Abstract
Various harmonization methods have been employed for obtaining MRI from different scanners. However, no study has yet focused on the clinical utility of the CycleGAN technique in reducing MRI interscanner variability for patients with brain metastasis across longitudinal visits. We developed a head-to-head and longitudinal CycleGAN-based deep learning (DL) algorithm for MRI harmonization and validated its utility for follow-up (FU) MRI evaluation in patients with unchanged brain metastasis, who had FU MRI taken using a different MRI scanner. We trained the head-to-head and longitudinal CycleGAN to generate harmonized second postcontrast 3D T1W MR images with similar image impressions as the initial postcontrast 3D T1W MR images. The image similarity scores between the baseline (BL) and harmonized FU images were higher than those between the baseline and original FU images. As compared with baseline, differences in the CNRs of brain subregions were lower for the harmonized FU images than for the original FU images. More cases were read to be unchanged on the harmonized FU images than on the original FU images in terms of border, size, and contrast enhancement at a higher level of diagnostic confidence. The proposed CycleGAN algorithm may potentially decrease false positivity for the diagnosis of progression in FU MRI evaluation of brain metastasis.