We explored the feasibility and impact of an MRI-only imaging pipeline using deep learning models to synthesize CT-like images from conventional MRI scans. CT imaging is a cornerstone of modern diagnostics, widely used for bone assessment, radiation therapy planning, and surgical preparation. Despite its clinical value, CT exposes patients to ionizing radiation, posing cumulative health risks when repeated scans are required. MRI, by contrast, provides excellent soft tissue contrast without radiation exposure but lacks the ability to accurately capture bone density and fine structural detail—historically preventing it from fully replacing CT in many clinical workflows.
By producing synthetic CT images that preserved clinically relevant bone information, our approach has the potential to significantly reduce patient radiation exposure while maintaining diagnostic accuracy. Beyond image generation, we integrated AI-based diagnostic tools to assist in image interpretation. These tools can automatically highlight regions of interest, flag potential abnormalities, and provide decision support to radiologists as a secondary review layer, improving diagnostic confidence and efficiency.
The integration of synthetic CT generation and AI-assisted diagnostics offers substantial benefits across multiple stakeholders. Patients benefit from reduced radiation exposure, clinicians gain enhanced diagnostic tools, and healthcare systems could see improved efficiency and reduced costs. Ultimately, this MRI-only, AI-driven approach has the potential to transform medical imaging workflows, improve patient safety, and redefine standards of care across a wide range of clinical applications.