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Item type:Publication, Access status: with files , Randomized greedy Kaczmarz for multi-contrast magnetic particle imaging(Infinite Science Publishing, 2026-03-18); ; ; ; Multi-contrast magnetic particle imaging allows for separating signals from different tracer materials or microenvi-ronments from one joint measurement, yielding multi-channel images that distinguish tracers or encode properties such as temperature or viscosity. However, leakage artifacts enforce a noticeably longer application of iterative standard methods like the Kaczmarz algorithm to reach satisfactory reconstruction results. A greedy randomized version of the Kaczmarz method is adapted, showing the potential for faster recovery.Publicationtype: Journal Article16 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Multi-contrast MPI matrix compressionMulti-contrast magnetic particle imaging (MPI) reconstructs the signal from different tracer materials or environments, resulting in multi-channel images that enable temperature or viscosity quantification. Since the multi-contrast problem is ill-posed, it is addressed by regularization methods that are commonly solved using the Kaczmarz algorithm. Unlike the single-contrast MPI problem, the multi-contrast one requires a high number of iterations to converge. Matrix compression techniques were already successfully used in single-contrast reconstruction and matrix recovery applications as in compressed sensing. Our work proposes to use matrix compression to reduce the reconstruction time needed to achieve good reconstruction quality in multi-contrast MPI.Publicationtype: Journal Article7 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Analysis of leakage artifacts and their impact on convergence of algebraic reconstruction in multi-contrast magnetic particle imagingObjective. 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.Publicationtype: Journal Article5 30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Sparse Kaczmarz for convergence speed-up in multi-contrast magnetic particle imagingMagnetic Particle Imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatio-temporal resolution, and capability to quantify the tracer distribution. Image reconstruction in MPI is an ill-posed problem, which regularization methods can address. In MPI, Tikhonov regularization is most commonly used and the corresponding optimization problem is usually solved using the Kaczmarz algorithm. Reconstruction using the Kaczmarz method for single-contrast MPI is very efficient as it produces the desired images fast after a small number of iterations. For multi-contrast MPI, however, the regular Kaczmarz algorithm fails to obtain good-quality images without channel leakage when using a small number of iterations. In this work, we propose a sparsity-promoting regularization term and an associated sparse Kaczmarz method in order to speed up convergence, especially in sparse channels. The proposed method reduces the channel leakage and as a result, speeds up convergence.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Influence of the system matrix on channel leakage artifacts in multi-contrast MPIMagnetic 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 can be addressed by the use of regularization methods. Single-and multi-contrast MPI reconstructions produce different kinds of artifacts. In this work, the multi-contrast MPI channel leakage is introduced and an analysis of the multi-contrast MPI system matrix properties is conducted to understand the source of the multi-contrast MPI channel leakage.Publicationtype: Journal Article5 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Multi-contrast MPI channel leakage reduction using a two-step measurement and reconstruction methodMagnetic particle imaging (MPI) is an imaging modality that measures the response of magnetic nanoparticle tracers to alternating magnetic fields. There has recently been exploration into multi-contrast MPI, in which the signal from different tracer materials or environments is separately reconstructed, resulting in multi-channel images that enable temperature or viscosity quantification. In this work, we investigate the channel leakage in multi-contrast MPI reconstruction and we introduce a two-step measurement and reconstruction method to quantify and reduce channel leakage between multi-contrast MPI channels.Publicationtype: Journal Article9 30 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Accelerated Kaczmarz for convergence speed-up in multi-contrast magnetic particle imaging(Infinite Science Publishing, 2022-03-21); ; ; Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatio-temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by the use of regularization methods. The corresponding optimization problem is most commonly solved using the Kaczmarz algorithm. Reconstruction using the Kaczmarz method for single-contrast MPI is very efficient as it produces the desired images fast with a small number of iterations. For multi-contrast MPI, however, the regular Kaczmarz algorithm fails to obtain good quality images without channel leakage when using a small number of iterations. In this work, we propose to use an accelerated Kaczmarz method in order to reduce the reconstruction time needed to achieve a good separation of the channels and a good image quality in multi-contrast MPI.Publicationtype: Journal Article51 142 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Reduction of bias for sparsity promoting regularization in MPI(Infinite Science Publishing, 2021-12-17); ; ; ; Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensi-tivity, high spatial and temporal resolution, and its ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which can be addressed by regularization methods that lead to a reconstruction bias, which is apparent in a systematic mismatch between true and reconstructed tracer distribution. This is expressed in a background signal, a mismatch of the spatial support of the tracer distribution and a mismatch of its values. In this work, MPI reconstruction bias and its impact are investigated and a recently proposed debiasing method with significant bias reduction capabilities is adopted.Publicationtype: Journal ArticleScopus© Citations 2 36 185 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Bias-reduction for sparsity promoting regularization in magnetic particle imaging(Infinite Science Publishing, 2020-09-02); ; ; Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.Publicationtype: Journal Article68 82