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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.

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Academic Units
Biomedical Engineering
Published Here
August 13, 2010

Notes

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.

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