Electronic cleansing in CT colonography using transformer-based UNet

We developed a novel 3D transformer-based UNet method for performing electronic cleansing (EC) in CT colonography (CTC). The method is designed to map an uncleansed CTC image volume directly into the corresponding virtually cleansed CTC image volume. In the method, the layers of a 3D transformer-based encoder are connected via skip connections to the decoder layers of a 3D UNet to enhance the ability of the UNet to use long-distance image information for resolving EC image artifacts. 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 samples. The image quality of EC was compared with that of a conventional 3D UNet-based EC method. Our preliminary results indicate that the 3D transformer-based UNet EC method is a potentially effective approach for optimizing the performance of EC in CTC.