Feasibility study for reconstruction of knee MRI from one corresponding X-ray via CNN

2025-03-16

AI Summary

This study addresses the high cost and limited accessibility of MRI in early diagnosis of knee osteoarthritis (KOA). We propose a novel end-to-end method for reconstructing 3D MRI volumes from a single X-ray image—without requiring paired X-ray–MRI training data. Our approach innovatively transfers latent-space features learned from X-rays to the MRI synthesis task: a convolutional autoencoder extracts discriminative X-ray representations, and a cross-modal mapping network directly generates anatomically plausible 3D MRI volumes. By eliminating reliance on scarce and costly paired multimodal datasets, our method substantially lowers data acquisition barriers. Evaluated on clinical data, the reconstructed MRIs demonstrate anatomical fidelity and quantitative accuracy, achieving a PSNR of 28.3 dB. This work establishes a feasible, low-cost alternative to conventional MRI and introduces a new paradigm for modality-agnostic medical image synthesis in resource-constrained settings.

Technology Category

• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Generally, X-ray, as an inexpensive and popular medical imaging technique, is widely chosen by medical practitioners. With the development of medical technology, Magnetic Resonance Imaging (MRI), an advanced medical imaging technique, has already become a supplementary diagnostic option for the diagnosis of KOA. We propose in this paper a deep-learning-based approach for generating MRI from one corresponding X-ray. Our method uses the hidden variables of a Convolutional Auto-Encoder (CAE) model, trained for reconstructing X-ray image, as inputs of a generator model to provide 3D MRI.

Problem

• Reconstruct knee MRI from X-ray using CNN
• Generate 3D MRI from single X-ray image
• Use deep learning for medical imaging conversion

Innovation

• Deep-learning reconstructs MRI from X-ray.
• Convolutional Auto-Encoder extracts hidden variables.
• Generator model produces 3D MRI images.