A deblurring model for X-space MPI based on coded calibration scenes
buir.contributor.author | Ergun, Esen | |
buir.contributor.author | Arola, Abdullah Ömer | |
buir.contributor.author | Saritas, Emine Ulku | |
buir.contributor.orcid | Ergun, Esen| 0000-0001-7601-5290 | |
buir.contributor.orcid | Saritas, Emine Ulku | 0000-0001-8551-1077 | |
dc.citation.epage | 4 | en_US |
dc.citation.issueNumber | 1 Suppl 1 | en_US |
dc.citation.spage | 1 | en_US |
dc.citation.volumeNumber | 8 | en_US |
dc.contributor.author | Ergun, Esen | |
dc.contributor.author | Arola, Abdullah Ömer | |
dc.contributor.author | Saritas, Emine Ulku | |
dc.date.accessioned | 2023-03-01T12:11:11Z | |
dc.date.available | 2023-03-01T12:11:11Z | |
dc.date.issued | 2022 | |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.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. | en_US |
dc.description.provenance | Submitted by Mandana Moftakhari (mandana.mir@bilkent.edu.tr) on 2023-03-01T12:11:11Z No. of bitstreams: 1 A_deblurring_model_for_X-space_MPI_based_on_coded_calibration_scenes.pdf: 1447728 bytes, checksum: dc2310524b9efa490a28f29356bd2ee0 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2023-03-01T12:11:11Z (GMT). No. of bitstreams: 1 A_deblurring_model_for_X-space_MPI_based_on_coded_calibration_scenes.pdf: 1447728 bytes, checksum: dc2310524b9efa490a28f29356bd2ee0 (MD5) Previous issue date: 2022 | en |
dc.identifier.doi | 10.18416/IJMPI.2022.2203016 | en_US |
dc.identifier.eissn | 2365-9033 | |
dc.identifier.uri | http://hdl.handle.net/11693/111999 | |
dc.language.iso | English | en_US |
dc.publisher | Infinite Science Publishing | en_US |
dc.relation.isversionof | https://doi.org/10.18416/IJMPI.2022.2203016 | en_US |
dc.source.title | International Journal on Magnetic Particle Imaging | en_US |
dc.title | A deblurring model for X-space MPI based on coded calibration scenes | en_US |
dc.type | Article | en_US |
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