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Missing scans are inevitable in longitudinal studies due to either subjectdropouts or failed scans. In this paper, we propose a deep learning frameworkto predict missing scans from acquired scans, catering to longitudinal infantstudies. Prediction of infant brain MRI is challenging owing to the rapidcontrast and structural changes particularly during the first year of life. Weintroduce a trustworthy metamorphic generative adversarial network (MGAN) fortranslating infant brain MRI from one time-point to another. MGAN has three keyfeatures: (i) Image translation leveraging spatial and frequency informationfor detail-preserving mapping; (ii) Quality-guided learning strategy thatfocuses attention on challenging regions. (iii) Multi-scale hybrid lossfunction that improves translation of tissue contrast and structural details.Experimental results indicate that MGAN outperforms existing GANs by accuratelypredicting both contrast and anatomical details.

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network