A deblurring model for X-space MPI based on coded calibration scenes

buir.contributor.orcidErgun, Esen| 0000-0001-7601-5290en_US
buir.contributor.orcidSaritas, Emine Ulku | 0000-0001-8551-1077en_US
dc.citation.epage4en_US
dc.citation.issueNumber1 Suppl 1en_US
dc.citation.spage1en_US
dc.citation.volumeNumber8en_US
dc.contributor.authorErgun, Esen
dc.contributor.authorArola, Abdullah Ömer
dc.contributor.authorSaritas, Emine Ulku
dc.contributor.bilkentauthorErgun, Esen
dc.contributor.bilkentauthorArola, Abdullah Ömer
dc.contributor.bilkentauthorSaritas, Emine Ulku
dc.date.accessioned2023-03-01T12:11:11Z
dc.date.available2023-03-01T12:11:11Z
dc.date.issued2022
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractX-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.en_US
dc.identifier.doi10.18416/IJMPI.2022.2203016en_US
dc.identifier.eissn2365-9033
dc.identifier.urihttp://hdl.handle.net/11693/111999
dc.language.isoEnglishen_US
dc.publisherInfinite Science Publishingen_US
dc.relation.isversionofhttps://doi.org/10.18416/IJMPI.2022.2203016en_US
dc.source.titleInternational Journal on Magnetic Particle Imagingen_US
dc.titleA deblurring model for X-space MPI based on coded calibration scenesen_US
dc.typeArticleen_US
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