Synthetic computed tomography from magnetic resonance imaging: An editorial on deep learning approaches for hip and knee image translation
Abstract
This editorial provides a concise overview of synthetic computed tomography (CT) generation from magnetic resonance imaging (MRI) for musculoskeletal care, with a focus on hip and knee applications. We outline why interest in this area is growing, including the potential to reduce radiation exposure, consolidate imaging into a single visit, and align measurements across modalities for preoperative planning and longitudinal follow-up. At a high level, we summarize how deep learning methods transform MRI into CT-like volumes and what that could mean for bone morphology, implant planning, and combined assessment of bone and soft tissue. We briefly introduce representative model classes, including conditional generative adversarial networks that pair realism objectives with voxelwise constraints and diffusion models that replace adversarial training with iterative denoising for stable optimization and strong anatomical fidelity. Our goal is to orient clinicians, engineers, and researchers to the key promises and open questions, and to motivate collaborative studies that test whether synthetic CT can deliver reliable, reproducible, and clinically useful information at the point of care.
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