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DCE-MRI 的统一脉冲响应模型。

A unified impulse response model for DCE-MRI.

机构信息

Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, USA.

出版信息

Magn Reson Med. 2012 Nov;68(5):1632-46. doi: 10.1002/mrm.24162. Epub 2012 Jan 31.

Abstract

We describe the gamma capillary transit time model, a generalized impulse response model for DCE-MRI that mathematically unifies the Tofts-Kety, extended Tofts-Kety, adiabatic tissue homogeneity, and two-compartment exchange models. By including a parameter (α⁻¹) representing the width of the distribution of capillary transit times within a tissue voxel, the GCTT model discriminates tissues having relatively monodisperse transit time distributions from those having a large degree of heterogeneity. All five models were compared using in vivo data acquired in three brain tumors (one glioblastoma multiforme, one pleomorphic xanthoastrocytoma, and one anaplastic meningioma) and Monte Carlo simulations. Our principal findings are : (1) The four most commonly used models for dynamic contrast-enhanced magnetic resonance imaging can be unified within a single formalism. (2) Application of the GCTT model to in vivo data incurs only modest penalties in parameter uncertainty and computational cost. (3) Measured nonparametric impulse response functions in human brain tumors are well described by the GCTT model. (4) Estimation of α⁻¹ is feasible but achieving statistical significance requires higher SNR than is typically obtained in single voxel dynamic contrast-enhanced magnetic resonance imaging data. These results suggest that the GCTT model may be useful for extraction of information about tumor physiology beyond what is obtained using current modeling methodologies.

摘要

我们描述了伽马毛细血管渡越时间模型,这是一种用于 DCE-MRI 的广义脉冲响应模型,它从数学上统一了 Tofts-Kety、扩展 Tofts-Kety、绝热组织均匀性和双室交换模型。通过包含一个参数(α⁻¹)来表示组织体素内毛细血管渡越时间分布的宽度,GCTT 模型可以区分具有相对单分散渡越时间分布的组织与具有较大异质性的组织。使用在三个脑肿瘤(一个多形性胶质母细胞瘤、一个多形性黄色星形细胞瘤和一个间变性脑膜瘤)和蒙特卡罗模拟中获得的体内数据比较了所有五个模型。我们的主要发现是:(1)动态对比增强磁共振成像中最常用的四个模型可以在单个形式中统一。(2)将 GCTT 模型应用于体内数据仅会导致参数不确定性和计算成本适度增加。(3)在人类脑肿瘤中测量的非参数脉冲响应函数可以很好地用 GCTT 模型描述。(4)α⁻¹的估计是可行的,但要达到统计学意义,需要比在单体积动态对比增强磁共振成像数据中通常获得的更高的 SNR。这些结果表明,GCTT 模型可能有助于提取比当前建模方法获得的关于肿瘤生理学的信息。

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