Genetic and Epigenetic Algorithms Optimization of U-Net Architectures for Low-Dose Scintigraphy Image Reconstruction

This study introduces a novel approach for optimizing models that reconstruct high-quality full-dose bone scintigraphy images from their 40% low-dose counterparts using optimized attention-based U-Net architectures. We utilized Genetic and Epigenetic Algorithms (epiGA) hyperparameter optimization of two distinct models: a standard Attention U-Net and an Attention U-Net modified with ResNet blocks. Models were trained using a hybrid Mean Squared Error and Structural Similarity (MSE/SSIM) loss function. Obtained results demonstrated superior performance, achieving an average SSIM of 0.9197 and an average Peak Signal-to-Noise ratio (PSNR) of 34.1516 dB, significantly surpassing the baseline low-dose image quality, by gaining ΔSSIM = 0.0333 and ΔPSNR = 3.0729 dB, due to hyperparameter optimization. Comparative benchmarks against Bayesian optimization revealed that epiGA offers superior search efficiency—exploring twice the architecture space in comparable wall-clock time—while consistently identifying more compact, hardware-efficient solutions. These results highlight the effectiveness of integrating epigenetic mechanisms for robust, scalable hyperparameter tuning in medical image reconstruction.