Homogenization of breast MRI across imaging centers and feature analysis using unsupervised deep embedding.

We propose an intensity-based technique to homogenize dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data acquired at six institutions. A total of 234 T1-weighted MRI volumes acquired at the peak kinetic curve were obtained for study of the homogenization and unsupervised deep-learning feature extraction techniques. The homogenization uses reference regions of adipose breast tissue since they are less susceptible to variations due to cancer and contrast medium. For the homogenization, the moments of the distribution of reference pixel intensities across the cases were matched and the remaining intensity distributions were matched accordingly. A deep stacked autoencoder with six convolutional layers was trained to reconstruct a 128x128 MRI slice and to extract a latent space of 1024 dimensions. We used the latent space from the stacked autoencoder to extract deep embedding features that represented the global and local structures of the imaging data. An analysis using spectral embedding of the latent space shows that, before homogenization the dominating factor was the dependency on the imaging center; after homogenization the histograms of the cases between different centers were matched and the center dependency was reduced. The results of feature analysis indicate that the proposed homogenization approach may lessen the effects of different imaging protocols and scanners in MRI, which may then allow more consistent quantitative analysis of radiomic information across patients and improve the generalizability of machine learning methods across different clinical sites. Further study is underway to evaluate the performance of machine learning models with and without image homogenization.