Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg Essen, Essen, Germany.
High Field and Hybrid MR Imaging, University of Duisburg-Essen, Essen, Germany.
PLoS One. 2020 Jun 4;15(6):e0233209. doi: 10.1371/journal.pone.0233209. eCollection 2020.
The impact of a method for MR-based respiratory motion correction of PET data on lesion visibility and quantification in patients with oncologic findings in the lung was evaluated. Twenty patients with one or more lesions in the lung were included. Hybrid imaging was performed on an integrated PET/MR system using 18F-FDG as radiotracer. The standard thoracic imaging protocol was extended by a free-breathing self-gated acquisition of MR data for motion modelling. PET data was acquired simultaneously in list-mode for 5-10 mins. One experienced radiologist and one experienced nuclear medicine specialist evaluated and compared the post-processed data in consensus regarding lesion visibility (scores 1-4, 4 being best), image noise levels (scores 1-3, 3 being lowest noise), SUVmean and SUVmax. Motion-corrected (MoCo) images were additionally compared with gated images. Non-motion-corrected free-breathing data served as standard of reference in this study. Motion correction generally improved lesion visibility (3.19 ± 0.63) and noise ratings (2.95 ± 0.22) compared to uncorrected (2.81 ± 0.66 and 2.95 ± 0.22, respectively) or gated PET data (2.47 ± 0.93 and 1.30 ± 0.47, respectively). Furthermore, SUVs (mean and max) were compared for all methods to estimate their respective impact on the quantification. Deviations of SUVmax were smallest between the uncorrected and the MoCo lesion data (average increase of 9.1% of MoCo SUVs), while SUVmean agreed best for gated and MoCo reconstructions (MoCo SUVs increased by 1.2%). The studied method for MR-based respiratory motion correction of PET data combines increased lesion sharpness and improved lesion activity quantification with high signal-to-noise ratio in a clinical setting. In particular, the detection of small lesions in moving organs such as the lung and liver may thus be facilitated. These advantages justify the extension of the PET/MR imaging protocol by 5-10 minutes for motion correction.
评估了一种基于 MR 的呼吸运动校正 PET 数据的方法对肺部肿瘤患者病灶可见性和定量的影响。共纳入 20 例肺部有 1 个或多个病灶的患者。使用 18F-FDG 作为示踪剂，在集成的 PET/MR 系统上进行混合成像。标准胸部成像方案通过自由呼吸门控采集 MR 数据进行运动建模进行扩展。PET 数据以列表模式采集 5-10 分钟。一名经验丰富的放射科医生和一名经验丰富的核医学专家对处理后的数据进行评估，并就病灶可见性（评分 1-4，4 为最佳）、图像噪声水平（评分 1-3，3 为最低噪声）、SUVmean 和 SUVmax 进行共识评估。此外，还将运动校正（MoCo）图像与门控图像进行比较。在这项研究中，未校正的自由呼吸数据用作标准。与未校正或门控 PET 数据（分别为 2.47 ± 0.93 和 1.30 ± 0.47）相比，运动校正通常可以提高病灶的可见性（3.19 ± 0.63）和噪声评分（2.95 ± 0.22）。此外，还比较了所有方法的 SUV（平均值和最大值），以估计它们对定量的各自影响。MoCo SUV 的 SUVmax 偏差最小（MoCo SUV 增加 9.1%），而 gated 和 MoCo 重建的 SUVmean 最吻合（MoCo SUV 增加 1.2%）。该研究中用于基于 MR 的呼吸运动校正 PET 数据的方法结合了增加病灶锐利度和改善病灶活性定量，同时具有高信噪比，适用于临床环境。特别是，在肺部和肝脏等运动器官中，可能更容易检测到小病灶。这些优势证明 PET/MR 成像方案延长 5-10 分钟进行运动校正具有合理性。
