Optimizing non-local means for denoising low dose CT

  • Authors:

  • Zachary S. Kelm;Daniel Blezek;Brian Bartholmai;Bradley J. Erickson

  • Affiliations:

  • Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN;Department of Physiology and Biomedical Engineering, Mayo Clinic and Department of Radiology, Rochester, MN;Department of Radiology, Mayo Clinic, Rochester, MN;Department of Physiology and Biomedical Engineering, Mayo Clinic and Department of Radiology, Rochester, MN

  • Venue:
  • ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
  • Year:
  • 2009

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Abstract

Due to the rapid increase in use of CT imaging and the recently-heightened awareness of radiation-induced cancer, improving the diagnostic quality of low dose CT has become increasingly important. One potential method is to increase the signal-to-noise ratio of low dose images through denoising. Non-local means is a promising approach; however, it has many potentially adjustable parameters and application-specific areas of improvement. The filter uses a weighted average of similar regions to denoise each image pixel. Though the classic formulation uses only patches from the image being filtered, these patches can, in principle, be drawn from other images. In CT images, patches can be drawn from neighboring slices. We used that potential to increase the peak signal-to-noise ratio (PSNR) by over 4 dB when denoising low dose phantom CT images, and quantitatively demonstrated the filter's sensitivity to adjustment of each of its parameters.