TranSMS: transformers for super-resolution calibration in magnetic particle imaging

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buir.contributor.authorGungor, Alper
buir.contributor.authorAskin, Baris
buir.contributor.authorSaritas, Emine Ulku
buir.contributor.authorÇukur, Tolga
buir.contributor.orcidÇukur, Tolga|0000-0002-2296-851X
buir.contributor.orcidSaritas, Emine Ulku|0000-0001-8551-1077
buir.contributor.orcidGungor, Alper|0000-0002-3043-9124
dc.citation.epage3574en_US
dc.citation.issueNumber12en_US
dc.citation.spage3562en_US
dc.citation.volumeNumber41en_US
dc.contributor.authorGungor, Alper
dc.contributor.authorAskin, Baris
dc.contributor.authorSoydan, D.A.
dc.contributor.authorSaritas, Emine Ulku
dc.contributor.authorTop, C. B.
dc.contributor.authorÇukur, Tolga
dc.date.accessioned2023-02-16T07:47:35Z
dc.date.available2023-02-16T07:47:35Z
dc.date.issued2022-07-11
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.description.abstractMagnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imagingen_US
dc.description.provenanceSubmitted by Fatma Kaya (fattttoky.55@gmail.com) on 2023-02-16T07:47:35Z No. of bitstreams: 1 TranSMS_transformers_for_super-resolution_calibration_in_magnetic_particle_imaging.pdf: 6679297 bytes, checksum: aefd96807fcaaf9a5031432494f844bf (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-16T07:47:35Z (GMT). No. of bitstreams: 1 TranSMS_transformers_for_super-resolution_calibration_in_magnetic_particle_imaging.pdf: 6679297 bytes, checksum: aefd96807fcaaf9a5031432494f844bf (MD5) Previous issue date: 2022-07-11en
dc.identifier.doi10.1109/TMI.2022.3189693en_US
dc.identifier.eissn1558-254X
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/11693/111402
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttps://www.doi.org/10.1109/TMI.2022.3189693en_US
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.subjectMagnetic particle imagingen_US
dc.subjectCalibrationen_US
dc.subjectSystem matrixen_US
dc.subjectReconstructionen_US
dc.subjectTransformeren_US
dc.subjectDeep learningen_US
dc.titleTranSMS: transformers for super-resolution calibration in magnetic particle imagingen_US
dc.typeArticleen_US

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