  Magnetic particle imaging (MPI) offers exceptional contrast for magnetic
nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in
MPI starts with a calibration scan to measure the system matrix (SM), which is
then used to set up an inverse problem to reconstruct images of the MNP
distribution during subsequent scans. This calibration enables the
reconstruction to sensitively account for various system imperfections. Yet
time-consuming SM measurements have to be repeated under notable changes in
system properties. Here, we introduce a novel deep learning approach for
accelerated MPI calibration based on Transformers for SM super-resolution
(TranSMS). Low-resolution SM measurements are performed using large MNP samples
for improved signal-to-noise ratio efficiency, and the high-resolution SM is
super-resolved via model-based deep learning. TranSMS leverages a vision
transformer module to capture contextual relationships in low-resolution input
images, a dense convolutional module for localizing high-resolution image
features, and a data-consistency module to ensure measurement fidelity.
Demonstrations on simulated and experimental data indicate that TranSMS
significantly improves SM recovery and MPI reconstruction for up to 64-fold
acceleration in two-dimensional imaging.
