RetinexFlow for CT metal artifact reduction
This addresses the problem of degraded image quality and difficult diagnosis in CT imaging due to metal artifacts, offering a method that does not require prior knowledge of implant locations, though it appears incremental as it builds on existing theories and techniques.
The paper tackles metal artifact reduction in CT imaging by formulating it as decomposition and completion tasks, proposing RetinexFlow, an end-to-end model based on Retinex theory and conditional normalizing flow, which achieves superior quantitative and qualitative results on simulation and clinical datasets.
Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult. However, previous methods either require prior knowledge of the location of metal implants, or have modeling deviations with the mechanism of artifact formation, which limits the ability to obtain high-quality CT images. In this work, we formulate metal artifacts reduction problem as a combination of decomposition and completion tasks. And we propose RetinexFlow, which is a novel end-to-end image domain model based on Retinex theory and conditional normalizing flow, to solve it. Specifically, we first design a feature decomposition encoder for decomposing the metal implant component and inherent component, and extracting the inherent feature. Then, it uses a feature-to-image flow module to complete the metal artifact-free CT image step by step through a series of invertible transformations. These designs are incorporated in our model with a coarse-to-fine strategy, enabling it to achieve superior performance. The experimental results on on simulation and clinical datasets show our method achieves better quantitative and qualitative results, exhibiting better visual performance in artifact removal and image fidelity