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Background signals are a primary source of artifacts in magnetic particleimaging and limit the sensitivity of the method since background signals areoften not precisely known and vary over time. The state-of-the art method forhandling background signals uses one or several background calibrationmeasurements with an empty scanner bore and subtracts a linear combination ofthese background measurements from the actual particle measurement. Thisapproach yields satisfying results in case that the background measurements aretaken in close proximity to the particle measurement and when the backgroundsignal drifts linearly. In this work, we propose a joint estimation of particledistribution and background signal based on a dictionary that is capable ofrepresenting typical background signals and allows for precise estimation ofthe background even when the latter is drifting non-linearly over time. Using asingular-value decomposition, the dictionary is derived from a large number ofbackground calibration scans that do not need to be recorded in close proximityto the particle measurement. The dictionary is sufficiently expressive andrepresented by its principle components. The proposed joint estimation ofparticle distribution and background signal is expressed as a linearTikhonov-regularized least squares problem, which can be efficiently solved. Inphantom experiments it is shown that the method strongly suppresses backgroundartifacts and even allows to estimate and remove the direct feed-through of theexcitation field.

Efficient Joint Estimation of Tracer Distribution and Background Signals in Magnetic Particle Imaging using a Dictionary Approach