基于数据驱动的 PET 数据呼吸运动补偿在心脏 PET/CT 和 PET/MR 成像中的应用。
Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging.
机构信息
QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Artificial Intelligence in Medicine program, Cedars-Sinai Medical Center, Los Angeles, California, USA.
出版信息
J Nucl Cardiol. 2020 Dec;27(6):2216-2230. doi: 10.1007/s12350-019-01613-2. Epub 2019 Feb 13.
BACKGROUND
Respiratory patient motion causes blurring of the PET images that may impact accurate quantification of perfusion and infarction extents in PET myocardial viability studies. In this study, we investigate the feasibility of correcting for respiratory motion directly in the PET-listmode data prior to image reconstruction using a data-driven, projection-based, respiratory motion compensation (DPR-MoCo) technique.
METHODS
The DPR-MoCo method was validated using simulations of a XCAT phantom (Biograph mMR PET/MR) as well as experimental phantom acquisitions (Biograph mCT PET/CT). Seven patient studies following a dual-tracer (F-FDG/N-NH) imaging-protocol using a PET/MR-system were also evaluated. The performance of the DPR-MoCo method was compared against reconstructions of the acquired data (No-MoCo), a reference gate method (gated) and an image-based MoCo method using the standard reconstruction-transform-average (RTA-MoCo) approach. The target-to-background ratio (TBR) in the myocardium and the noise in the liver (CoV) were evaluated for all acquisitions. For all patients, the clinical effect of the DPR-MoCo was assessed based on the end-systolic (ESV), the end-diastolic volumes (EDV) and the left ventricular ejection fraction (EF) which were compared to functional values obtained from the cardiac MR.
RESULTS
The DPR-MoCo and the No-MoCo images presented with similar noise-properties (CoV) (P = .12), while the RTA-MoCo and reference-gate images showed increased noise levels (P = .05). TBR values increased for the motion limited reconstructions when compared to the No-MoCo reconstructions (P > .05). DPR-MoCo results showed higher correlation with the functional values obtained from the cardiac MR than the No-MoCo results, though non-significant (P > .05).
CONCLUSION
The projection-based DPR-MoCo method helps to improve PET image quality of the myocardium without the need for external devices for motion tracking.
背景
呼吸病人运动导致 PET 图像模糊，可能会影响 PET 心肌存活研究中灌注和梗塞程度的准确量化。在这项研究中，我们研究了在图像重建之前，使用基于数据的、基于投影的呼吸运动补偿（DPR-MoCo）技术，直接在 PET 列表模式数据中校正呼吸运动的可行性。
方法
使用 XCAT 体模（Biograph mMR PET/MR）的模拟以及实验体模采集（Biograph mCT PET/CT）对 DPR-MoCo 方法进行了验证。还评估了使用 PET/MR 系统进行双示踪剂（F-FDG/N-NH）成像方案的七名患者研究。DPR-MoCo 方法的性能与采集数据的重建（无 MoCo）、参考门控方法（门控）和使用标准重建-变换-平均（RTA-MoCo）方法的基于图像的 MoCo 方法进行了比较。评估了所有采集的心肌靶背景比（TBR）和肝脏噪声（CoV）。对于所有患者，基于收缩末期（ESV）、舒张末期容积（EDV）和左心室射血分数（EF），评估了 DPR-MoCo 的临床效果，并将其与心脏磁共振获得的功能值进行了比较。
结果
DPR-MoCo 和无 MoCo 图像的噪声特性（CoV）相似（P =.12），而 RTA-MoCo 和参考门控图像显示噪声水平增加（P =.05）。与无 MoCo 重建相比，运动受限重建的 TBR 值增加（P >.05）。与无 MoCo 结果相比，DPR-MoCo 结果与心脏磁共振获得的功能值相关性更高，但无统计学意义（P >.05）。
结论
基于投影的 DPR-MoCo 方法有助于在不使用外部运动跟踪设备的情况下提高心肌的 PET 图像质量。
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