Innovative MRI Denoising Using Federated and Transfer Learning

Magnetic resonance imaging (MRI) is crucial for medical diagnostics, providing detailed images essential for accurate diagnoses. However, centralized image processing systems pose significant data privacy risks, particularly when sharing patient data across institutions. This study addresses the dual challenges of MRI denoising and data privacy by introducing a novel hybrid model within a federated learning (FL) framework. The proposed approach combines transfer learning and FL to enhance MRI denoising performance while ensuring patient data remains secure and decentralized. Specifically, a VGG-denoising autoencoder (VGG-DAE) integrates a pretrained VGG16 network with an autoencoder, trained across eight clients simulating diverse medical institutions. FL enables localized data storage and aggregates model updates to refine a global model. Experimental results demonstrate the method's effectiveness, achieving a peak signal to noise ratio (PSNR) of 56.95 dB, significantly surpassing traditional denoising approaches where the state of the art PSNR is capped at 30 dB. This work underscores the potential of FL for secure and efficient MRI denoising, offering a significant contribution to medical imaging by improving noise reduction while preserving data privacy.