Weill Cornell Medical College, New York, NY 10021, USA.
AJNR Am J Neuroradiol. 2013 Jun-Jul;34(6):1168-74. doi: 10.3174/ajnr.A3368. Epub 2012 Dec 20.
Accurate diagnosis of normal pressure hydrocephalus is challenging because the clinical symptoms and radiographic appearance of NPH often overlap those of other conditions, including age-related neurodegenerative disorders such as Alzheimer and Parkinson diseases. We hypothesized that radiologic differences between NPH and AD/PD can be characterized by a robust and objective MR imaging DTI technique that does not require intersubject image registration or operator-defined regions of interest, thus avoiding many pitfalls common in DTI methods.
We collected 3T DTI data from 15 patients with probable NPH and 25 controls with AD, PD, or dementia with Lewy bodies. We developed a parametric model for the shape of intracranial mean diffusivity histograms that separates brain and ventricular components from a third component composed mostly of partial volume voxels. To accurately fit the shape of the third component, we constructed a parametric function named the generalized Voss-Dyke function. We then examined the use of the fitting parameters for the differential diagnosis of NPH from AD, PD, and DLB.
Using parameters for the MD histogram shape, we distinguished clinically probable NPH from the 3 other disorders with 86% sensitivity and 96% specificity. The technique yielded 86% sensitivity and 88% specificity when differentiating NPH from AD only.
An adequate parametric model for the shape of intracranial MD histograms can distinguish NPH from AD, PD, or DLB with high sensitivity and specificity.
常压性脑积水的准确诊断具有挑战性,因为 NPH 的临床症状和影像学表现常与其他疾病重叠,包括阿尔茨海默病和帕金森病等与年龄相关的神经退行性疾病。我们假设,NPH 和 AD/PD 之间的放射学差异可以通过一种强大而客观的磁共振成像 DTI 技术来描述,该技术不需要进行受试者间图像配准或操作员定义的感兴趣区域,从而避免了 DTI 方法中常见的许多陷阱。
我们从 15 例可能的 NPH 患者和 25 例 AD、PD 或路易体痴呆患者中收集了 3T DTI 数据。我们开发了一种用于颅内平均弥散率直方图形状的参数模型,该模型将脑和脑室成分与主要由部分容积体素组成的第三成分分开。为了准确拟合第三成分的形状,我们构建了一个名为广义 Voss-Dyke 函数的参数函数。然后,我们检查了使用拟合参数对 NPH 与 AD、PD 和 DLB 进行鉴别诊断的效果。
使用 MD 直方图形状的参数,我们以 86%的灵敏度和 96%的特异性从其他 3 种疾病中区分出临床可能的 NPH。当仅将 NPH 与 AD 区分时,该技术的灵敏度为 86%,特异性为 88%。
对于颅内 MD 直方图形状的充分参数模型可以以高灵敏度和特异性区分 NPH 与 AD、PD 或 DLB。