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

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• 作者：Thomy Mertzanidou^a ; ^{t.mertzanidou@cs.ucl.ac.uk} ; John Hipwell^a ; M. Jorge Cardoso^a ; Xiying Zhang^a ; Christine Tanner^b ; Sebastien Ourselin^a ; Ulrich Bick^d ; Henkjan Huisman^c ; Nico Karssemeijer^c ; David Hawkes^a
• 关键词：Multimodal registration ; 2D/3D registration ; Breast tissue classification ; Mammography
• 刊名：Medical Image Analysis
• 出版年：2012
• 期刊代码：90_13618415
• 类别：cp
• 出版时间：July, 2012
• 卷：16
• 期：5
• 页码：966-975
• 文件大小：1048 K

摘要

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 卤 1.6 mm 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 13.1 mm. 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 8.9 mm. 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.