用X光生成膝关节MRI，让低成本影像更接近真实软组织细节。
Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI from X-ray: Integrating Radiographic Feature Information
- 基于扩散模型，输入X光+患者特征信息生成MRI序列。
- 生成的MRI视觉效果更接近真实扫描，增加推理步数提升连续性。
- 加入额外患者信息可提高生成精度，适合医学影像补全场景。
膝关节骨关节炎（KOA）是一种常见骨骼肌肉疾病，通常通过成本较低的X射线进行诊断。尽管磁共振成像（MRI）能提供更优的软组织可视化，但其高成本和有限可及性严重限制了广泛应用。为探索填补这一影像差距的可能性，我们开展了一项可行性研究，采用基于扩散的模型，以X射线图像作为条件输入，并结合目标深度及额外患者特异性特征信息，生成对应的MRI序列。结果表明，本方法生成的MRI体积在视觉上更接近真实MRI扫描；增加推理步数可提升合成MRI序列的连续性和平滑度。通过消融实验进一步验证，融入仅靠X射线无法提供的附加患者特异性信息，能有效提升生成MRI的准确性和临床相关性，凸显利用外部患者信息改善MRI生成的潜力。该研究可在 https://zwang78.github.io/ 获取。
原文摘要 · Abstract (English)
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/.
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