A sparse row-action algorithm for magnetic particle imaging

TY - JOUR

T1 - A sparse row-action algorithm for magnetic particle imaging

AU - Lieb, F.

AU - Knopp, T.

N1 - Publisher Copyright: © 2020 Infinite Science Publishing.

PY - 2020

Y1 - 2020

N2 - The image reconstruction in Magnetic Particle Imaging (MPI) relies on efficiently solving an ill-posed inverse problem. Current state-of-the-art reconstruction methods are either based on row-action methods with fast convergence but limited noise suppression or advanced sparsity constraints showing better image quality, but suffering from a higher computational complexity and slower convergence. In this contribution, we propose a novel row-action framework where advanced sparsity constraints, e.g., a combination of l1-and TV-norm, can be included. Its performance is numerically evaluated on simulated and real MPI data, showing a significant reduction of computation time while retaining the enhanced imaging quality.

AB - The image reconstruction in Magnetic Particle Imaging (MPI) relies on efficiently solving an ill-posed inverse problem. Current state-of-the-art reconstruction methods are either based on row-action methods with fast convergence but limited noise suppression or advanced sparsity constraints showing better image quality, but suffering from a higher computational complexity and slower convergence. In this contribution, we propose a novel row-action framework where advanced sparsity constraints, e.g., a combination of l1-and TV-norm, can be included. Its performance is numerically evaluated on simulated and real MPI data, showing a significant reduction of computation time while retaining the enhanced imaging quality.

U2 - 10.18416/IJMPI.2020.2009002

DO - 10.18416/IJMPI.2020.2009002

M3 - Other (editorial matter etc.)

AN - SCOPUS:85090297548

VL - 6

JO - Int J Magn Part Imag

JF - Int J Magn Part Imag

SN - 2365-9033

IS - 2

M1 - 2009002

ER -