Open MPI Data

The goal of the OpenMPIData initiative is to provide MPI measurement data to those interested in MPI research. Initially, the project included only the first few datasets, covering the most interesting cases for getting started with MPI. More recently, we have started to make the measurement data from our most recent publications available to the public. We are proud that the OpenMPIData has already been noticed by several research groups, e.g. to develop and evaluate new reconstruction algorithms.

We invite those with MPI hardware to share their data, and those interested in MPI research to contribute by using this data in their research. The project is hosted on GitHub and a detailed description is available here. To contribute, simply open an issue on the project's GitHub page.

All data are stored in the MPI Data Format (MDF), which provides a common data format for the storing of MPI raw, calibration, and reconstruction data, and are made available under a Creative Commons license CC-BY-4.0.

Reconstruction results of the shape phantom 3D dataset

Publications

Publications

[145061]
Title: Suppression of Motion Artifacts Caused by Temporally Recurring Tracer Distributions in Multi-Patch Magnetic Particle Imaging.
Written by: N. Gdaniec, M. Boberg, M. Möddel, P. Szwargulski, and T. Knopp
in: <em>IEEE Transactions on Medical Imaging</em>. November (2020).
Volume: <strong>39</strong>. Number: (11),
on pages: 3548-3558
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DOI: 10.1109/TMI.2020.2998910
URL: https://arxiv.org/abs/2205.01085
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[www]

Note: article, multi-patch, artifact, opendata, openaccess

Abstract: Magnetic particle imaging is a tracer based imaging technique to determine the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Due to physiological constraints, the imaging volume is restricted in size and larger volumes are covered by shifting object and imaging volume relative to each other. This results in reduced temporal resolution, which can lead to motion artifacts when imaging dynamic tracer distributions. A common source of such dynamic distributions are cardiac and respiratory motion in in-vivo experiments, which are in good approximation periodic. We present a raw data processing technique that combines data snippets into virtual frames corresponding to a specific state of the dynamic motion. The technique is evaluated on the basis of measurement data obtained from a rotational phantom at two different rotational frequencies. These frequencies are determined from the raw data without reconstruction and without an additional navigator signal. The reconstructed images give reasonable representations of the rotational phantom frozen in several different states of motion while motion artifacts are suppressed.