Self-supervised learning for multi-center magnetic resonance imaging harmonization without traveling phantoms.

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Abstract

With the progress of artificial intelligence (AI) in magnetic resonance imaging (MRI), large-scale multi-center MRI datasets have a great influence on diagnosis accuracy and model performance. However, multi-center images are highly variable due to the variety of scanners or scanning parameters in use, which has a negative effect on the generality of AI-based diagnosis models. To address this problem, we propose a self-supervised harmonization (SSH) method. Mapping the style of images between centers allows harmonization without traveling phantoms to be formalized as an unpaired image-to-image translation problem between two domains. The mapping is a two-stage transform, consisting of a modified cycle generative adversarial network (cycleGAN) for style transfer and a histogram matching module for structure fidelity. The proposed algorithm is demonstrated using female pelvic MRI images from two different 3T systems, compared with two state-of-the-art deep-learning (DL)-based methods and one conventional method. In the absence of traveling phantoms, we evaluate harmonization from three perspectives: image fidelity and noise suppression, the ability to remove inter-center differences, and the influence on the downstream model. The results show that SSH yields superior generality of downstream cervical cancer classification models by significantly decreasing the difference in radiomics features and achieves greater image fidelity and noise suppression, as quantified by the mean structure similarity index measure (MSSIM) and the peak signal-to-noise ratio (PSNR).© 2022 Institute of Physics and Engineering in Medicine.