Diffusion tensor MR imaging of the brain: effect of diffusion weighting on trace and anisotropy measurements

Abstract

Background and purpose: In human brain, the relationship between MR signal and b value is complicated by cerebral perfusion, restricted diffusion, anisotropy, cellular membrane permeability, and active cellular transport of water molecules. Our purpose was to evaluate the effect of the number and strength of diffusion-sensitizing gradients on measured isotropic apparent diffusion coefficients (ADCi), fractional anisotropy (FA), and their respective SD in different anatomic locations of the brain.

Methods: Quantitative apparent diffusion coefficients and diffusion anisotropy brain maps were obtained from 10 healthy volunteers by using six different levels of diffusion weighting (b0 = 0, bl = 160, b2 = 320, b3 = 480, b4 = 640, and b5 = 800 s/mm2), applied sequentially in six different directions (Gxx, Gyy, Gzz, Gxy, Gxz, Gyz) and coupled to a single-shot spin-echo echo-planar (2,045/115 [TR/TE]) MR imaging technique. ADCi, FA, eigenvalues (lambda1, lambda2, lamdba3)1 of the principal eigenvectors, and their respective SD were measured from seven different anatomic locations in the brain. Repeated measures analysis of variance was used to evaluate for the existence of significant differences in the average and SD of the calculated ADCi and FA as a function of the number and strength of b values. When a difference existed, the Bonferroni t method was used for paired comparisons of the groups.

Results: The measured ADCi was affected by the number and strength of b values (P < .05). The SD of the ADCi was affected by the strength (P < .05) but not the number of b values (P > .05). The measured FA was unaffected by the number and strength of b values (P > .05). The SD was affected by the number and strength of b values (P < .05).

Conclusion: The number and strength of b values do influence measures of diffusion and anisotropy. Attention to the choice of diffusion sensitization parameters is important in decisions regarding clinical feasibility (acquisition time) and normative measures.

fig 1.

Axial ADC_i, FA, and eigenvalue maps. A, ROI used in data analysis are superimposed on axial ADC_i map and FA map calculated at the level of the basal ganglia. 1, left putamen; 2, right putamen; 3, genu of corpus callosum; 4, splenium of the corpus callosum. ROI were also placed in the pons and centrum semiovale (not shown). The location and size of the ROI varied across participants and hemispheres but was kept fixed across the different maps. B, Axial calculated eigenvalue maps (λ1, λ2, λ3) at the level of the basal ganglia.

fig 2.

Signal decay versus b value obtained from diffusion-weighted (six directions) MR images of a water phantom. The monoexponential decay indicates minimal influence of experimental/systemic errors on measurements

fig 3.

Graph of the average ADC_i for the different number of b values, obtained from the right centrum semiovale, shows a significant difference between the b0b1 group and the remaining four groups. fig 4. Graph of the average ADC_i for the different strengths of b values, obtained from the right centrum semiovale, shows a significant difference between all the groups except b0b4 and b0b5.

fig 5.

Graph of the average SD of ADC_i for the different strengths of b values, obtained from the splenium of the corpus callosum, shows a significant difference between the b0b1 group and the remaining four groups. fig 6. Graph of the average SD of FA for the different strengths of b values, obtained from the splenium of the corpus callosum, shows a significant difference between the b0b1 group and the remaining four groups.