Metal-conscious Embedding for CBCT Projection Inpainting
This work addresses metal artifacts in CBCT imaging, which degrade image quality for medical applications, and is incremental as it builds on existing inpainting methods with novel embedding techniques.
The authors tackled metal artifact reduction in cone-beam computed tomography (CBCT) by proposing a hybrid network with metal-conscious embedding methods for projection inpainting, achieving a mean absolute error of 0.079 in metal regions and a peak signal-to-noise ratio of 42.346 in CBCT projections.
The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window (Swin) vision transformer (ViT) and a convolutional neural network is proposed as a baseline network for the inpainting task. To incorporate metal information for the Swin ViT-based encoder, metal-conscious self-embedding and neighborhood-embedding methods are investigated. Both methods have improved the performance of the baseline network. Furthermore, by choosing appropriate window size, the model with neighborhood-embedding could achieve the lowest mean absolute error of 0.079 in metal regions and the highest peak signal-to-noise ratio of 42.346 in CBCT projections. At the end, the efficiency of metal-conscious embedding on both simulated and real cadaver CBCT data has been demonstrated, where the inpainting capability of the baseline network has been enhanced.