Analysis of leakage artifacts and their impact on convergence of algebraic reconstruction in multi-contrast magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconstructs the signal from different tracer materials or environments separately, resulting in multi-channel images that enable quantification of, for example, temperature or viscosity. Single- and multi-contrast MPI reconstructions produce different kinds of artifacts. The objective of this work is threefold: first, to present the concept of multi-contrast specific MPI channel leakage artifacts; second, to ascertain the source of these leakage artifacts; and third, to introduce a method for their reduction.Approach.A definition for leakage artifacts is established, and a quantification method is proposed. A comprehensive analysis is conducted to establish a connection between the properties of the multi-contrast MPI system matrix and the leakage artifacts. Moreover, a two-step measurement and reconstruction method is introduced to reduce channel leakage artifacts between multi-contrast MPI channels.Main results.The severity of these artifacts correlates with the system matrix shape and condition number and depends on the similarity of the corresponding frequency components. Using the proposed two-step method on both semi-simulated and measured data a significant leakage reduction and speed up the convergence of the multi-contrast MPI reconstruction was observed.Significance.The multi-contrast system matrix analysis we conducted is essential for understanding the source of the channel leakage artifacts and finding methods to reduce them. Our proposed two-step method is expected to improve the potential for real-time multi-contrast MPI applications.