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

TL;DR

This study explores a diffusion-based model that generates MRI images from X-ray inputs, incorporating patient-specific data to improve accuracy, potentially aiding in more accessible knee osteoarthritis diagnosis.

Contribution

The paper introduces a novel diffusion-based approach for cross-modal MRI generation from X-rays, integrating additional patient features to enhance image quality and clinical relevance.

Findings

Generated MRI images are visually closer to real scans.

Increasing inference steps improves image continuity and smoothness.

Incorporating patient-specific data enhances generation accuracy.

Abstract

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 is visually closer to real MRI scans. Moreover, increasing inference steps enhances the continuity and smoothness of the synthesized MRI sequences. Through ablation studies, we further validate that integrating supplementary 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 MRI generation. This study is available at https://zwang78.github.io/.