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Ensemble of neural networks for 3D position estimation in monolithic PET detectors

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Ensemble of neural networks for 3D position estimation in monolithic PET detectors

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dc.contributor.author Iborra Carreres, Amadeo es_ES
dc.contributor.author González Martínez, Antonio Javier es_ES
dc.contributor.author González, A. es_ES
dc.contributor.author Bousse, A. es_ES
dc.contributor.author Visvikis, Dimitris es_ES
dc.date.accessioned 2020-05-30T03:30:51Z
dc.date.available 2020-05-30T03:30:51Z
dc.date.issued 2019-10-04 es_ES
dc.identifier.issn 0031-9155 es_ES
dc.identifier.uri http://hdl.handle.net/10251/144674
dc.description.abstract [EN] We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality of our approach is to exploit simulations to obtain reference data, in combination with a variability reduction that the network ensembles offer, thus, removing the need of extensive per-detector calibration measurements. This procedure delivers an ensemble valid for any detector of the same design. We show the capability of the ensemble to solve the 3D positioning problem through testing four different detector designs with Monte Carlo data, measurements from physical detectors and reconstructed images from the MindView scanner. Network ensembles allow the detector to achieve a 2-2.4 mm FWHM, depending on its design, and the associated reconstructed images present improved SNR, CNR and SSIM when compared to those based on the MindView built-in positioning algorithm. es_ES
dc.language Inglés es_ES
dc.publisher IOP Publishing es_ES
dc.relation.ispartof Physics in Medicine and Biology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Positron-emission tomography es_ES
dc.subject Monolithic PET detectors es_ES
dc.subject Ensemble of neural networks es_ES
dc.subject Monte Carlo generated training es_ES
dc.subject Interaction position determination es_ES
dc.subject Depth of interaction determination es_ES
dc.title Ensemble of neural networks for 3D position estimation in monolithic PET detectors es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1088/1361-6560/ab3b86 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/603002/EU/Multimodal Imaging of Neurological Disorders/ es_ES
dc.rights.accessRights Abierto 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 Iborra Carreres, A.; González Martínez, AJ.; González, A.; Bousse, A.; Visvikis, D. (2019). Ensemble of neural networks for 3D position estimation in monolithic PET detectors. Physics in Medicine and Biology. 64(19):1-20. https://doi.org/10.1088/1361-6560/ab3b86 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1088/1361-6560/ab3b86 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 20 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 64 es_ES
dc.description.issue 19 es_ES
dc.identifier.pmid 31416053 es_ES
dc.relation.pasarela S\406055 es_ES
dc.contributor.funder European Commission es_ES
dc.description.references Agostinelli, S., Allison, J., Amako, K., Apostolakis, J., Araujo, H., Arce, P., … Barrand, G. (2003). Geant4—a simulation toolkit. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 506(3), 250-303. doi:10.1016/s0168-9002(03)01368-8 es_ES
dc.description.references Anger, H. O. (1958). Scintillation Camera. Review of Scientific Instruments, 29(1), 27-33. doi:10.1063/1.1715998 es_ES
dc.description.references Borghi, G., Tabacchini, V., Seifert, S., & Schaart, D. R. (2015). Experimental Validation of an Efficient Fan-Beam Calibration Procedure for <formula formulatype=«inline»><tex Notation=«TeX»>$k$</tex></formula>-Nearest Neighbor Position Estimation in Monolithic Scintillator Detectors. IEEE Transactions on Nuclear Science, 62(1), 57-67. doi:10.1109/tns.2014.2375557 es_ES
dc.description.references Bruyndonckx, P., Lemaitre, C., van der Laan, D. J., Maas, M., Schaart, D., Yonggang, W., … Tavernier, S. (2008). Evaluation of Machine Learning Algorithms for Localization of Photons in Undivided Scintillator Blocks for PET Detectors. IEEE Transactions on Nuclear Science, 55(3), 918-924. doi:10.1109/tns.2008.922811 es_ES
dc.description.references Bruyndonckx, P., Leonard, S., Tavernier, S., Lemaitre, C., Devroede, O., Yibao Wu, & Krieguer, M. (2004). Neural network-based position estimators for PET detectors using monolithic LSO blocks. IEEE Transactions on Nuclear Science, 51(5), 2520-2525. doi:10.1109/tns.2004.835782 es_ES
dc.description.references González, A. J., Conde, P., Iborra, A., Aguilar, A., Bellido, P., García-Olcina, R., … Benlloch, J. M. (2015). Detector block based on arrays of 144 SiPMs and monolithic scintillators: A performance study. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 787, 42-45. doi:10.1016/j.nima.2014.10.078 es_ES
dc.description.references Gonzalez, A. J., Pincay, E. J., Canizares, G., Lamprou, E., Sanchez, S., Catret, J. V., … Correcher, C. (2019). Initial Results of the MINDView PET Insert Inside the 3T mMR. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(3), 343-351. doi:10.1109/trpms.2018.2866899 es_ES
dc.description.references Gonzalez-Montoro, A., Benlloch, J. M., Gonzalez, A. J., Aguilar, A., Canizares, G., Conde, P., … Sanchez, F. (2017). Performance Study of a Large Monolithic LYSO PET Detector With Accurate Photon DOI Using Retroreflector Layers. IEEE Transactions on Radiation and Plasma Medical Sciences, 1(3), 229-237. doi:10.1109/trpms.2017.2692819 es_ES
dc.description.references Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), 993-1001. doi:10.1109/34.58871 es_ES
dc.description.references Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), 251-257. doi:10.1016/0893-6080(91)90009-t es_ES
dc.description.references Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366. doi:10.1016/0893-6080(89)90020-8 es_ES
dc.description.references Jackson, C., O’Neill, K., Wall, L., & McGarvey, B. (2014). High-volume silicon photomultiplier production, performance, and reliability. Optical Engineering, 53(8), 081909. doi:10.1117/1.oe.53.8.081909 es_ES
dc.description.references Jaouen, V., Bert, J., Boussion, N., Fayad, H., Hatt, M., & Visvikis, D. (2019). Image Enhancement With PDEs and Nonconservative Advection Flow Fields. IEEE Transactions on Image Processing, 28(6), 3075-3088. doi:10.1109/tip.2018.2881838 es_ES
dc.description.references Lecoq, P. (2016). Development of new scintillators for medical applications. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 809, 130-139. doi:10.1016/j.nima.2015.08.041 es_ES
dc.description.references Llosá, G., Barrillon, P., Barrio, J., Bisogni, M. G., Cabello, J., Del Guerra, A., … de La Taille, C. (2013). High performance detector head for PET and PET/MR with continuous crystals and SiPMs. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 702, 3-5. doi:10.1016/j.nima.2012.08.099 es_ES
dc.description.references Llosá, G., Barrio, J., Lacasta, C., Bisogni, M. G., Guerra, A. D., Marcatili, S., … Piemonte, C. (2010). Characterization of a PET detector head based on continuous LYSO crystals and monolithic, 64-pixel silicon photomultiplier matrices. Physics in Medicine and Biology, 55(23), 7299-7315. doi:10.1088/0031-9155/55/23/008 es_ES
dc.description.references Maas, M. C., Schaart, D. R., van der Laan, D. J. (Jan), Bruyndonckx, P., Lemaître, C., Beekman, F. J., & van Eijk, C. W. E. (2009). Monolithic scintillator PET detectors with intrinsic depth-of-interaction correction. Physics in Medicine and Biology, 54(7), 1893-1908. doi:10.1088/0031-9155/54/7/003 es_ES
dc.description.references Marcinkowski, R., Mollet, P., Van Holen, R., & Vandenberghe, S. (2016). Sub-millimetre DOI detector based on monolithic LYSO and digital SiPM for a dedicated small-animal PET system. Physics in Medicine and Biology, 61(5), 2196-2212. doi:10.1088/0031-9155/61/5/2196 es_ES
dc.description.references Muller, F., Schug, D., Hallen, P., Grahe, J., & Schulz, V. (2018). Gradient Tree Boosting-Based Positioning Method for Monolithic Scintillator Crystals in Positron Emission Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences, 2(5), 411-421. doi:10.1109/trpms.2018.2837738 es_ES
dc.description.references Muller, F., Schug, D., Hallen, P., Grahe, J., & Schulz, V. (2019). A Novel DOI Positioning Algorithm for Monolithic Scintillator Crystals in PET Based on Gradient Tree Boosting. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(4), 465-474. doi:10.1109/trpms.2018.2884320 es_ES
dc.description.references Nayar, S. K., Ikeuchi, K., & Kanade, T. (1991). Surface reflection: physical and geometrical perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7), 611-634. doi:10.1109/34.85654 es_ES
dc.description.references Pani, R., Vittorini, F., Cinti, M. N., Bennati, P., Pellegrini, R., Ridolfi, S., … Sacco, D. (2009). Revisited position arithmetics for LaBr3:Ce continuous crystals. Nuclear Physics B - Proceedings Supplements, 197(1), 383-386. doi:10.1016/j.nuclphysbps.2009.10.109 es_ES
dc.description.references Schaart, D. R., van Dam, H. T., Seifert, S., Vinke, R., Dendooven, P., Löhner, H., & Beekman, F. J. (2009). A novel, SiPM-array-based, monolithic scintillator detector for PET. Physics in Medicine and Biology, 54(11), 3501-3512. doi:10.1088/0031-9155/54/11/015 es_ES
dc.description.references Van Dam, H. T., Seifert, S., Vinke, R., Dendooven, P., Lohner, H., Beekman, F. J., & Schaart, D. R. (2011). Improved Nearest Neighbor Methods for Gamma Photon Interaction Position Determination in Monolithic Scintillator PET Detectors. IEEE Transactions on Nuclear Science, 58(5), 2139-2147. doi:10.1109/tns.2011.2150762 es_ES
dc.description.references Van der Laan, D. J. (Jan), Schaart, D. R., Maas, M. C., Beekman, F. J., Bruyndonckx, P., & van Eijk, C. W. E. (2010). Optical simulation of monolithic scintillator detectors using GATE/GEANT4. Physics in Medicine and Biology, 55(6), 1659-1675. doi:10.1088/0031-9155/55/6/009 es_ES
dc.description.references Wang, Y., Zhu, W., Cheng, X., & Li, D. (2013). 3D position estimation using an artificial neural network for a continuous scintillator PET detector. Physics in Medicine and Biology, 58(5), 1375-1390. doi:10.1088/0031-9155/58/5/1375 es_ES
dc.description.references Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.819861 es_ES


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