Evaluating and reducing the impact of white matter lesions on brain volume measurements

Abstract

MR-based measurements of brain volumes may be affected by the presence of white matter (WM) lesions. Here, we assessed how and to what extent this may happen for WM lesions of various sizes and intensities. After inserting WM lesions of different sizes and intensities into T1-W brain images of healthy subjects, we assessed the effect on two widely used automatic methods for brain volume measurement such as SIENAX (segmentation-based) and SIENA (registration-based). To explore the relevance of partial volume (PV) estimation, we performed the experiments with two different PV models, implemented by the same segmentation algorithm (FAST) of SIENAX and SIENA. Finally, we tested potential solutions to this issue. The presence of WM lesions did not bias measurements for registration-based method such as SIENA. By contrast, the presence of WM lesions affected segmentation-based brain volume measurements such as SIENAx. The misclassification of both gray matter (GM) and WM volumes varied considerably with lesion size and intensity, especially when the lesion intensity was similar to that of the GM/WM interface. The extent to which the presence of WM lesions could affect tissue-class measures was clearly driven by the PV modeling used, with the mixel-type PV model giving a lower error in the presence of WM lesions. The tissue misclassification due to WM lesions was still present when they were masked out. By contrast, refilling the lesions with intensities matching the surrounding normal-appearing WM ensured accurate tissue-class measurements and thus represents a promising approach for accurate tissue classification and brain volume measurements.

Figure 1

Illustrative example of the creation of four different Gaussian intensity distributions (CSF, CSF/GM, GM, and GM/WM) from the histogram intensity of an “original” image (top). The binarized regions of interest of the “artificial” images (previously created from the “original” image and a given lesion mask, see methods for details) were then filled by randomly extracting voxel values from each of the four different Gaussian intensity distributions (Fig. 1‐ bottom).

Figure 2

The graphs illustrate the percentage differences (y‐axis, defined as 100 × (V ₂ − V ₁)/V ₁, where V ₁ and V ₂ represent the first and the second volume measurement, respectively) in the segmentation‐based measurements (as assessed by SIENAX) of NBV (pd‐NBV, top panels), NWMV (pd‐NWMV, central panels), and NGMV (pd‐NGMV, bottom panels) when lesions were inserted into the “original” T1‐weighted images with an increasing lesion load (x‐axis) and different intensities ( for GM/WM interface, for GM, for CSF/GM and for CSF). The analysis was performed by using two different partial volume approaches as provided by FAST‐3 (left column) and FAST‐4 (right column) (see Methods for details). Each dot and vertical line in the graphs represents the mean and standard deviations of the five percentage differences obtained by comparing each “original” T1‐weighted image with the related “artificial” T1‐ weighted image of a given lesion load and intensity.

Figure 3

The graphs illustrate the percentage brain volume changes (PBVC, y‐axis) in the registration‐based measurements (as assessed by SIENA) when lesions were inserted into the “original” T1‐weighted images with an increasing lesion load (x‐axis) and different intensities ( for GM/WM interface, for GM, for CSF/WM and for CSF). The analysis was performed by using two different partial volume approaches as provided by FAST‐3 (left column) and FAST‐4 (right column) (see Methods for details). Each dot and vertical line in the graphs represents the mean and standard deviations of the five percentage differences obtained by comparing each “original” T1‐weighted image with the related “artificial” T1‐ weighted image of a given lesion load and intensity.

Figure 4

The graphs illustrate the regression lines of NGMV measurements (y‐axis) in relation to lesion load (x‐axis) when lesions were either “masked out” ( ) or “refilled” with the surrounding normal‐appearing WM (). Values of NGMV at 0 lesion load are given by those of the “original” T1‐weighed images of the healthy controls. The analysis was performed by using two different partial volume approaches as provided by FAST‐3 (left column) and FAST‐4 (right column) (see Methods for details).