Zanderigo Francesca, Mann J John, Ogden R Todd
Molecular Imaging and Neuropathology Division, New York State Psychiatric Institute, New York, New York, United States of America.
Department of Psychiatry, Columbia University, New York, New York, United States of America.
PLoS One. 2017 May 1;12(5):e0176636. doi: 10.1371/journal.pone.0176636. eCollection 2017.
Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study.
HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region.
For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND.
HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist.
为了对正电子发射断层显像(PET)示踪剂与其感兴趣的靶点的特异性结合进行定量分析,需要估计其不可置换分布容积(VND)。通常认为VND在全脑范围内具有可比性,其测定方法要么是通过一个不含靶点的参考区域(许多示踪剂和靶点往往没有这样的区域),要么是在使用对靶点具有高亲和力的另一种分子阻断靶点前后对每个受试者进行成像(这种方法繁琐且会增加辐射暴露)。在此,我们提出并针对示踪剂[11C]DASB和[11C]CUMI - 101验证了一种新的数据驱动的混合反卷积方法(HYDECA),该方法可在个体水平上确定VND,而无需参考区域或阻断研究。
HYDECA需要血浆中经代谢物校正的示踪剂浓度曲线,并使用奇异值分解从测量的时间 - 活度曲线估计多个脑区的脉冲响应函数。HYDECA将每个区域的脉冲响应函数分解为一个参数化的不可置换成分(它是VND的函数,假设在各区域相同)和一个非参数化的特异性成分之和。这两个成分对每个脉冲响应函数的贡献不同寻常。不同区域显示出这两个成分的不同贡献,HYDECA通过检查跨区域的数据来找到合适的共同VND。HYDECA的实现需要确定两个调整参数,我们针对给定的示踪剂提出了两种客观选择这些参数的策略:使用来自阻断研究的数据以及对示踪剂进行实际模拟。利用现有的重测数据,我们将HYDECA对VND和结合势的估计与基于使用所谓参考区域估计的VND所获得的结果进行比较。
对于[11C]DASB和[11C]CUMI - 101,我们发现无论用于优化调整参数的策略如何,与通常使用非理想参考区域所获得的结果相比,HYDECA对VND的估计偏差要小得多。当考虑结合势BPP和BPND时,HYDECA的重测可重复性与使用从非理想参考区域确定的VND所获得的可重复性相当。
对于不存在有效参考区域的示踪剂和靶点,HYDECA无需进行阻断研究即可提供个体特异性的VND估计。