Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.
Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.
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DOI：
10.1088/0031-9155/60/22/8643
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发表时间：
2015-11-21
影响因子：
3.5
通讯作者：
Rahmim A
中科院分区：
文献类型：
--
作者：
Karakatsanis NA;Zhou Y;Lodge MA;Casey ME;Wahl RL;Zaidi H;Rahmim A
我们最近开发了一种动态多床 PET 数据采集框架，将 Patlak 体素分析的定量优势转化为常规临床全身 (WB) 成像领域。标准 Patlak (sPatlak) 线性图形分析假设 PET 示踪剂摄取不可逆，忽略了 FDG 去磷酸化的影响，许多 PET 研究已表明这一点。在这项工作中：（i）利用非线性广义Patlak（gPatlak）模型，包括净流出率常数kloss，（ii）引入混合（s/g）Patlak（hPatlak）成像技术来增强摄取率Ki图像的对比度与噪声比（CNR）。采用一组代表性的动力学参数值和 XCAT 模型来生成真实的 4D 模拟 PET 数据，并且所提出的方法还在 11 名 WB 动态 PET 患者研究中进行了评估。对 2 组感兴趣区域 (ROI) 的模拟 Ki 图像进行定量分析，相对于 Ki 具有低 (ROI A) 或高 (ROI B) 真实 kloss，表明 gPatlak 具有卓越的准确性。发现对于 ROI A 和 B，sPatlak 的偏差分别比 gPatlak 差 16-18% 和 20-40%。相比之下，gPatlak 的噪声平均比 sPatlak 高 10%。同时，hPatlak 的偏差和噪声水平始终介于其他两种方法之间。总的来说，hPatlak 在所有 ROI 的目标背景比 (TBR) 和 CNR 方面优于所有方法。对患者数据集的验证证明了所有 Patlak 方法的临床可行性，而 TBR 和 CNR 评估证实了我们的模拟结果，并表明临床数据中存在不可忽略的 kloss 可逆性。因此，我们建议将 gPatlak 用于高度定量的成像任务，而对于强调病变可检测性（例如 TBR、CNR）而不是定量的任务，或者对于高水平的噪声，则首选 hPatlak。最后，与常规 SUV 值相比，gPatlak 和 hPatlak CNR 系统性更高。
We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16–18% and 20–40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.