Differential diagnosis of normal pressure hydrocephalus by MRI mean diffusivity histogram analysis

Abstract

Background and purpose: 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.

Materials and 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.

Results: 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.

Conclusions: An adequate parametric model for the shape of intracranial MD histograms can distinguish NPH from AD, PD, or DLB with high sensitivity and specificity.

Fig 1.

The MD histograms of a healthy subject (top left), a patient with AD (bottom left), and a patient with NPH (top and bottom right). MD histogram of the same patient with NPH was fitted with the original Voss-Dyke function (top right) and with the proposed generalized Voss-Dyke function (bottom right).

Fig 2.

The voxels with MD values between 1E-3 mm²/s and 4E-3 mm²/s for a patient with NPH. Notice the area of increased MD in the periventricular white matter (green arrows). Values above 3E-3 mm²/s correspond mostly to ventricles, whereas values below 1E-3 mm²/s are primarily within the brain parenchyma (gray).

Fig 3.

The original Voss-Dyke function is in blue; the generalized function is in red. In the center is homotopy mapping μ_θ(t) = t^θμ_brain + (1 − t^θ)μ_CSF. Right, for the original Voss-Dyke function, case θ = 1. The intermediate Gaussian distributions are equally between P_CSF(MD) and P_brain(MD). Left, in the case θ = 0.4, the intermediate distributions are skewed toward P_brain(MD), giving the slope to the generalized Voss-Dyke function. The inserts on the left and right represent the Voss-Dyke functions resulting as the sum of the corresponding Gaussians.

Fig 4.

Fraction of the midrange MD voxels (f_mix) versus θ. Lower values of θ mean a higher number of voxels, with MD values closer to parenchyma MD values and a lower number of the voxels with MD values closer to CSF. The classification line presented on the graph is θ_out = −0.4 + 2.1 * f_mix.

Fig 5.

The receiver operating characteristic curves for the patients with NPH classification against patients with AD only (left) and the joint AD, PD, and LBD group (right). The receiver operating characteristic points presented are for the cutoff lines of the form θ_out = −0.4 + β * f_mix, β ∈ (1, 3.5), with the best cutoff obtained by β = 2.1.