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

Jin, Yinpeng; Angelini, Elsa D.; Esser, Peter D.; Laine, Andrew F.

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 coefficients to eliminate noise components in spatial-frequency space prior to reconstruction. In the case of high noise levels, detailed scales of sub-band images are usually dominated by noise which cannot be easily removed using traditional thresholding schemes. To address this issue, a cross-scale regularization scheme is introduced, which takes into account cross-scale coherence of structured signals. Preliminary results show promising performance in denoising clinical SPECT and PET images for liver and brain studies. Wavelet thresholding 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.


More About This Work

Academic Units
Biomedical Engineering
Published Here
August 13, 2010


Medical image computing and computer-assisted intervention - MICCAI 2003 : 6th International Conference, Montréal, Canada, November 2003 : proceedings ; Lecture Notes in Computer Science, Volume 2879 (Berlin ; New York : Springer-Verlag, 2003), pp. 32-40.