@article{0551fb7764254fd8bbb3480314534fae,
title = "Motion compensation for non-periodic dynamic tracer distributions in multi-patch magnetic particle imaging",
abstract = "Objective. While the spatial and temporal resolution of magnetic particle imaging is very high, the size of the field of view is limited due to physiological constraints. Multi-patch scans allow for covering larger areas by sequentially scanning smaller subvolumes, so-called patches. The visualization of tracer dynamics with a high temporal resolution are of particular interest in many applications, e.g. cardiovascular interventions or blood flow measurements. The reconstruction of non-periodic dynamic tracer distributions is currently realized by the reconstruction of a time-series of frames under the assumption of nearly static behavior during the scan of each frame. While this approach is feasible for limited velocities, it results in data gaps in multi-patch scans leading thus to artifacts for strong dynamics. In this article, we are aiming for the reconstruction of dynamic tracer concentrations with high velocities and the compensation of motion and multi-patch artifacts. Approach. We present a reconstruction method for dynamic tracer distributions using a dynamic forward model and representing the concentration within each voxel by a spline curve. The method is evaluated with simulated single- and multi-patch data. Main results. The dynamic model enables for the reconstruction of fast tracer dynamics from few frames and the spline approach approximates the missing data which reduces multi-patch artifacts. Significance. The presented method allows to compensate motion and multi-patch artifacts and to reconstruct fast dynamic tracer distributions with arbitrary motion patterns.",
keywords = "dynamic inverse problems, image reconstruction, magnetic particle imaging, model-based reconstruction, motion compensation, regularization",
author = "Christina Brandt and Christiane Schmidt",
note = "Physics in Medicine Biology",
year = "2022",
month = apr,
day = "21",
doi = "10.1088/1361-6560/ac5ce6",
language = "English",
volume = "67",
journal = "Physics in medicine and biology",
issn = "0031-9155",
publisher = "IOP Publishing Ltd.",
number = "8",
}
@phdthesis{9869c8fbdafd416594691021ce7edc4a,
title = "Magnetic Particle Imaging - Modeling and Solving a Dynamic Inverse Problem",
abstract = "Magnetic particle imaging (MPI) is a functional, tracer-based medical imaging technique, which measures the non-linear response of magnetic nanoparticles to a dynamic magnetic field. The visualization of tracer dynamics with high temporal resolution is of particular interest in many applications, e.g. cardiovascular interventions or blood flow measurements. While MPI offers a very high spatial and temporal resolution, the size of its field-of-view is limited by physiological constraints. Multi-patch scans, sequentially scanning smaller subvolumes, so-called patches, allow to increase the total field-of-view. The forward operator, or system matrix, required for image reconstruction can be determined by calibration scans or physical models. Neither measured system matrices nor the standard forward models in MPI account for changes in the tracer concentration during a single scanning cycle. As a result, to date, non-periodic dynamic tracer distributions are mostly reconstructed as a time-series of frames under the assumption of nearly static behavior during the scan of each frame. While being a feasible approach for limited velocities, the reduced temporal resolution and data gaps in multi-patch sequences and the ignorance of dynamics in the forward operators cause motion and displacement artifacts in the case of strong dynamics. In this thesis, we introduce a reconstruction method for dynamic tracer distributions based on a dynamic forward model and a spline representation of the concentration. First, we present the dynamic MPI model and analyze its influence on the measurements and reconstructions with and without noise compared to the static model. Second, we establish the dynamic reconstruction approach for non-periodic motion in multi-patch sequences. Third, the new method is evaluated on the basis of synthetic single- and multi-patch data showing that the dynamic model enables for the reconstruction of fast tracer dynamics from a few frames and the spline approach approximates the missing data, which reduces ...",
author = "Christiane Schmidt",
year = "2022",
language = "English",
school = "University of Hamburg",
}
@article{cdc30df8fc1748c19ca47a8f90360f42,
title = "Modeling Magnetic Particle Imaging for Dynamic Tracer Distributions",
abstract = "Magnetic Particle Imaging (MPI) is a promising tracer-based, functional medical imaging technique which measures the non-linear magnetization response of magnetic nanoparticles to a dynamic magnetic field. For image reconstruction, system matrices from time-consuming calibration scans are used predominantly. Finding modeled forward operators for magnetic particle imaging, which are able to compete with measured matrices in practice, is an ongoing topic of research. The existing models for magnetic particle imaging are by design not suitable for arbitrary dynamic tracer concentrations. Neither modeled nor measured system matrices account for changes in the concentration during a single scanning cycle. In this paper we present a new MPI forward model for dynamic concentrations. A static model will be introduced briefly, followed by the changes due to the dynamic behavior of the tracer concentration. Furthermore, the relevance of this new extended model is examined by investigating the influence of the extension and example reconstructions with the new and the standard model.",
keywords = "Dynamic inverse problems, Magnetic particle imaging, Model-based reconstruction, Motion artifacts, Motion compensation",
author = "Christina Brandt and Christiane Schmidt",
year = "2021",
month = nov,
day = "8",
doi = "10.1007/s11220-021-00368-w",
language = "English",
volume = "22",
journal = "Sensing and Imaging ",
publisher = "Springer Nature Switzerland AG",
number = "1",
}
@article{f6805914f4db4d1da11c1691c2401bde,
title = "Dynamic concentration reconstruction for magnetic particle imaging using splines",
keywords = "Squid, Magnetic Nanoparticles, Magnetometers, Nanomagnetics, Magnetic Fluids, Nanoparticles, Squid, Magnetic Nanoparticles, Magnetometers, Nanomagnetics, Magnetic Fluids, Nanoparticles",
author = "C. Schmidt and C. Brandt",
note = "Anzahl Autoren: 2 |",
year = "2020",
month = sep,
day = "2",
doi = "10.18416/IJMPI.2020.2009049",
language = "English",
volume = "6",
journal = "International Journal on Magnetic Particle Imaging",
issn = "2365-9033",
publisher = "Infinite Science Publishing",
number = "2",
}