Edge-preserving metal artifact reduction

Abstract

Metal implants in the field of measurement lead to strong artifacts in CT images and reduce the image quality and the diagnostic value severely. We introduce frequency split metal artifact reduction (FSMAR), a conceptually new MAR method which is designed to reduce metal artifacts and preserve details and edges of structures even close to metal implants. There are many MAR methods which simply replace unreliable parts of the projection data by inpainting. FSMAR is a combination of an inpainting-based MAR method with a frequency split approach. Normalized metal artifact reduction (NMAR) is chosen as the inpainting-based MAR method in this work. The high frequencies of the original image, where all rawdata were used for the reconstruction, are combined with an NMAR-corrected image. NMAR uses a normalization step to reduce metal artifacts without introducing severe new artifacts. Algorithms using a frequency split were already used in CT for example to reduce cone-beam artifacts. FSMAR is tested for patient datasets with different metal implants. The study includes patients with hip prostheses, a neuro coil, and a spine fixation. All datasets were scanned with modern clinical dual source CT scanners. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to metal implants.