Background: CT perfusion images have a high contrast ratio between voxels representing different anatomy, such\nas tissue or vessels, which makes image segmentation of tissue and vascular regions relatively easy. However, grey\nand white matter tissue regions have relatively low values and can suffer from poor signal to noise ratios. While\nsmoothing can improve the image quality of the tissue regions, the inclusion of much higher valued vascular voxels\ncan skew the tissue values. It is thus desirable to smooth tissue voxels separately from other voxel types, as has been\npreviously implemented using mean filter kernels. We created a novel Masked Smoothing method that performs\nGaussian smoothing restricted to tissue voxels. Unlike previous methods, it is implemented as a combination of\nseparable kernels and is therefore fast enough to consider for clinical work, even for large kernel sizes.\nMethods: We compare our Masked Smoothing method to alternatives using Gaussian smoothing on an unaltered\nimage volume and Gaussian smoothing on an image volume with vascular voxels set to zero. Each method was tested\non simulation data, collected phantom data, and CT perfusion data sets. We then examined tissue voxels for bias and\nnoise reduction.\nResults: Simulation and phantom experiments demonstrate that Masked Smoothing does not bias the underlying\ntissue value, whereas the other smoothing methods create significant bias. Furthermore, using actual CT perfusion\ndata, we demonstrate significant differences in the calculated CBF and CBV values dependent on the smoothing\nmethod used.\nConclusion: The Masked Smoothing is fast enough to allow eventual clinical usage and can remove the bias of tissue\nvoxel values that neighbor blood vessels. Conversely, the other Gaussian smoothing methods introduced significant\nbias to the tissue voxels.
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