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De-noising of SPECT and PET images is a challenging task due to the inherent low signal-to-noise ratio of acquired data. Wavelet based multi- scale denoising methods typically apply thresholding operators on sub-band co- efficients to eliminate noise components in spatial-frequency space prior to re- construction. In the case of high noise levels, detailed scales of sub-band im- ages are usually dominated by noise which cannot be easily removed using tra- ditional thresholding schemes. To address this issue, a cross-scale regulariza- tion scheme is introduced, which takes into account cross-scale coherence of structured signals. Preliminary results show promising performance in denois- ing clinical SPECT and PET images for liver and brain studies. Wavelet thresh- olding was also compared to denoising with a brushlet expansion. The proposed regularization scheme eliminates the need for threshold parameter settings, making the denoising process less tedious and suitable for clinical practice.

De-noising SPECT/PET Images Using Cross-Scale Regularization