LI Fengqi, SU Ya, BAI Mei, LU Jie
Objective To propose a denoising method for positron emission tomography (PET) images based on adaptive hybrid filtering and verify its denoising effect at different acquisition times. Methods By dynamically adjusting the filtering window size, the appropriate filtering parameters were selected to adapt to different noise levels. To verify the effectiveness of the method, PET images of 33 patients with brain tumors were used, and three different signal acquisition times (3, 5, and 7 min) were adopted as experimental samples. By comparing the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) and mean squared error (MSE) between pixels with the traditional filtering methods, the denoising effect of the adaptive hybrid filter was evaluated. Results The experimental results showed that when the signal acquisition time was 3 min, the adaptive hybrid filtering method [PSNR: (60.9569±2.0467) dB, SSIM: 0.9960±0.0015, the MAE between pixels: 6.6901±2.1756, MSE: 0.0571±0.0217] had a better denoising effect than the traditional method, and the difference was statistically significant (P<0.05). When the acquisition time was 5 min, the results of PSNR, MAE and MSE between pixels obtained by adaptive hybrid filtering [PSNR: (62.0394±2.2481) dB, SSIM: 0.9971±0.0012, the MAE between pixels: 4.7381±1.7955, MSE: 0.0450±0.0174] were superior to those of other methods, and the difference was statistically significant (P<0.05). When the acquisition time was 7 min, the PSNR, MAE and MSE values between the pixels of the adaptive hybrid filtering [PSNR: (62.7323±2.2265) dB, SSIM: 0.9976±0.0009, MAE: 3.8339±1.5475, MSE: 0.0384±0.0149] were significantly better than those of other methods, and the difference was statistically significant (P<0.05). The visual scores of adaptive hybrid filtering at the three acquisition times (3 min: 2.64±0.50; 5 min: 3.18±0.50; 7 min: 3.55±0.46) were better than those of other methods. Conclusion The adaptive hybrid filtering method can reduce noise while preserving the edges and details of the image. It is suitable for denoising PET images and demonstrates better denoising performance on images with different noise levels, making it more robust.