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Lorentz Transmission Electron Microscopy (TEM) observations of magneticnanoparticles contain information on the magnetic and electrostatic potentials.Vector Field Electron Tomography (VFET) can be used to reconstructelectromagnetic potentials of the nanoparticles from their corresponding LTEMimages. The VFET approach is based on the conventional filtered back projectionapproach to tomographic reconstructions and the availability of an incompleteset of measurements due to experimental limitations means that thereconstructed vector fields exhibit significant artifacts. In this paper, weoutline a model-based iterative reconstruction (MBIR) algorithm to reconstructthe magnetic vector potential of magnetic nanoparticles. We combine a forwardmodel for image formation in TEM experiments with a prior model to formulatethe tomographic problem as a maximum a-posteriori probability estimationproblem (MAP). The MAP cost function is minimized iteratively to determine thevector potential. A comparative reconstruction study of simulated as well asexperimental data sets show that the MBIR approach yields quantifiably betterreconstructions than the VFET approach.

3D Reconstruction of the Magnetic Vector Potential using Model Based Iterative Reconstruction