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Open Access Highly Accessed Research article

Masked smoothing using separable kernels for CT perfusion images

David S Wack1*, Kenneth V Snyder2, Kevin F Seals3 and Adnan H Siddiqui2

Author Affiliations

1 Dept. of Nuclear Medicine and Center for Positron Emission Tomography, The University at Buffalo, State University of New York, Buffalo, NY, USA

2 Dept. of Neurosurgery and Toshiba Stroke and Vascular Research Center, The University at Buffalo, State University of New York, Buffalo, NY, USA

3 School of Medicine, The University at Buffalo, State University of New York, Buffalo, NY, USA

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BMC Medical Imaging 2014, 14:28  doi:10.1186/1471-2342-14-28

Published: 21 August 2014

Abstract

Background

CT perfusion images have a high contrast ratio between voxels representing different anatomy, such as tissue or vessels, which makes image segmentation of tissue and vascular regions relatively easy. However, grey and white matter tissue regions have relatively low values and can suffer from poor signal to noise ratios. While smoothing can improve the image quality of the tissue regions, the inclusion of much higher valued vascular voxels can skew the tissue values. It is thus desirable to smooth tissue voxels separately from other voxel types, as has been previously implemented using mean filter kernels. We created a novel Masked Smoothing method that performs Gaussian smoothing restricted to tissue voxels. Unlike previous methods, it is implemented as a combination of separable kernels and is therefore fast enough to consider for clinical work, even for large kernel sizes.

Methods

We compare our Masked Smoothing method to alternatives using Gaussian smoothing on an unaltered image volume and Gaussian smoothing on an image volume with vascular voxels set to zero. Each method was tested on simulation data, collected phantom data, and CT perfusion data sets. We then examined tissue voxels for bias and noise reduction.

Results

Simulation and phantom experiments demonstrate that Masked Smoothing does not bias the underlying tissue value, whereas the other smoothing methods create significant bias. Furthermore, using actual CT perfusion data, we demonstrate significant differences in the calculated CBF and CBV values dependent on the smoothing method used.

Conclusion

The Masked Smoothing is fast enough to allow eventual clinical usage and can remove the bias of tissue voxel values that neighbor blood vessels. Conversely, the other Gaussian smoothing methods introduced significant bias to the tissue voxels.