Open Source Software for Medical Imaging

Medical imaging requires an enormous amount of expert knowledge in signal processing, image reconstruction and image processing. At our institute we develop imaging algorithms using the open source scientific programming language Julia and make them available via the collaborative version control platform GitHub under the MIT license. This enables the documentation of scientific methodology and ensures the reproducibility of our own research contributions. Furthermore, even scientists with only rudimentary knowledge of medical imaging are enabled to use state-of-the-art image reconstruction methods. We are happy about any feedback / suggestions that can be send by email to us. Improvements and amendments can be also directly made on GitHub.

All MPI related projects are collected in the MagneticParticleImaging organization, while the institute's contributions to MRI are collected in the MagneticResonanceImaging organization. Projects, which cannot be assigned clearly to an imaging method, are maintained JuliaImageRecon organization, where are our main contributions are:

Furthermore, some individual packages are mainteined under the account of Tobias Knopp and the IBIResearch organization, where our main contributions are:

Magnetic Particle Imaging
Magnetic Resonance Imaging
Further Projects

Publications

[191175]
Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.
Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. (2024).
Volume: <strong>10</strong>. Number: (1 Suppl 1),
on pages: 1-4
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DOI: https://doi.org/10.18416/IJMPI.2024.2403028
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Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers (ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.