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Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures

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Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures

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dc.contributor.author Larroza, Andrés es_ES
dc.contributor.author Moliner, Laura es_ES
dc.contributor.author Álvarez-Gómez, Juan Manuel es_ES
dc.contributor.author Oliver-Gil, Sandra es_ES
dc.contributor.author Espinós-Morató, Héctor es_ES
dc.contributor.author Vergara-Díaz, Marina es_ES
dc.contributor.author Rodríguez-Álvarez, María J. es_ES
dc.date.accessioned 2021-07-21T10:39:05Z
dc.date.available 2021-07-21T10:39:05Z
dc.date.issued 2019-11-02 es_ES
dc.identifier.isbn 978-1-7281-4164-0 es_ES
dc.identifier.issn 2577-0829 es_ES
dc.identifier.uri http://hdl.handle.net/10251/169674
dc.description.abstract [EN] Synthetic computed tomography (CT) images derived from magnetic resonance images (MRI) are of interest for radiotherapy planning and positron emission tomography (PET) attenuation correction. In recent years, deep learning implementations have demonstrated improvement over atlasbased and segmentation-based methods. Nevertheless, several open questions remain to be addressed, such as which is the best of MRI sequences and neural network architectures. In this work, we compared the performance of different combinations of two common MRI sequences (T1- and T2-weighted), and three state-of-the-art neural networks designed for medical image processing (Vnet, HighRes3dNet and ScaleNet). The experiments were conducted on brain datasets from a public database. Our results suggest that T1 images perform better than T2, but the results further improve when combining both sequences. The lowest mean average error over the entire head (MAE = 101.76 ± 10.4 HU) was achieved combining T1 and T2 scans with HighRes3dNet. All tested deep learning models achieved significantly lower MAE (p < 0.01) than a well-known atlas-based method. es_ES
dc.description.sponsorship This work was supported by the Spanish Government grants TEC2016-79884-C2 and RTC-2016-5186-1, and by the European Union through the European Regional Development Fund (ERDF) es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.relation.ispartof 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures es_ES
dc.type Comunicación en congreso es_ES
dc.type Artículo es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1109/NSS/MIC42101.2019.9060051 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TEC2016-79884-C2-2-R/ES/DESARROLLO DEL SOFTWARE PARA SISTEMA DE DIAGNOSTICO POR IMAGEN MOLECULAR PARA CORAZON EN CONDICIONES DE STRESS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTC-2016-5186-1/ES/Control objetivo del deterioro cognitivo mediante análisis de imagen de amiloide/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.description.bibliographicCitation Larroza, A.; Moliner, L.; Álvarez-Gómez, JM.; Oliver-Gil, S.; Espinós-Morató, H.; Vergara-Díaz, M.; Rodríguez-Álvarez, MJ. (2019). Deep learning for MRI-based CT synthesis: a comparison of MRI sequences and neural network architectures. IEEE. 1-4. https://doi.org/10.1109/NSS/MIC42101.2019.9060051 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2019) es_ES
dc.relation.conferencedate Octubre 26-Noviembre 02,2019 es_ES
dc.relation.conferenceplace Manchester, UK es_ES
dc.relation.publisherversion https://doi.org/10.1109/NSS/MIC42101.2019.9060051 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 4 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\411124 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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