MRI to X-ray mammography registration using a volume-preserving affine transformation

• Published in: Medical image analysis Vol. 16; no. 5; pp. 966 - 975
• Main Authors: Mertzanidou, Thomy, Hipwell, John, Cardoso, M. Jorge, Zhang, Xiying, Tanner, Christine, Ourselin, Sebastien, Bick, Ulrich, Huisman, Henkjan, Karssemeijer, Nico, Hawkes, David
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2012
• Subjects: 2D/3D registration; Algorithms; Breast Neoplasms - diagnosis; Breast tissue classification; Female; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Mammography; Mammography - methods; Multimodal registration; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; 2D/3D registration; Mammography; Breast tissue classification; Multimodal registration
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2012.03.001

[Display omitted] ► Presents a framework for MRI to X-ray mammography registration for clinical use. ► Incorporates an EM-MRF algorithm for breast tissue classification. ► Optimisation of a volume-preserving, affine transformation model. ► Demonstration that an intensity-based approach produces clinically useful accuracy. ► Median accuracy of 13.1mm when tested on 113 registrations of clinical data sets. X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis.