Quantifying T 2 relaxation time changes within lesions defined by apparent diffusion coefficient in grey and white matter in acute stroke patients

Abstract

The apparent diffusion coefficient (ADC) of cerebral water, as measured by diffusion MRI, rapidly decreases in ischaemia, highlighting a lesion in acute stroke patients. The MRI T ₂ relaxation time changes in ischaemic brain such that T ₂ in ADC lesions may be informative of the extent of tissue damage, potentially aiding in stratification for treatment. We have developed a novel user-unbiased method of determining the changes in T ₂ in ADC lesions as a function of clinical symptom duration based on voxel-wise referencing to a contralateral brain volume. The spherical reference method calculates the most probable pre-ischaemic T ₂ on a voxel-wise basis, making use of features of the contralateral hemisphere presumed to be largely unaffected. We studied whether T ₂ changes in the two main cerebral tissue types, i.e. in grey matter (GM) and white matter (WM), would differ in stroke. Thirty-eight acute stroke patients were accrued within 9 h of symptom onset and scanned at 3 T for 3D T ₁-weighted, multi b-value diffusion and multi-echo spin echo MRI for tissue type segmentation, quantitative ADC and absolute T ₂ images, respectively. T ₂ changes measured by the spherical reference method were 1.94 ± 0.61, 1.50 ± 0.52 and 1.40 ± 0.54 ms h^-1 in the whole, GM, and WM lesions, respectively. Thus, T ₂ time courses were comparable between GM and WM independent of brain tissue type involved. We demonstrate that T ₂ changes in ADC-delineated lesions can be quantified in the clinical setting in a user unbiased manner and that T ₂ change correlated with symptom onset time, opening the possibility of using the approach as a tool to assess severity of tissue damage in the clinical setting.

Figure 1

Flowchart showing the overall process of obtaining med(ΔT₂) for the lesion, and a calculated ΔT₂ image via the spherical reference method. The illustrated process begins with the four MNI-registered, brain-extracted, input images: ADC, T₂-weighted (echo-summed), quantitative T₂, and T₁-weighted. Stage 1) Lesion Identification. ADC and T₂ images are used to identify the ischaemic lesion and create three masks: the lesion mask; the non-lesion mask (normal tissue without CSF); the non-lesion, lesion-hemisphere mask (normal tissue in the hemisphere of the lesion). Stage 2) Segmentation. GM and WM segmentations are produced from the T₂-weighted image, and a subcortical structure segmentation is produced from the T₁-weighted image. Stage 3) Mask Combination. If GM and WM tissue is to be studied separately, the subcortical segmentation masks are logically removed from the GM and WM segmentation masks, and the resulting masks are logically combined with the three masks of stage (1) to produced cortical GM and WM versions (see centre grey square of the flowchart). Stage 4) Penalty Function Optimisation. The spherical reference algorithm (inputs are T₂ and T₂-weighted images) is executed many times in order optimise the penalty function radius and width. This is achieved by minimising MAD(ΔT₂) + 5|med(ΔT₂)|, where ΔT₂ is calculated between the voxels of the non-lesion, lesion-hemisphere mask and the contralateral hemisphere voxels of the non-lesion mask. Stage 5) Spherical Reference Algorithm. Using the optimised penalty function radius and width from stage (4), and via the T₂ and T₂-weighted images, med(ΔT₂) is calculated for the lesion, using the lesion and non-lesion masks (or their segmented versions). A ΔT₂ image (shown in the top-right of the figure) can be produced by replacing the lesion mask with a lesion-hemisphere mask (not shown in the figure).

Figure 2

Examples of ADC images (a, c, e, g) with lesions demarcated, and quantitative T₂ images (b, d, f, h) with lesions segmented into WM (blue) and GM (red). Panels (a, b): NIHSS at admission, 18; patient age, 45; stroke classification, TACS; onset time, 415 min. Panels (c, d): NIHSS at admission, 10; patient age, 57; stroke classification, LACS; onset time, 569 min. Panels (e, f): NIHSS at admission, 3; patient age, 86; stroke classification, POCS; onset time, 408 min. Panels (g, h): NIHSS at admission, 5; patient age, 80; stroke classification, POCS; onset time, 338 min.

Figure 3

Lesion ADC values. The solid black line is the linear regression with observational 95% confidence intervals displayed by dashed black lines. The displayed error bars are the median absolute deviations. Regression results are given in Table 3.

Figure 4

ADC lesion volumes (in mm³) for the subset of patients with both a hyperacute scan (scan 1) and a follow-up scan (scan 2).

Figure 5

Spherical Reference method regressions for All Tissue, GM and WM lesions. The solid black line is the linear regression with observational 95% confidence intervals displayed by dashed black lines. The ‘error’ bars displayed in the plots are the median absolute deviations (MADs) and represent the spread of values within the lesion or, alternatively, can be regarded as representing the tissue-state heterogeneity of the lesion. Regression results are given in Table 4.

Figure 6

Mirror Reference method regressions for All Tissue, GM and WM lesions. The solid black line is the linear regression with observational 95% confidence intervals displayed by dashed black lines. The ‘error’ bars depicted in the plots are the propagated MADs (see Methods section) from the lesion and contralateral regions. Regression results are given in Table 4.