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Noise Reduction using Novel Loss Functions to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction on Low Resolution QCT

dc.contributor.authorThomsen, Felix
dc.contributor.authorDelrieux, Claudio A.
dc.contributor.authorPisula, Juan I.
dc.contributor.authorFuertes, José Manuel
dc.contributor.authorLucena, Manuel
dc.contributor.authorGarcía, Rodrigo de Luis
dc.contributor.authorBorggrefe, Jan
dc.date.accessioned2025-01-14T11:50:51Z
dc.date.available2025-01-14T11:50:51Z
dc.date.issued2020-12
dc.description.abstractMicro-structural parameters of the thoracic or lumbar spine generally carry insufficient accuracy and precision for clinical in vivo studies when assessed on quantitative computed tomography (QCT). We propose a 3D convolutional neural network with specific loss functions for QCT noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-truth patches. Comparison of training and test errors revealed high robustness against over-fitting. While not showing effects for the assessment of BMD and voxel-wise densities, the filter improved thoroughly the computation of TMD and BV/TV with respect to the unfiltered data. Root-mean-square and accuracy errors of low resolution TMD and BV/TV decreased to less than $17%$ of the initial values. Furthermore filtered low resolution scans revealed still more TMD- and BV/TV-relevant information than high resolution CT scans, either unfiltered or filtered with two state-of-the-art standard denoising methods. The proposed architecture is threshold and rotational invariant, applicable on a wide range of image resolutions at once, and likely serves for an accurate computation of further micro-structural parameters. Furthermore, it is less prone for over-fitting than neural networks that compute structural parameters directly. In conclusion, the method is potentially important for the diagnosis of osteoporosis and other bone diseases since it allows to assess relevant 3D micro-structural information from standard low exposure CT protocols such as 100mAs and 120kVp.es_ES
dc.description.sponsorshipAgencia Nacional de Promoción Cientı́fica y Tecnológica (ANPCyT) PICT 2017-1731es_ES
dc.identifier.citationFelix S.L. Thomsen, Claudio A. Delrieux, Juan I. Pisula, José M. Fuertes García, Manuel Lucena, Rodrigo de Luis García, Jan Borggrefe, Noise reduction using novel loss functions to compute tissue mineral density and trabecular bone volume fraction on low resolution QCT, Computerized Medical Imaging and Graphics, Volume 86, 2020es_ES
dc.identifier.issn0895-6111es_ES
dc.identifier.otherhttps://doi.org/10.1016/j.compmedimag.2020.101816es_ES
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0895611120301117?via%3Dihubes_ES
dc.identifier.urihttps://hdl.handle.net/10953/3941
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.ispartofComputerized Medical Imaging and Graphicses_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectConvolutional neural networkes_ES
dc.subjectIn vivoes_ES
dc.subjectLocal micro-structurees_ES
dc.subjectPhantom studyes_ES
dc.subjectRegressiones_ES
dc.subject.udc681.3es_ES
dc.titleNoise Reduction using Novel Loss Functions to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction on Low Resolution QCTes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_ES

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