Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging

TL;DR

This paper introduces a learning-based method for translating MR projections into X-ray projections, enhancing image synthesis accuracy for hybrid imaging in interventional medical procedures.

Contribution

It proposes a high-capacity generator network with a high-frequency weighted loss to improve detail accuracy in projection-to-projection translation.

Findings

Achieves only 6% deviation from ground truth

Attains a structural similarity of 0.913

Produces sharp, natural-looking X-ray projections

Abstract

Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only $6$% and structural similarity measure of $0.913\,\pm\,0.005$. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details.