Abstract #2184

High resolution PET image denoising using anatomical priors by K-nearest neighborhood method in the feature space

Mehdi Khalighi¹, Timothy Deller², Kevin Chen¹, Tyler Toueg³, Dawn Holley¹, Kim Halbert¹, Floris Jansen², Elizabeth Mormino³, Michael Zeineh¹, Farshad Moradi¹, Greg Zaharchuk¹, and Andrei Iagaru¹

¹Radiology, Stanford University, Stanford, CA, United States, ²Engineering Dept., GE Healthcare, Waukesha, WI, United States, ³Neurology, Stanford University, Stanford, CA, United States

After PET images are reconstructed by OSEM, a spatial filter which exploits the correlation between neighboring voxels, is applied to remove noise. A new filtering method is proposed that also exploits the correlation between voxels from the same tissue. Anatomical priors are processed for bias correction and registered to PET images. For each voxel, similar voxels within the PET volume are identified using KNN method in the feature-space built by anatomical priors. These similar voxels and also the neighboring voxels are then used to remove the high frequency noise on PET images using a Gaussian and a weighted averaging filter.

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