easy to integrate into existing reconstruction pipelines.
This work addresses numerical reconstruction in magnetic particle imaging (MPI) by improving Tikhonov regularization with nonnegativity constraint through better data fidelity choice, regularization parameter selection, and acceleration via randomized singular value decomposition, achieving enhanced reconstruction quality on a public dataset.
Magnetic particle imaging (MPI) is a medical imaging modality of recent origin, and it exploits the nonlinear magnetization phenomenon to recover the spatially dependent concentration of the nanoparticles. Currently, image reconstruction in MPI is frequently carried out by standard Tikhonov regularization with nonnegativity constraint, which is then minimized by a Kaczmarz type method. In this work, we revisit several issues in the numerical reconstruction in MPI from the perspective of modern inverse theory, i.e., the choice of data fidelity, and choosing a suitable regularization parameter and accelerating Kaczmarz iteration via randomized singular value decomposition. These algorithmic tricks are straightforward to implement and easy to incorporate in existing reconstruction algorithms. Their significant potentials are illustrated by extensive numerical experiments on a publicly available dataset.