3D generative AI for electronic cleansing in CT colonography

Abstract

We developed a novel 3D generative artificial intelligence (AI) method for performing electronic cleansing (EC) in CT colonography (CTC). In the method, a 3D transformer-based UNet is used as a generator to map an uncleansed CTC image volume directly into a virtually cleansed CTC image volume. A 3D-PatchGAN is used as a discriminator to provide feedback to the generator to improve the quality of the EC images generated by the 3D transformer-based UNet. The EC method was trained by use of the CTC image volumes of an anthropomorphic phantom that was filled partially with a mixture of foodstuff and an iodinated contrast agent. The CTC image volume of the corresponding empty phantom was used as the reference standard. The quality of the EC images was tested visually with six clinical CTC test cases and quantitatively based on a phantom test set of 100 unseen sample image volumes. The image quality of EC was compared with that of a previous 3D GAN-based EC method. Our preliminary results indicate that the 3D generative AI-based EC method outperforms our previous 3D GAN-based EC method and thus can provide an effective EC method for CTC.