Adaptive data-driven motion detection and optimized correction for brain PET.
Adaptive data-driven motion detection and optimized correction for brain PET.
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脑PET自适应数据驱动运动检测及优化校正。
DOI：
10.1016/j.neuroimage.2022.119031
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发表时间：
2022-05-15
期刊：
影响因子：
5.7
通讯作者：
Lu, Yihuan
中科院分区：
文献类型：
--
作者：
Revilla, Enette Mae;Gallezot, Jean-Dominique;Naganawa, Mika;Toyonaga, Takuya;Fontaine, Kathryn;Mulnix, Tim A.;Onofrey, John E.;Carson, Richard;Lu, Yihuan
PET扫描过程中的头部运动会导致图像质量下降、高摄取区域的浓度降低以及动态数据集动力学分析的结果测量不正确。在此之前，我们提出了一种数据驱动的方法，示踪剂分布中心（COD），在没有外部运动跟踪设备的情况下检测头部运动。在那里，运动检测使用一维的COD轨迹与半自动检测算法，需要多个用户定义的参数和手动干预。在这项研究中，我们开发了一种新的数据驱动的运动检测算法，它是自动的，自适应噪声水平，不需要用户定义的参数，并使用所有的三维COD轨迹（3DCOD）。首先使用30项模拟研究（18 F-FDG，N = 15; 11 C-raclopride（RAC），N = 15）对3DCOD进行了验证和测试。在22个真实的人体数据集上测试了所提出的运动校正方法，其中20个数据集从高分辨率研究断层扫描仪（18 F-FDG，N = 10; 11 C-RAC，N = 10）获得，2个数据集从Siemens Biograph mCT扫描仪获得。实时基于硬件的运动跟踪信息（Vicra）可用于所有真实的研究，并用作金标准。将3DCOD与Vicra、无运动校正（NMC）、单向COD（我们以前的方法称为1DCOD）和两种传统的基于帧的图像配准（FIR）算法进行比较，即，模拟和真实的研究的FIR 1（基于使用衰减校正重建的预定义帧）和FIR 2（无衰减校正）。对于模拟研究，3DCOD产生-2.3 ± 1.4%（所有受试者和11个脑区的平均值±标准差）18F-FDG的感兴趣区（ROI）摄取误差与Vicra相比（所有受试者和2个地区的11 C-RAC为−3.4 ± 1.7%）（完美校正），而NMC、FIR 1、FIR 2和1DCOD产生−25.4 ± 11.1%（11 C-RAC为−34.5 ± 16.1%）、−13.4 ± 3.5%（−16.1 ± 4.6%）、−5.7 ± 3.6%（−8.0 ± 4.5%）和−2.6 ± 1.5%（−5.1 ± 2.7%）。对于真实的HRT研究，3DCOD产生的18 F-FDG差异为−0.3 ± 2.8%（11 C-RAC为−0.4 ± 3.2%），而NMC、FIR 1、FIR 2和1DCOD为−14.9 ± 9.0%（−24.5 ± 14.6%）、−3.6 ± 4.9%（−13.4 ± 14.3%）、−0.6 ± 3.4%（−6.7 ± 5.3%）和−1.5 ± 4.2%（−2.2 ± 4.1%）。总之，在非TOF扫描仪上进行的研究中，所提出的运动校正方法产生了与基于硬件的多示踪剂运动跟踪方法相当的性能，包括具有大的频繁头部运动的非常具有挑战性的病例。
Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic analysis of dynamic datasets. Previously, we proposed a data-driven method, center of tracer distribution (COD), to detect head motion without an external motion tracking device. There, motion was detected using one dimension of the COD trace with a semiautomatic detection algorithm, requiring multiple user defined parameters and manual intervention. In this study, we developed a new data-driven motion detection algorithm, which is automatic, self-adaptive to noise level, does not require user-defined parameters and uses all three dimensions of the COD trace (3DCOD). 3DCOD was first validated and tested using 30 simulation studies (18F-FDG, N = 15; 11C-raclopride (RAC), N = 15) with large motion. The proposed motion correction method was tested on 22 real human datasets, with 20 acquired from a high resolution research tomograph (HRRT) scanner (18F-FDG, N = 10; 11C-RAC, N = 10) and 2 acquired from the Siemens Biograph mCT scanner. Real-time hardware-based motion tracking information (Vicra) was available for all real studies and was used as the gold standard. 3DCOD was compared to Vicra, no motion correction (NMC), one-direction COD (our previous method called 1DCOD) and two conventional frame-based image registration (FIR) algorithms, i.e., FIR1 (based on predefined frames reconstructed with attenuation correction) and FIR2 (without attenuation correction) for both simulation and real studies. For the simulation studies, 3DCOD yielded −2.3 ± 1.4% (mean ± standard deviation across all subjects and 11 brain regions) error in region of interest (ROI) uptake for 18F-FDG (−3.4 ± 1.7% for 11C-RAC across all subjects and 2 regions) as compared to Vicra (perfect correction) while NMC, FIR1, FIR2 and 1DCOD yielded −25.4 ± 11.1% (−34.5 ± 16.1% for 11C-RAC), −13.4 ± 3.5% (−16.1 ± 4.6%), −5.7 ± 3.6% (−8.0 ± 4.5%) and −2.6 ± 1.5% (−5.1 ± 2.7%), respectively. For real HRRT studies, 3DCOD yielded −0.3 ± 2.8% difference for 18F-FDG (−0.4 ± 3.2% for 11C-RAC) as compared to Vicra while NMC, FIR1, FIR2 and 1DCOD yielded −14.9 ± 9.0% (−24.5 ± 14.6%), −3.6 ± 4.9% (−13.4 ± 14.3%), −0.6 ± 3.4% (−6.7 ± 5.3%) and −1.5 ± 4.2% (−2.2 ± 4.1%), respectively. In summary, the proposed motion correction method yielded comparable performance to the hardware-based motion tracking method for multiple tracers, including very challenging cases with large frequent head motion, in studies performed on a non-TOF scanner.
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