Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network
This addresses the challenge of incomplete data in longitudinal infant brain studies, which is incremental as it builds on existing GAN methods with specific improvements.
The paper tackles the problem of missing scans in longitudinal infant brain MRI studies by predicting missing scans from acquired ones using a metamorphic generative adversarial network (MGAN), which outperforms existing GANs in accurately predicting contrast and anatomical details.
Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of tissue contrast and structural details. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both contrast and anatomical details.