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Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation.

Abstract : Denoising of Positron Emission Tomography (PET) images is a challenging task due to the inherent low signal-to-noise ratio (SNR) of the acquired data. A pre-processing denoising step may facilitate and improve the results of further steps such as segmentation, quantification or textural features characterization. Different recent denoising techniques have been introduced and most state-of-the-art methods are based on filtering in the wavelet domain. However, the wavelet transform suffers from some limitations due to its non-optimal processing of edge discontinuities. More recently, a new multi scale geometric approach has been proposed, namely the curvelet transform. It extends the wavelet transform to account for directional properties in the image. In order to address the issue of resolution loss associated with standard denoising, we considered a strategy combining the complementary wavelet and curvelet transforms. We compared different figures of merit (e.g. SNR increase, noise decrease in homogeneous regions, resolution loss, and intensity bias) on simulated and clinical datasets with the proposed combined approach and the wavelet-only and curvelet-only filtering techniques. The three methods led to an increase of the SNR. Regarding the quantitative accuracy however, the wavelet and curvelet only denoising approaches led to larger biases in the intensity and the contrast than the proposed combined algorithm. This approach could become an alternative solution to filters currently used after image reconstruction in clinical systems such as the Gaussian filter.
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Submitted on : Tuesday, July 16, 2013 - 11:14:26 AM
Last modification on : Monday, October 11, 2021 - 2:22:10 PM
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Adrien Le Pogam, Houda Hanzouli, Mathieu Hatt, Catherine Cheze Le Rest, Dimitris Visvikis. Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation.. Medical Image Analysis, Elsevier, 2013, 17 (8), pp.877-891. ⟨10.1016/⟩. ⟨inserm-00844926⟩



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