Feasibility study for reconstruction of knee MRI from one corresponding X-ray via CNN
This addresses the need for more accessible MRI-like diagnostics from cheaper X-rays for medical practitioners, but appears incremental as it builds on existing deep learning techniques.
The paper tackled the problem of generating knee MRI from a single X-ray using a deep learning approach, achieving a method that uses hidden variables from a Convolutional Auto-Encoder to produce 3D MRI.
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.