Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach.

内容摘要

Brain age estimation provides a noninvasive MRI biomarker of neurodevelopment. In infancy, rapid regionally ordered myelination reflects brain maturation, yet early-life brain age estimation remains underexplored, particularly with myelination-sensitive MRI and biologically informed modeling. To develop and evaluate a biologically informed deep learning framework for infant brain age estimation using T1w/T2w ratio MRI. Retrospective. Internal cohort: 629 infants aged 0-24&#x2009;months (626 with age-appropriate myelination, train/validation/test&#x2009;=&#x2009;376/125/125), 3 with myelin-related developmental abnormalities for qualitative review. External cohort: 10 healthy infants aged 0-15&#x2009;months (5 females, 5 males). Internal: 3T; 3D gradient-echo or 2D spin-echo T1w, and 2D turbo spin-echo T2w. External: 3T; 3D gradient-echo T1w and 2D turbo spin-echo T2w. 3D convolutional neural networks were trained with T1w, T2w, and T1w/T2w ratio inputs using manually defined biological age labels from visual myelination assessment. The model incorporated multi-task learning for age regression, white matter segmentation, and image reconstruction. Performance was evaluated using five-fold cross-validation with repeated random splits. Metrics included mean absolute error, root mean squared error, R 2 $$ {R}^2 $$ , and Pearson and Spearman correlations. Modality differences were tested using one-way ANOVA, t $$ t $$ -tests, and Mann-Whitney U $$ U $$ , with Cohen's d $$ d $$ and 95% confidence intervals. In the external cohort, absolute prediction errors were compared using the Wilcoxon signed-rank test. Statistical significance was defined as p < 0.05 $$ p<0.05 $$ . T1w/T2w ratio models achieved the best overall performance (MAE: 1.489&#x2009; &#xb1; $$ \pm $$ 0.302&#x2009;months; r $$ r $$ &#x2009;=&#x2009;0.966&#x2009; &#xb1; $$ \pm $$ &#x2009;0.012), compared with T1w (2.055&#x2009; &#xb1; $$ \pm $$ &#x2009;0.944; 0.933&#x2009; &#xb1; $$ \pm $$ &#x2009;0.061), T2w (1.794&#x2009; &#xb1; $$ \pm $$ &#x2009;0.434; 0.947&#x2009; &#xb1; $$ \pm $$ &#x2009;0.023), T1w+T2w (1.546&#x2009; &#xb1; $$ \pm $$ &#x2009;0.291; 0.960&#x2009; &#xb1; $$ \pm $$ &#x2009;0.013), and T1w+T2w+RI (1.498&#x2009; &#xb1; $$ \pm $$ &#x2009;0.313; 0.963 &#xb1; $$ \pm $$ 0.012). Modality effects were significant for MAE, RMSE, R 2 $$ {R}^2 $$ , r $$ r $$ , but not for &#x3c1; $$ \rho $$ ( p = 0.250 $$ p=0.250 $$ ). Auxiliary-task and multi-scale modeling numerically improved performance (MAE, 1.203&#x2009;months; r $$ r $$ &#x2009;=&#x2009;0.979). External validation showed the lowest error for the RI-based model (MAE, 1.16&#x2009;months), and Grad-CAM highlighted myelination-relevant white matter. T1w/T2w ratio MRI combined with biologically informed deep learning enabled accurate and interpretable infant brain age estimation. This framework showed promising cross-scanner performance and may support MRI-based assessment of early brain maturation. 3. 2. Assessing brain development in infants is critical for early detection of developmental delays. This study developed a deep learning model that estimates infant brain age from MRI by combining two standard scan types into a ratio image highlighting myelin, the insulating coating around nerve fibers that increases as the brain matures. Trained on 629 infants aged 0&#x2013;24&#x2009;months, the model predicted developmental age with a mean error of approximately 1.5&#x2009;months. Attention maps confirmed the model focused on regions known to undergo early myelination. This approach showed consistent performance across two different scanners and may support objective monitoring of infant brain maturation.