Breast MRI and X-ray mammography registration using gradient values
dc.contributor.author
dc.date.accessioned
2020-01-28T08:52:44Z
dc.date.available
2020-01-28T08:52:44Z
dc.date.issued
2019-05-01
dc.identifier.issn
1361-8415
dc.identifier.uri
dc.description.abstract
Breast magnetic resonance imaging (MRI) and X-ray mammography are two image modalities widely used for early detection and diagnosis of breast diseases in women. The combination of these modalities, traditionally done using intensity-based registration algorithms, leads to a more accurate diagnosis and treatment, due to the capability of co-localizing lesions and susceptibles areas between the two image modalities. In this work, we present the first attempt to register breast MRI and X-ray mammographic images using intensity gradients as the similarity measure. Specifically, a patient-specific biomechanical model of the breast, extracted from the MRI image, is used to mimic the mammographic acquisition. The intensity gradients of the glandular tissue are directly projected from the 3D MRI volume to the 2D mammographic space, and two different gradient-based metrics are tested to lead the registration, the normalized cross-correlation of the scalar gradient values and the gradient correlation of the vectoral gradients. We compare these two approaches to an intensity-based algorithm, where the MRI volume is transformed to a synthetic computed tomography (pseudo-CT) image using the partial volume effect obtained by the glandular tissue segmentation performed by means of an Expectation-Maximization algorithm. This allows us to obtain the digitally reconstructed radiographies by a direct intensity projection. The best results are obtained using the scalar gradient approach along with a transversal isotropic material model, obtaining a target registration error (TRE), in millimeters, of 5.65 ± 2.76 for CC- and of 7.83 ± 3.04 for MLO-mammograms, while the TRE is 7.33 ± 3.62 in the 3D MRI. We also evaluate the effect of the glandularity of the breast as well as the landmark position on the TRE, obtaining moderated correlation values (0.65 and 0.77 respectively), concluding that these aspects need to be considered to increase the accuracy in further approaches
dc.description.sponsorship
This research has been partially supported from the University of Girona (MPC UdG 2016/022 grant), the European Union within the Marie Sklodowska-Curie Innovative Training Networks (SCARtool project H2020-MSCA-IF-2014, reference 657875) and the Ministry of Economy and Competitiveness of Spain, under project SMARTER (DPI2015-68442-R) and the FPI grant BES-2013-065314
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation
info:eu-repo/grantAgreement/MINECO//DPI2015-68442-R/ES/ANALISIS DE IMAGENES INTELIGENTE PARA LOS RETOS EN EL CRIBADO DE CANCER DE MAMA/
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.media.2019.02.013
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Medical Image Analysis, 2019, vol. 54, p. 76-87
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri
dc.subject
dc.title
Breast MRI and X-ray mammography registration using gradient values
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/657875/EU/Scattered radiation reduction tool to improve computer-aided diagnosis performance in digital breast tomosynthesis/SCARtool
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
029686
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1361-8423