Multi-Dimensional Tensor-Based Adaptive Filter (TBAF) for Low Dose X-Ray CT

Edge-preserving adaptive filtering within CT image reconstruction is a powerful method to reduce image noise and hence to reduce patient dose. However, highly sophisticated adaptive filters typically comprise many parameters which must be adjusted carefully in order to obtain optimal filter performance and to avoid artifacts caused by the filter. In this work we applied an anisotropic tensor-based adaptive image filter (TBAF) to CT image reconstruction, both as an image-based post-processing step, as well as a regularization step within an iterative reconstruction. The TBAF is a generalization of the filter of reference.(1) Provided that the image noise (i.e. the variance) of the original image is known for each voxel, we adjust all filter parameters automatically. Hence, the TBAF can be applied to any individual CT dataset without user interaction. This is a crucial feature for a possible application in clinical routine. The TBAF is compared to a well-established adaptive bilateral filter using the same noise adjustment. Although the differences between both filters are subtle, edges and local structures emerge more clearly in the TBAF filtered images while anatomical details are less affected than by the bilateral filter.