Background Quantitative bone SPECT/CT is useful for disease follow up and inter-patient comparison. For bone metastatic malignant lesions, spine is the most commonly invaded site. However, Quantitative studies with large sample size investigating all the segments of normal cervical, thoracic and lumbar vertebrae are seldom reported. This study was to evaluate the quantitative tomography of normal vertebrae using 99mTc-MDP with SPECT/CT to investigate the feasibility of standardized uptake value (SUV) for differential diagnosis of benign and malignant bone lesions. Methods A retrospective study was carried out involving 221 patients (116 males and 105 females) who underwent SPECT/CT scan using 99mTc-MDP. The maximum SUV (SUVmax), mean SUV (SUVmean) and CT values (Hounsfield Unit, HU) of 2416 normal vertebrae bodies, 157 benign bone lesions and 118 malignant bone metastasis foci were obtained. The correlations between SUVmax of normal vertebrae and CT values of normal vertebrae, age, height, weight, BMI of patients were analyzed. Statistical analysis was performed with data of normal, benign and malignant groups corresponding to same sites and gender. Results The SUVmax and SUVmean of normal vertebrae in males were markedly higher than those in females (P < 0.0009). The SUVmax of each normal vertebral segment showed a strong negative correlation with CT values in both males and females (r = − 0.89 and − 0.92, respectively; P < 0.0009). The SUVmax of normal vertebrae also showed significant correlation with weight, height, and BMI in males (r = 0.4, P < 0.0009; r = 0.28, P = 0.005; r = 0.22, P = 0.026), and significant correlation with weight and BMI in females (r = 0.32, P = 0.009; r = 0.23, P = 0.031). The SUVmax of normal group, benign bone lesion group and malignant bone metastasis foci group showed statistical differences in both males and females. Conclusion Our study evaluated SUVmax and SUVmean of normal vertebrae, benign bone lesion and malignant bone metastasis foci with a large sample population. Preliminary results proved the potential value of SUVmax in differentiation benign and malignant bone lesions. The results may provide a quantitative reference for clinical diagnosis and the evaluation of therapeutic response in vertebral lesions.
Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods 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 U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P > 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.
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