Exploring Symmetries in Breast MRI Scans

R. Alterson, D. B. Plewes Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada Synopsis: Currently, breast MR images are assessed in terms of the kinetics and location of uptake of Gd-DTPA. However, mammographic interpretation often uses symmetry between left and right breasts to indicate the site of potential tumour masses but has not been used in breast MRI. In this work, we present such a method for characterizing breast symmetry based on three objective measures of similarity including multiresolution non-orthogonal wavelet representation, three-dimensional intensity distributions and concurrence matrices. Studies based on 51 normal MRI scans of randomly selected patients showed that the sensitivity of symmetry detection rate approached 94%. Introduction: Symmetry between breasts has been used for many years in X-ray mammography to aid in the detection of disease. Typically, suspicious areas in one breast are compared, based on human visual perception, with the same area in the opposite breast and any differences may be reflective of disease processes. However, the use of symmetry in mammography is fundamentally limited by the fact that mammography is a 2D projection imaging method. Alternatively, MRI breast imaging offers several advantages, as it is a high resolution, 3D imaging method that exhibits very high contrast between normal breast tissue entities. Currently, breast MRI is based on the use of Gd-DTPA enhancement reflective of tumour angiogenesis but virtually no use is made of breast symmetry in image interpretation. In this work, we explore the question of symmetry between a patient’s left and right breast in three-dimensional MRI scans. The purpose of this study was to verify that symmetry, in terms of volume, texture or structure, could be demonstrated in 3D breast MRI scans. Another objective is to develop a set of similarity metrics that can be used for longitudinal studies and for computer aided tracking of changes in breast tissue. Methods: To assess breast symmetry, we employed objective measures, based on theoretical models for image comparison. One approach was based on the use of nonorthogonal Gabor wavelets to characterize texture [1]. Theses wavelets have biological counterparts in the cortical human visual systems as they can effectively capture orientation and spatial frequency qualities [2]. In addition, we employed concurrence matrices and also intensity distributions to represent the volumes of fiberglandular tissue and fat [3]. For evaluation purposes we randomly selected 51 normal patients who had previously obtained bi-lateral breast MRI from a large population screening study. T1-weighted, sagittal images obtained prior to contrast enhancement by Gd-DTPA were used in the analysis (Figure 1). Results: The outputs of the different similarity measures were used to produce statistics pertaining to the degree of symmetry detected a breast and those from the entire population including the patient’s contra-lateral breast. Figure 2 illustrates, for example, Gabor magnitude outputs at six different orientations as applied to a single MRI breast scan. Detection rates for each of the comparison methods as well as the combined results are presented in table 1 (the closest match group threshold was set to the closest 4 patients or roughly 8%). We obtained a detection rate of the patient’s contra-lateral breast with a combined sensitivity approaching 94%. Discussion: These data demonstrate that automated symmetry detection of breast symmetry is both feasible and accurate. While preliminary, these data suggest that improved detection accuracy may be possible beyond standard means to breast MRI interpretation. Further, the methods presented here can be used for longitudinal studies by applying them to breast MRI scans taken at different times. References: [1] D. Dunn, W.E. Higgins, and J. Wakeley, “Texture segmentation using 2-D Gabor elementary functions”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, pp. 130-149, February 1994. [2] P. Jones and L.A. Palmer, “An evaluation of the two-dimensional Gabor model of simple receptive fields in cat striate cortex”, Journal of Neurophysiology, vol. 58, no. 6, pp. 1187-1211, December 1987. [3] A. Pitiot, A.W. Toga, N. Ayache and P.M. Thompson, “Texture-Based MRI Segmentation with a Two-Stage Hybrid Neural Classifier”, IEEE 2002 World Congress on Computational Intelligence and Neural Nets, Honolulu, HI, May 12-17, 2002.