Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar der Technischen Universität München, Ismaninger Strasse 22, Munich, Germany.
Mol Imaging. 2010 Dec;9(6):319-28.
Positron emission tomography-computed tomography (PET-CT) is superior compared to stand-alone PET in evaluation of malignancies. Few studies have employed high-resolution structural information to correct PET. We designed a semiautomatic algorithm using CT and PET to obtain a partial volume corrected (PVC) standardized uptake value (SUV) and a combined morphologic and functional parameter (multimodal SUV) for lymph node assessment. Lesions were segmented by a semiautomatic algorithm in CT images. Lesion volume was used for PVC and for calculating the multimodal SUV. The method was applied to 47 lymph nodes (30 patients) characterized as suspicious in 18F-fluorodeoxyglucose-PET-CT. In phantoms, PVC improved significantly the measured uptake of the lesion. In patients, 36 lymph nodes could be segmented without problems; in 11 lesions, a manual interaction was necessary. SUVs before PVC (mean 1.29) increased significantly (p < .0005) after PVC (mean 2.8). If SUV 2.5 was used as a threshold value to distinguish between benign and malignant lesions, 11 of the 47 lesions changed from benign to malignant after the PVC. The mean multimodal SUV was 0.39 mL for the benign lesions and 4.47 mL for the malignant lesions. In this work we presented a method for quantitative analysis of lymph nodes in PET-CT. PVC leads to significant differences in SUV.
正电子发射断层扫描计算机断层扫描(PET-CT)在恶性肿瘤评估方面优于单独的 PET。少数研究采用高分辨率结构信息来校正 PET。我们设计了一种使用 CT 和 PET 的半自动算法,以获得部分容积校正(PVC)标准化摄取值(SUV)和用于淋巴结评估的形态和功能组合参数(多模态 SUV)。病变通过 CT 图像中的半自动算法进行分割。病变体积用于 PVC 和计算多模态 SUV。该方法应用于在 18F-氟代脱氧葡萄糖-PET-CT 中被认为可疑的 47 个淋巴结(30 名患者)。在体模中,PVC 显著改善了病变的摄取测量值。在患者中,36 个淋巴结可以无问题地进行分割;在 11 个病变中,需要手动交互。在 PVC 之前的 SUV(平均值 1.29)在 PVC 后显著增加(p <.0005)(平均值 2.8)。如果 SUV 2.5 被用作区分良性和恶性病变的阈值,则 47 个病变中有 11 个在 PVC 后从良性变为恶性。良性病变的平均多模态 SUV 为 0.39 mL,恶性病变的平均多模态 SUV 为 4.47 mL。在这项工作中,我们提出了一种用于 PET-CT 中淋巴结定量分析的方法。PVC 导致 SUV 有显著差异。