A denoiser scaling technique for plug-and-play MPI reconstruction

Date
2023-03-19
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
International Journal on Magnetic Particle Imaging
Print ISSN
Electronic ISSN
2365-9033
Publisher
Infinite Science Publishing
Volume
9
Issue
1
Pages
2303041-1 - 2303041-4
Language
en
Journal Title
Journal ISSN
Volume Title
Series
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.

Course
Other identifiers
Book Title
Keywords
Citation
Published Version (Please cite this version)