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利用 DSC-MRI 评估血 ΔR2* 非线性效应对绝对灌注定量的影响:与 Xe-133 SPECT 的比较。

Effects of blood ΔR2* non-linearity on absolute perfusion quantification using DSC-MRI: comparison with Xe-133 SPECT.

机构信息

Department of Medical Radiation Physics, Lund University, Lund, Sweden.

出版信息

Magn Reson Imaging. 2013 Jun;31(5):651-5. doi: 10.1016/j.mri.2012.12.001. Epub 2013 Jan 31.

Abstract

PURPOSE

To evaluate whether a non-linear blood ΔR2*-versus-concentration relationship improves quantitative cerebral blood flow (CBF) estimates obtained by dynamic susceptibility contrast (DSC) MRI in a comparison with Xe-133 SPECT CBF in healthy volunteers.

MATERIAL AND METHODS

Linear as well as non-linear relationships between ΔR2* and contrast agent concentration in blood were applied to the arterial input function (AIF) and the venous output function (VOF) from DSC-MRI. To reduce partial volume effects in the AIF, the arterial time integral was rescaled using a corrected VOF scheme.

RESULTS

Under the assumption of proportionality between the two modalities, the relationship CBF(MRI)=0.58CBF(SPECT) (r=0.64) was observed using the linear relationship and CBF(MRI)=0.51CBF(SPECT) (r=0.71) using the non-linear relationship.

DISCUSSION

A smaller ratio of the VOF time integral to the AIF time integral and a somewhat better correlation between global DSC-MRI and Xe-133 SPECT CBF estimates were observed using the non-linear relationship. The results did not, however, confirm the superiority of one model over the other, potentially because realistic AIF signal data may well originate from a combination of blood and surrounding tissue.

摘要

目的

评估在健康志愿者中,与 Xe-133 SPECT CBF 比较时,通过动态磁敏感对比(DSC)MRI 获得的定量脑血流(CBF)估计值,非线性血 ΔR2*-与浓度关系是否能改善。

材料和方法

将线性和非线性关系应用于 DSC-MRI 的动脉输入函数(AIF)和静脉输出函数(VOF)中的 ΔR2*与血液中的对比剂浓度之间。为了减少 AIF 中的部分容积效应,使用校正的 VOF 方案对动脉时间积分进行了重新缩放。

结果

在线性关系下,假设两种模式之间存在比例关系,观察到 CBF(MRI)=0.58CBF(SPECT)(r=0.64),而在非线性关系下,观察到 CBF(MRI)=0.51CBF(SPECT)(r=0.71)。

讨论

使用非线性关系时,观察到 VOF 时间积分与 AIF 时间积分的比值较小,以及 DSC-MRI 和 Xe-133 SPECT CBF 估计值之间的相关性略好。然而,结果并没有证实一种模型优于另一种模型,这可能是因为实际的 AIF 信号数据很可能源自血液和周围组织的组合。

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