3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning
This work addresses the calibration bottleneck for MPI researchers and practitioners, offering a significant improvement over existing methods.
The authors tackled the time-consuming calibration problem in magnetic particle imaging by proposing a deep learning framework, 3d-SMRnet, which recovers a 3D system matrix with a subsampling factor of 64 in under a minute and outperforms compressed sensing in matrix quality, image quality, and processing time.
Magnetic particle imaging (MPI) data is commonly reconstructed using a system matrix acquired in a time-consuming calibration measurement. The calibration approach has the important advantage over model-based reconstruction that it takes the complex particle physics as well as system imperfections into account. This benefit comes for the cost that the system matrix needs to be re-calibrated whenever the scan parameters, particle types or even the particle environment (e.g. viscosity or temperature) changes. One route for reducing the calibration time is the sampling of the system matrix at a subset of the spatial positions of the intended field-of-view and employing system matrix recovery. Recent approaches used compressed sensing (CS) and achieved subsampling factors up to 28 that still allowed reconstructing MPI images of sufficient quality. In this work, we propose a novel framework with a 3d-System Matrix Recovery Network and demonstrate it to recover a 3d system matrix with a subsampling factor of 64 in less than one minute and to outperform CS in terms of system matrix quality, reconstructed image quality, and processing time. The advantage of our method is demonstrated by reconstructing open access MPI datasets. The model is further shown to be capable of inferring system matrices for different particle types.