Improvement of image quality in PET using post-reconstruction hybrid spatial-frequency domain filtering

PET images commonly suffer from the high noise level and poor signal-to-noise ratio (SNR), thus adversely impacting lesion detectability and quantitative accuracy. In this work, a novel hybrid dual-domain PET denoising approach is proposed, which combines the advantages of both spatial and transform domain filtering to preserve image textures while minimizing quantification uncertainty. Spatial domain denoising techniques excel at preserving high-contrast patterns compared to transform domain filters, which perform well in recovering low-contrast details normally smoothed out by spatial domain filters. For spatial domain filtering, the non-local mean algorithm was chosen owing to its performance in denoising high-contrast features whereas multi-scale curvelet denoising was exploited for the transform domain owing to its capability to recover small details. The proposed hybrid method was compared to conventional post-reconstruction Gaussian and edge preserving bilateral filters. Computer simulations of a thorax phantom containing three small lesions, experimental measurements using the Jaszczak phantom and clinical whole-body PET/CT studies were used to evaluate the performance of the proposed PET denoising technique. The proposed hybrid filter increased the SNR from 8.0 (non-filtered PET image) to 39.3 for small lesions in the computerized thorax phantom, while Gaussian and bilateral filtering led to SNRs of 23.3 and 24.4, respectively. For the experimental Jaszczak phantom, the contrast-to-noise ratio (CNR) improved from 10.84 when using Gaussian smoothing to 14.02 and 19.39 using the bilateral and the proposed hybrid filters, respectively. The clinical studies further demonstrated the superior performance of the hybrid method, yielding a quantification change (the original noisy OSEM image was used as reference in the absence f ground truth) in malignant lesions of −2.4% compared to −11.9% and −6.6% achieved using Gaussian and bilateral filters, respectively. In some cases, the visual difference between the ...