Abstract
Computed tomography images can be reconstructed using different kernels, depending on the purpose of the examination. Sharp kernels allow the edges of objects such as bones or calcifications to be more precisely defined than is possible with soft kernels; however, they also produce more noise, which makes soft tissue segmentation much more difficult, e.g. in case of a heart modeling.
In this paper, image denoising results are demonstrated for images reconstructed with different kernels. The CT scans of the same patient were reconstructed with 8 kernels: B26f, B30f, B31f, B35f, B36f, B41f, B46f and B50f. All the images were filtered using denoising filters: Anisotropic Diffusion, Gaussian Smoothing, Non-Local Means, and Sigma Filter. The similarity of the images to reference images (B26f) was calculated using SSIM. The results show that filtering can substantially increase the similarity between images reconstructed with a hard or soft kernel.
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Lasek, J., Piórkowski, A. (2021). CT Scan Transformation from a Sharp to a Soft Reconstruction Kernel Using Filtering Techniques. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_6
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