X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks
This could enhance low-cost X-ray machines for niche medical applications by providing CT-like 3D views, but it appears incremental as it builds on existing GAN methods for a specific bottleneck.
The paper tackles the problem of reconstructing CT scans from only two orthogonal X-rays using a GAN framework, achieving high-quality 3D volumes with quantitative improvements, though specific numbers are not provided in the abstract.
Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too. Traditional CT reconstruction methods require hundreds of X-ray projections through a full rotational scan of the body, which cannot be performed on a typical X-ray machine. In this work, we propose to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework. A specially designed generator network is exploited to increase data dimension from 2D (X-rays) to 3D (CT), which is not addressed in previous research of GAN. A novel feature fusion method is proposed to combine information from two X-rays.The mean squared error (MSE) loss and adversarial loss are combined to train the generator, resulting in a high-quality CT volume both visually and quantitatively. Extensive experiments on a publicly available chest CT dataset demonstrate the effectiveness of the proposed method. It could be a nice enhancement of a low-cost X-ray machine to provide physicians a CT-like 3D volume in several niche applications.