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Masked smoothing using separable kernels for CT perfusion images.

David S Wack1, Kenneth V Snyder, Kevin F Seals

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

BMC Medical Imaging
|August 23, 2014
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Summary
This summary is machine-generated.

Masked Smoothing, a novel CT perfusion imaging technique, effectively reduces bias in tissue voxel values without skewing results. This method is fast and suitable for clinical applications, unlike traditional smoothing approaches.

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Area of Science:

  • Medical imaging
  • Image processing
  • Radiology

Background:

  • CT perfusion imaging presents challenges in segmenting grey and white matter due to low signal-to-noise ratios.
  • Standard smoothing techniques can introduce bias by including high-valued vascular voxels.
  • Separate smoothing of tissue voxels is desirable to maintain accuracy.

Purpose of the Study:

  • To introduce and evaluate a novel Masked Smoothing method for CT perfusion imaging.
  • To assess the performance of Masked Smoothing compared to traditional Gaussian smoothing.
  • To determine the clinical feasibility and accuracy of the new method.

Main Methods:

  • Developed a Gaussian smoothing method restricted to tissue voxels using separable kernels.
  • Compared Masked Smoothing against standard Gaussian smoothing and zero-vessel Gaussian smoothing.
  • Evaluated methods using simulated, phantom, and actual CT perfusion data.

Main Results:

  • Masked Smoothing demonstrated no bias in underlying tissue values in simulations and phantom data.
  • Alternative smoothing methods introduced significant bias to tissue voxels.
  • Significant differences in cerebral blood flow (CBF) and cerebral blood volume (CBV) were observed based on smoothing method.

Conclusions:

  • Masked Smoothing is computationally efficient for clinical use.
  • The method successfully removes bias in tissue voxels adjacent to blood vessels.
  • Traditional smoothing methods introduce unacceptable bias in CT perfusion analysis.