深度学习图像去噪技术辅助下的低剂量F-氟脱氧葡萄糖正电子发射断层扫描在淋巴瘤患者中的应用
Low F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma.
作者信息
Yan Lei, Wang Zhao, Li Dacheng, Wang Yangyang, Yang Guangjie, Zhao Yujun, Kong Yan, Wang Rui, Wu Runze, Wang Zhenguang
机构信息
Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
Central Research Institute, Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China.
出版信息
Quant Imaging Med Surg. 2024 Jan 3;14(1):111-122. doi: 10.21037/qims-23-817. Epub 2024 Jan 2.
BACKGROUND
Patients with lymphoma receive multiple positron emission tomography/computed tomography (PET/CT) exams for monitoring of the therapeutic response. With PET imaging, a reduced level of injected fluorine-18 fluorodeoxyglucose ([F]FDG) activity can be administered while maintaining the image quality. In this study, we investigated the efficacy of applying a deep learning (DL) denoising-technique on image quality and the quantification of metabolic parameters and Deauville score (DS) of a low [F]FDG dose PET in patients with lymphoma.
METHODS
This study retrospectively enrolled 62 patients who underwent [F]FDG PET scans. The low-dose (LD) data were simulated by taking a 50% duration of routine-dose (RD) PET list-mode data in the reconstruction, and a U-Net-based denoising neural network was applied to improve the images of LD PET. The visual image quality score (1 = undiagnostic, 5 = excellent) and DS were assessed in all patients by nuclear radiologists. The maximum, mean, and standard deviation (SD) of the standardized uptake value (SUV) in the liver and mediastinum were measured. In addition, lesions in some patients were segmented using a fixed threshold of 2.5, and their SUV, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) were measured. The correlation coefficient and limits of agreement between the RD and LD group were analyzed.
RESULTS
The visual image quality of the LD group was improved compared with the RD group. The DS was similar between the RD and LD group, and the negative (DS 1-3) and positive (DS 4-5) results remained unchanged. The correlation coefficients of SUV in the liver, mediastinum, and lesions were all >0.85. The mean differences of SUV and SUV between the RD and LD groups, respectively, were 0.22 [95% confidence interval (CI): -0.19 to 0.64] and 0.02 (95% CI: -0.17 to 0.20) in the liver, 0.13 (95% CI: -0.17 to 0.42) and 0.02 (95% CI: -0.12 to 0.16) in the mediastinum, and -0.75 (95% CI: -3.42 to 1.91), and -0.13 (95% CI: -0.57 to 0.31) in lesions. The mean differences in MTV and TLG were 0.85 (95% CI: -2.27 to 3.98) and 4.06 (95% CI: -20.53 to 28.64) between the RD and LD groups.
CONCLUSIONS
The DL denoising technique enables accurate tumor assessment and quantification with LD [F]FDG PET imaging in patients with lymphoma.
背景
淋巴瘤患者需接受多次正电子发射断层扫描/计算机断层扫描（PET/CT）检查以监测治疗反应。对于PET成像，在保持图像质量的同时可减少氟-18氟脱氧葡萄糖（[F]FDG）的注射剂量。在本研究中，我们探讨了应用深度学习（DL）去噪技术对淋巴瘤患者低剂量[F]FDG PET图像质量、代谢参数定量及迪沃利评分（DS）的影响。
方法
本研究回顾性纳入62例行[F]FDG PET扫描的患者。通过在重建过程中采用常规剂量（RD）PET列表模式数据50%的时长来模拟低剂量（LD）数据，并应用基于U-Net的去噪神经网络改善LD PET图像。核放射科医生对所有患者的视觉图像质量评分（1 = 无法诊断，5 = 优秀）和DS进行评估。测量肝脏和纵隔标准化摄取值（SUV）的最大值、平均值及标准差（SD）。此外，对部分患者的病变采用2.5的固定阈值进行分割，并测量其SUV、代谢肿瘤体积（MTV）及肿瘤病变糖酵解（TLG）。分析RD组与LD组之间的相关系数及一致性界限。
结果
与RD组相比，LD组的视觉图像质量得到改善。RD组与LD组的DS相似，阴性（DS 1 - 3）和阳性（DS 4 - 5）结果保持不变。肝脏、纵隔及病变部位SUV的相关系数均>0.85。RD组与LD组肝脏SUV的平均差值分别为0.22 [95%置信区间（CI）：-0.19至0.64]和0.02（95% CI：-0.17至0.20），纵隔分别为0.13（95% CI：-0.17至0.42）和0.02（95% CI：-0.12至0.16），病变部位分别为-0.75（95% CI：-3.42至1.91）和-0.13（95% CI：-0.57至0.31）。RD组与LD组MTV和TLG的平均差值分别为0.85（95% CI：-2.27至3.98）和4.06（95% CI：-20.53至28.64）。
结论
DL去噪技术可使淋巴瘤患者通过低剂量[F]FDG PET成像进行准确的肿瘤评估和定量分析
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