FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis

Abstract

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Deep learning methods for MRI-to-sCT have shown promising results, but their reliance on single-centre training dataset limits generalisation capabilities to diverse clinical settings. Moreover, creating centralised multi-centre datasets may pose privacy concerns. To address the aforementioned issues, we introduced FedSynthCT-Brain, an approach based on the Federated Learning (FL) paradigm for MRI-to-sCT in brain imaging. This is among the first applications of FL for MRI-to-sCT, employing a cross-silo horizontal FL approach that allows multiple centres to collaboratively train a U-Net-based deep learning model. We validated our method using real multicentre data from four European and American centres, simulating heterogeneous scanner types and acquisition modalities, and tested its performance on an independent dataset from a centre outside the federation. In the case of the unseen centre, the federated model achieved a median Mean Absolute Error (MAE) of $102.0$ HU across 23 patients, with an interquartile range of $96.7-110.5$ HU. The median (interquartile range) for the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PNSR) were $0.89(0.86-0.89)$ and $26.58(25.52-27.42)$, respectively. The analysis of the results showed acceptable performances of the federated approach, thus highlighting the potential of FL to enhance MRI-to-sCT to improve generalisability and advancing safe and equitable clinical applications while fostering collaboration and preserving data privacy.

References in corpus (10)

• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• On the Convergence of FedAvg on Non-IID Data
• Fair Resource Allocation in Federated Learning
• Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
• Male pelvic synthetic CT generation from T1-weighted MRI using 2D and 3D convolutional neural networks
• Beam Squint Analysis and Mitigation via Hybrid Beamforming Design in THz Communications
• Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
• Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
• Fed-Sim: Federated Simulation for Medical Imaging
• FedFisher: Leveraging Fisher Information for One-Shot Federated Learning

Cited by in corpus (1)

• FedGIN: Federated Learning with Dynamic Global Intensity Non-linear Augmentation for Organ Segmentation using Multi-modal Images