A Deep Learning Framework for Synthesizing Longitudinal Infant Brain MRI during Early Development

Purpose To develop a three-stage, age- and modality-conditioned framework to synthesize longitudinal infant brain MRI scans and account for rapid structural and contrast changes during early brain development. Materials and Methods This retrospective study utilized T1- and T2-weighted MRI scans (848 in total) from 139 infants in the Baby Connectome Project, collected between September 2016 and May 2020. The framework models three critical image cues related: volumetric expansion, cortical folding, and myelination, predicting missing time points with age and modality as predictive factors. The method was compared with LGAN, CounterSyn, and a diffusion-based approach using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the Dice similarity coefficient (DSC). Results The framework was trained on 119 participants (mean age ± SD, 11.25 months ± 6.16; 60 female and 59 male infants) and tested on 20 participants (mean age, 12.98 months ± 6.59; 11 female and nine male infants). For T1-weighted images, PSNRs were 25.44 ± 1.95 and 26.93 ± 2.50 for forward and backward MRI synthesis, respectively, and SSIMs were 0.87 ± 0.03 and 0.90 ± 0.02, respectively. For T2-weighted images, PSNRs were 26.35 ± 2.30 and 26.40 ± 2.56, respectively, with SSIMs of 0.87 ± 0.03 and 0.89 ± 0.02, respectively, showing significant outperformance compared with competing methods (P < .001). The framework also excelled in tissue segmentation (P < .001) and cortical reconstruction, achieving a DSC of 0.85 for gray matter and 0.86 for white matter, with intraclass correlation coefficients exceeding 0.8 in most cortical regions. Conclusion The proposed three-stage framework effectively synthesized age-specific infant brain MRI scans, outperforming competing methods in image quality and tissue segmentation and with strong performance in cortical reconstruction, demonstrating potential for developmental modeling and longitudinal analyses. Keywords: Pediatrics, Brain, Brain Stem, MRI, Infant Brain MRI Supplemental material is available for this article. © RSNA, 2025 See also commentary by Chaudhari and Rauschecker in this issue.