Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI from X-ray: Integrating External Radiographic Feature Information

Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, often diagnosed using X-rays due to its cost-effectiveness. While Magnetic Resonance Imaging (MRI) provides superior soft tissue visualization and serves as a valuable supplementary diagnostic tool, its high cost and limited accessibility significantly restrict its widespread use. To explore the feasibility of bridging this imaging gap, we conducted a feasibility study leveraging a diffusion-based model that uses an X-ray image as conditional input, alongside target depth and additional patient-specific feature information, to generate corresponding MRI sequences. Our findings demonstrate that the MRI volumes generated by our approach are not only visually closer to real MRI scans compared with other methods but also achieve the highest quantitative performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, by increasing the number of inference steps to interpolate between slice depths, we enhance the continuity of the generated volume, achieving higher adjacent slice correlation coefficients. Through ablation studies, we further validate that integrating supplemental patient-specific information, beyond what X-rays alone can provide, enhances the accuracy and clinical relevance of the generated MRI, which underscores the potential of leveraging external patient-specific information to improve the performance of the MRI generation. This study is available at https://zwang78.github.io/.