  Background signals are a primary source of artifacts in magnetic particle
imaging and limit the sensitivity of the method since background signals are
often not precisely known and vary over time. The state-of-the art method for
handling background signals uses one or several background calibration
measurements with an empty scanner bore and subtracts a linear combination of
these background measurements from the actual particle measurement. This
approach yields satisfying results in case that the background measurements are
taken in close proximity to the particle measurement and when the background
signal drifts linearly. In this work, we propose a joint estimation of particle
distribution and background signal based on a dictionary that is capable of
representing typical background signals and allows for precise estimation of
the background even when the latter is drifting non-linearly over time. Using a
singular-value decomposition, the dictionary is derived from a large number of
background calibration scans that do not need to be recorded in close proximity
to the particle measurement. The dictionary is sufficiently expressive and
represented by its principle components. The proposed joint estimation of
particle distribution and background signal is expressed as a linear
Tikhonov-regularized least squares problem, which can be efficiently solved. In
phantom experiments it is shown that the method strongly suppresses background
artifacts and even allows to estimate and remove the direct feed-through of the
excitation field.
