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基于 18F-FDG PET 的双时相代谢摄取率定量。

Dual time point based quantification of metabolic uptake rates in 18F-FDG PET.

机构信息

PET Center, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, 01328, Germany.

出版信息

EJNMMI Res. 2013 Mar 13;3(1):16. doi: 10.1186/2191-219X-3-16.

DOI:10.1186/2191-219X-3-16
PMID:23497553
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3717002/
Abstract

BACKGROUND

Assessment of dual time point (DTP) positron emission tomography was carried out with the aim of a quantitative determination of Km, the metabolic uptake rate of [18F]fluorodeoxyglucose as a measure of glucose consumption.

METHODS

Starting from the Patlak equation, it is shown that Km≈mt/ca0+V̄r/τa, where mt is the secant slope of the tissue response function between the dual time point measurements centered at t = t0. ca0=ca(t0) denotes arterial tracer concentration, V̄r is an estimate of the Patlak intercept, and τa is the time constant of the ca(t) decrease. We compared the theoretical predictions with the observed relation between Ks=mt/ca0 and Km in a group of nine patients with liver metastases of colorectal cancer for which dynamic scans were available, and Km was derived from conventional Patlak analysis. Twenty-two lesion regions of interest (ROIs) were evaluated. ca(t) was determined from a three-dimensional ROI in the aorta. Furthermore, the correlation between Km and late standard uptake value (SUV) as well as retention index was investigated. Additionally, feasibility of the approach was demonstrated in a whole-body investigation.

RESULTS

Patlak analysis yielded a mean Vr of V̄r=0.53±0.08 ml/ml. The patient averaged τa was 99 ± 23 min. Linear regression between Patlak-derived Km and DTP-derived Ks according to Ks = b · Km + a yielded b = 0.98 ± 0.05 and a = -0.0054 ± 0.0013 ml/min/ml (r = 0.98) in full accordance with the theoretical predictions b = 1 and a≈-V̄r/τa. Ks exhibits better correlation with Km than late SUV and retention index, respectively. Ks(c)=Ks+V̄r/τa is proposed as a quantitative estimator of Km which is independent of patient weight, scan time, and scanner calibration.

CONCLUSION

Quantification of Km from dual time point measurements compatible with clinical routine is feasible. The proposed approach eliminates the issues of static SUV and conventional DTP imaging regarding influence of chosen scanning times and inter-study variability of the input function. Ks and Ks(c) exhibit improved stability and better correlation with the true Km. These properties might prove especially relevant in the context of radiation treatment planning and therapy response control.

摘要

背景

通过双时间点(DTP)正电子发射断层扫描(PET)评估,旨在对[18F]氟脱氧葡萄糖(FDG)的代谢摄取率 Km 进行定量测定,以作为葡萄糖消耗的指标。

方法

从 Patlak 方程出发,证明 Km≈mt/ca0+V̄r/τa,其中 mt 是双时间点测量的组织响应函数的割线斜率,以 t = t0 为中心。ca0=ca(t0)表示动脉示踪剂浓度,V̄r 是 Patlak 截距的估计值,τa 是 ca(t)降低的时间常数。我们将理论预测与 9 例结直肠癌肝转移患者的动态扫描中观察到的 Km 与 Ks=mt/ca0 之间的关系进行了比较,其中 Km 是通过传统的 Patlak 分析得出的。对 22 个病灶感兴趣区(ROI)进行了评估。从主动脉的三维 ROI 中确定 ca(t)。此外,还研究了 Km 与晚期标准摄取值(SUV)和保留指数的相关性。此外,还在全身研究中证明了该方法的可行性。

结果

Patlak 分析得出 Vr 的平均值为 V̄r=0.53±0.08 ml/ml。患者平均 τa 为 99±23 min。根据 Ks=bm·Km+a,用 Patlak 法得出的 Km 和 DTP 法得出的 Ks 之间的线性回归得出 b=0.98±0.05 和 a=-0.0054±0.0013 ml/min/ml(r=0.98),这与理论预测 b=1 和 a≈-V̄r/τa 完全一致。Ks 与 Km 的相关性优于晚期 SUV 和保留指数。建议 Ks(c)=Ks+V̄r/τa 作为 Km 的定量估计量,该估计量与患者体重、扫描时间和扫描仪校准无关。

结论

从符合临床常规的双时间点测量中对 Km 进行定量是可行的。所提出的方法消除了静态 SUV 和传统 DTP 成像在所选扫描时间和输入函数的研究间变异性方面的问题。Ks 和 Ks(c)具有更高的稳定性,与真实 Km 的相关性更好。这些特性在放射治疗计划和治疗反应控制方面可能具有特别重要的意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/9689c98aadee/2191-219X-3-16-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/e0f3c69b789c/2191-219X-3-16-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/4ccfe24e5393/2191-219X-3-16-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/8b30d4417eaf/2191-219X-3-16-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/c9ca3c9dcb26/2191-219X-3-16-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/6ef8d0a1ce69/2191-219X-3-16-5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/216bd47036c2/2191-219X-3-16-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/9689c98aadee/2191-219X-3-16-7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/e0f3c69b789c/2191-219X-3-16-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/4ccfe24e5393/2191-219X-3-16-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/8b30d4417eaf/2191-219X-3-16-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/c9ca3c9dcb26/2191-219X-3-16-4.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/216bd47036c2/2191-219X-3-16-6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5067/3717002/9689c98aadee/2191-219X-3-16-7.jpg

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