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

Zhe Wang1, Rachid Jennane2, Aladine Chetouani3, Yung Hsin Chen1, Fabian Bauer4, Mohamed Jarraya1†
1Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, 02114, USA 2IDP Institute, UMR CNRS 7013, University of Orleans, Orleans, 45067, France 3L2TI Laboratory, Sorbonne Paris North University, Paris, 93430, France 4Division of Radiology, German Cancer Research Center, Heidelberg, 69120, Germany
arXiv Code

Please refresh the page if the GIFs are not synchronized :)

X-ray

Real MRI

Generated Pseudo-MRI

Pseudo-MRI (x2 Interpolation)

*It is noteworthy that the reconstructed MRI is not intended to serve as a clinical replacement for a real MRI. Still, it marks a pioneering cross-modal attempt to generate corresponding pseudo-MRI volumes from X-rays, for seeking cost-effective medical imaging solutions.

The inference process is shown as follows:

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 hinder its widespread use, leaving many patients with KOA reliant solely on X-ray imaging. In this study, we introduce a novel diffusion-based Xray2MRI model capable of generating pseudo-MRI volumes from one 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. Moreover, by increasing the inference times, the model achieves effective interpolation, further improving the continuity and smoothness of the generated MRI sequences, representing one promising initial attempt for cost-effective medical imaging solutions.

Flowchart of our proposed approach

MY ALT TEXT

BibTeX

@article{wang2024diffusion,
  title={A Diffusion-based Xray2MRI Model: Generating Pseudo-MRI Volumes From one Single X-ray},
  author={Wang, Zhe and Jennane, Rachid and Chetouani, Aladine and Jarraya, Mohamed},
  journal={arXiv preprint arXiv:2410.06997},
  year={2024}}