We proposed a self-supervised harmonization to achieve the generality and robustness of diagnostic models in multi-center MRI studies. By mapping the style of images from one center to another center, the harmonization without traveling phantoms was formalized as an unpaired image-to-image translation problem between two domains. The proposed method was demonstrated with pelvic MRI images from two different systems against two state-of-the-art deep-leaning (DL) based methods and one conventional method. The proposed method yields superior generality of diagnostic models by largely decreasing the difference in radiomics features and great image fidelity as quantified by mean structure similarity index measure (MSSIM).
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