Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks

TL;DR

This paper introduces a cycle-consistent GAN-based method for normalizing breast MRI images from different scanners, preserving breast structures while standardizing intensity and noise, to improve diagnostic algorithm generalization.

Contribution

The study presents a novel MRI normalization technique using CycleGAN with mutual information loss and a modified discriminator to preserve breast details across scanner types.

Findings

Successfully learned bidirectional mapping between GE and Siemens MRIs

Preserved breast structures and details during normalization

Enhanced generalization of diagnostic algorithms across scanners

Abstract

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography during the early detection and diagnosis of breast cancer. However, images generated by various MRI scanners (e.g. GE Healthcare vs Siemens) differ both in intensity and noise distribution, preventing algorithms trained on MRIs from one scanner to generalize to data from other scanners successfully. We propose a method for image normalization to solve this problem. MRI normalization is challenging because it requires both normalizing intensity values and mapping between the noise distributions of different scanners. We utilize a cycle-consistent generative adversarial network to learn a bidirectional mapping between MRIs produced by GE Healthcare and Siemens scanners. This allows us learning the mapping between two different scanner types without matched data, which is not commonly available. To ensure the preservation of breast shape and structures within the breast, we propose two technical innovations. First, we incorporate a mutual information loss with the CycleGAN architecture to ensure that the structure of the breast is maintained. Second, we propose a modified discriminator architecture which utilizes a smaller field-of-view to ensure the preservation of finer details in the breast tissue. Quantitative and qualitative evaluations show that the second proposed method was able to consistently preserve a high level of detail in the breast structure while also performing the proper intensity normalization and noise mapping. Our results demonstrate that the proposed model can successfully learn a bidirectional mapping between MRIs produced by different vendors, potentially enabling improved accuracy of downstream computational algorithms for diagnosis and detection of breast cancer. All the data used in this study are publicly available.