The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images ( P 0.999), similar detail of 99mTc-MDP ( P = 0.125) and the same diagnostic confidence ( P = 0.1875). U 2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT.
Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians.
The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U 2-Net (Qin et al. To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method.
0 kommentar(er)
