Deep Super-Resolution and Age Prediction for Brain MRI Using SRCNN: A Lightweight Framework for High-Fidelity Reconstruction and Regression

Abstract

Magnetic Resonance Imaging (MRI) provides a robust, non-invasive method for brain assessment and the detection of age-related biomarkers. However, due to hardware restrictions or the need for shorter acquisition times, clinical scans are often acquired at reduced spatial resolution, leading to degraded image quality that hampers tasks such as age prediction and other downstream analyses. To address this challenge, we propose a lightweight two-stage deep learning framework that combines super-resolution reconstruction with age regression. In the first stage, a Super-Resolution Convolutional Neural Network (SRCNN) enhances low-quality axial brain MRI slices, recovering fine structural details and improving overall image fidelity. The reconstructed images achieve a peak signal-to-noise ratio (PSNR) of 30.45 dB, a mean squared error (MSE) of 0.0010, and a structural similarity index (SSIM) of 0.9599 on the enhanced dataset. In the second stage, a specialized convolutional neural network leverages the super-resolved images to estimate biological age. Tested on the IXI dataset, the model demonstrates strong predictive accuracy, achieving a mean absolute error (MAE) of 2.35 years, a root mean squared error (RMSE) of 3.05 years, and a coefficient of determination (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex>) of 0.965. These results highlight the effectiveness of super-resolution as a preprocessing step for improving regression performance. The proposed lightweight and interpretable architecture illustrates the advantages of integrating high-fidelity reconstruction with predictive modeling in a unified pipeline, enabling improved brain MRI analysis and reliable age prediction.