15 citations · 25 across the 3 of their papers we have counts for
4 papers
Deep Learning for Restoring MPI System Matrices Using Simulated Training Data
Artyom Tsanda, Sarah Reiss, Konrad Scheffler +2
Magnetic particle imaging reconstructs tracer distributions using a system matrix obtained through time-consuming, noise-prone calibration measurements. Methods for addressing impe…
Efficient Chebyshev Reconstruction for the Anisotropic Equilibrium Model in Magnetic Particle Imaging
Christine Droigk, Daniel Hernández Durán, Marco Maass +2
Magnetic Particle Imaging (MPI) is a tomographic imaging modality capable of real-time, high-sensitivity mapping of superparamagnetic iron oxide nanoparticles. Model-based image re…
Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging
Marco Maass, Tobias Kluth, Christine Droigk +4
Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolut…
Characterization of the Clinically Approved MRI Tracer Resotran for Magnetic Particle Imaging in a Comparison Study
Fabian Mohn, Konrad Scheffler, Justin Ackers +7
Objective. The availability of magnetic nanoparticles with medical approval for human intervention is fundamental to the clinical translation of magnetic particle imaging (MPI). In…