A Diffusion-based Xray2MRI Model: Generating Pseudo-MRI Volumes From one Single X-ray

• URL: http://arxiv.org/abs/2410.06997v2
• Date: Thu, 17 Oct 2024 02:36:38 GMT
• Title: A Diffusion-based Xray2MRI Model: Generating Pseudo-MRI Volumes From one Single X-ray
• Authors: Zhe Wang, Rachid Jennane, Aladine Chetouani, Yung Hsin Chen, Fabian Bauer, Mohamed Jarraya,
• Abstract summary: We introduce a novel diffusion-based Xray2MRI model capable of generating pseudo-MRI volumes from a single X-ray image. Experimental results demonstrate that our proposed approach is capable of generating pseudo-MRI sequences that approximate real MRI scans.
• Score: 6.014316825270666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, and X-rays are commonly used for its diagnosis due to their cost-effectiveness. Magnetic Resonance Imaging (MRI), on the other hand, offers detailed soft tissue visualization and has become a valuable supplementary diagnostic tool for KOA. Unfortunately, the high cost and limited accessibility of MRI hinders its widespread use, leaving many patients with KOA to rely solely on X-ray imaging. In this study, we introduce a novel diffusion-based Xray2MRI model capable of generating pseudo-MRI volumes from a single X-ray image. In addition to using X-rays as conditional input, our model integrates target depth, KOA probability distribution, and image intensity distribution modules to guide the synthesis process, ensuring that the generated corresponding slices accurately correspond to the anatomical structures. Experimental results demonstrate that by integrating information from X-rays with additional input data, our proposed approach is capable of generating pseudo-MRI sequences that approximate real MRI scans. In addition, by increasing the number of inference steps, the model achieves effective interpolation, which further improves the continuity and smoothness of the generated MRI sequences, representing a promising first attempt at cost-effective medical imaging solutions. This study is available on https://zwang78.github.io/.

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