Federated metadata-constrained iRadonMAP framework with mutual learning for all-in-one computed tomography imaging
Computed tomography (CT) is an important diagnostic tool in clinical practice, widely used for disease screening and diagnosis. However, CT scans involve X-rays, which expose patients to radiation and potential health risks. Existing low-dose CT imaging often comes with degraded image quality, thereby affecting diagnostic accuracy. Although recent deep learning methods can markedly improve low-dose reconstruction quality, most rely on large centralized paired datasets collected under diverse vendors and scanning protocols—an approach constrained in medical imaging by privacy and regulatory requirements as well as ...