MPISuperRes-PnP: a super-resolution zero-shot plug-and-play reconstruction algorithm for magnetic particle imaging.
TL;DR
A super-resolution method for MPI based on a plug-and-play approach using a pre-trained denoiser in zero-shot fashion that incorporates benefits of deep learning without training and avoids the need of training data is derived.
Abstract
OBJECTIVE Magnetic Particle Imaging (MPI) is a promising, emerging medical imaging modality. MPI is based on the non-linear response of magnetic nanoparticles to an applied magnetic field and does not expose the specimen to ionizing radiation. The measured signal is the voltage induced in receive coils by the particles' response. Reconstructing the particle concentration from the signal constitutes the imaging task. Even using state-of-the-art measurement-based reconstruction, the associated spatial grid is very coarse, hence super-resolution techniques are important. In this work, we propose an approach for super-resolution in MPI inspired by energy minimization. Approach. Different methods have been proposed for super-resolution in MPI, ranging from upscaling of the associated system matrix to interpolation of the reconstruction. Here we incorporate super-resolution into the reconstruction task via an energy minimization formulation. Following the plug-and-play approach to energy minimization we derive a splitting scheme where the arising Gaussian denoising task is treated with a pre-trained learned Gaussian denoiser. Main results. We derive a super-resolution method for MPI based on a plug-and-play approach using a pre-trained denoiser in zero-shot fashion. This way, we incorporate benefits of deep learning without training and avoid the need of training data. Further, we provide a quantitative and qualitative evaluation of the proposed method on simulated data with realistic noise. Hyper-parameters are selected via an extended parameter search. The found parameters are applied for reconstruction on real data. We qualitatively show the applicability of our method on real data (MPIData: EquilibriumModelWithAnisotropy and 2D-OpenMPI Data). Significance. The proposed method employs a deep-learning denoiser without training -- thus it does not require presently scarcely available MPI training data. The denoiser behaves conservatively, i.e., no hallucination artifacts were observed. The super-resolution approach is generic such that it can be applied in future MPI contexts involving different regularizers or different imaging tasks.