Abstract
X-ray mammography and breast Magnetic Resonance Imaging (MRI) are two principal imaging modalities which are currently used for detection and diagnosis of breast disease in women. Since these imaging modalities exploit different contrast mechanisms, establishing spatial correspondence between mammograms and volumetric breast MRI scans is expected to aid the assessment and quantification of different type of breast malignancies. Finding such correspondence is, unfortunately, far from being a trivial problem – not only that the images have different contrasts and dimensionality, they are also acquired under vastly different physical conditions. As opposed to many complex standard methods relying on patient-specific bio-mechanical modelling, we developed a new simple approach to find the correspondences. This paper introduces a two-stage computational scheme which estimates the global (compression dependent) part of the spatial transformation first, followed by estimating the residual (tissue dependent) part of the transformation of much smaller magnitude. Experimental results on a clinical data-set, containing 10 subjects, validated the efficiency of the proposed approach. The average Target Registration Error (TRE) on the data-set is 5.44 mm with a standard deviation of 3.61 mm.
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Soleimani, H., Michailovich, O.V. (2020). 2D X-Ray Mammogram and 3D Breast MRI Registration. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_15
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DOI: https://doi.org/10.1007/978-3-030-59725-2_15
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