Numerical Reconstruction in Magnetic Particle Imaging

Abstract

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.

References in corpus (3)

• Sketching as a Tool for Numerical Linear Algebra
• Mathematical models for magnetic particle imaging
• Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging

Cited by in corpus (8)

• TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
• A Wavelet Based Sparse Row-Action Method for Image Reconstruction in Magnetic Particle Imaging
• Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging
• An ${\ell }^1$-Plug-and-Play Approach for MPI Using a Zero Shot Denoiser with Evaluation on the 3D Open MPI Dataset
• L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset
• On the Discrepancy Principle for Stochastic Gradient Descent
• Regularized Linear Inversion with Randomized Singular Value Decomposition
• Modeling the Magnetization Dynamics for Large Ensembles of Immobilized Magnetic Nanoparticles in Multi-dimensional Magnetic Particle Imaging