International Journal on Magnetic Particle Imaging IJMPI
Vol. 9 No. 1 Suppl 1 (2023): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2023.2303041

Proceedings Articles

A Denoiser Scaling Technique for Plug-and-Play MPI Reconstruction

Main Article Content

Alper Gungor (Bilkent University), Barış Aşkın (Carnegie Mellon University, Pittsburgh, PA), Damla Alptekin Soydan (Aselsan Research Center), Emine Ulku Saritas (Bilkent University), Can Barış Top (Aselsan Research Center), Tolga Çukur (Bilkent University)

Abstract

Image reconstruction based on the system matrix in magnetic particle imaging (MPI) involves an ill-posed inverse problem, which is often solved using iterative optimization procedures that use regularization. Reconstruction performance is highly dependent on the quality of information captured by the regularization prior. Learning-based methods have been recently introduced that significantly improve prior information in MPI reconstruction. Yet, these methods can perform suboptimally under drifts in the image scale between the training and test sets. In this study, we assess the influence of scale drifts on the performance a recent plug-ang-play method (PP-MPI) that uses a pre-trained denoiser. We introduce a new denoiser scaling technique that improves reliability of PP-MPI against deviations in image scale. The proposed technique enables high quality reconstructions that are robust against scale drifts between training and testing sets.

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