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Cited by 26Medical Image Segmentation TechniquesDigital Radiography and Breast ImagingMRI in cancer diagnosis

Breast MRI and X-ray mammography registration using gradient values

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García, Eloy, Diez, Yago, Diaz, Oliver, Lladó, Xavier, Gubern-Mérida, Albert, Martí, Robert, Martí, Joan, Oliver, Arnau·2019·Medical Image Analysis
DOI 10.1016/j.media.2019.02.013
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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.

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  • Registration between breast MRI and X-ray mammography is feasible through a volume-preserving affine transformation model or gradient-based registration methods, reaching accuracy on the order of 10 mm.

    “The registration between breast MRI and X-ray mammography has shown to be feasible through a properly defined volume-preserving affine transformation model (Mertzanidou et al. 2012) or through gradient-based registration methods (García et al. 2019). Both algorithms are able to reach registration accuracy in the order of 10mm.”

    From Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualisation of Bone Defects (2023) · cites this paper as García et al. 2019

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Cited by

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Authors
García, E., Diez, Y., Diaz, O., Lladó, X., Gubern-Mérida, A., Martí, R., Martí, J., & Oliver, A.
Affiliations
Universitat de Girona (ES)
Corresponding author
García, Eloy
ORCID
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2019-05
Volume
54
Pages
76-87
ISSN
1361-8415 (ISSN-L)
DOI
10.1016/j.media.2019.02.013
OpenAlex
W2917571395
Cited by
26
References
0
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 8 · 2022: 1 · 2021: 2 · 2020: 8 · 2019: 3
FWCI
1.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Medical Image Analysis (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Universitat de Girona Digital Repository (Universitat de Girona) (acceptedVersion, cc-by-nc-nd)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
  • RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, other-oa)
Funders
University of Girona (MPC UdG 2016/022)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Digital Radiography and Breast Imaging
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
Keywords
breast MRI, mammography, multimodal image registration, intensity gradient, gradient correlation, normalized cross-correlation, pseudo-CT, expectation-maximization algorithm, target registration error
MeSH terms
Algorithms, Breast, Breast Neoplasms, Contrast Media, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Mammography, Artifacts, Imaging, Three-Dimensional, Anatomic Landmarks, Multimodal Imaging

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