International Journal on Magnetic Particle Imaging
Vol 8 No 1 Suppl 1 (2022): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2022.2203016

Proceedings Articles

A Deblurring Model for X-space MPI Based on Coded Calibration Scenes

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Esen Ergun  (Bilkent University, Ankara, Turkey), Abdullah Ömer Arol  (Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey), Emine Ulku Saritas  (Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey), Tolga Çukur  (Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

X-space reconstructions suffer from blurring caused by the point spread function (PSF) of the Magnetic Particle Imaging (MPI) system. Here, we propose a deep learning method for deblurring x-space reconstructed images. Our proposed method learns an end-to-end mapping between the gridding-reconstructed collinear images from two partitions of a Lissajous trajectory and the underlying magnetic nanoparticle (MNP) distribution. This nonlinear mapping is learned using measurements from a coded calibration scene (CCS) to speed up the training process. Numerical experiments show that our learning-based method can successfully deblur x-space reconstructed images across a broad range of measurement signal-to-noise ratios (SNR) following training at a moderate SNR.

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