Projection Inpainting Using Partial Convolution for Metal Artifact Reduction
This addresses metal artifacts in medical imaging for patients with implants, but is incremental as it adapts an existing method to a specific domain.
The paper tackles metal artifact reduction in CT scans by using partial convolution for projection inpainting, which only relies on valid pixels, and shows it outperforms conventional convolution in U-Net architectures.
In computer tomography, due to the presence of metal implants in the patient body, reconstructed images will suffer from metal artifacts. In order to reduce metal artifacts, metals are typically removed in projection images. Therefore, the metal corrupted projection areas need to be inpainted. For deep learning inpainting methods, convolutional neural networks (CNNs) are widely used, for example, the U-Net. However, such CNNs use convolutional filter responses on both valid and corrupted pixel values, resulting in unsatisfactory image quality. In this work, partial convolution is applied for projection inpainting, which only relies on valid pixels values. The U-Net with partial convolution and conventional convolution are compared for metal artifact reduction. Our experiments demonstrate that the U-Net with partial convolution is able to inpaint the metal corrupted areas better than that with conventional convolution.