DIFFUSION-KURTOSIS IMAGING IN ASSESMENT OF BRAIN MICROSTRUCTURE. HEALTHY VOLUNTEERS MEASURMENTS
https://doi.org/10.24835/1607-0763-2018-4-108-126
Abstract
Aim: discover quantitative and qualitative variance of diffusion parameters in white and gray matter of healthyvolunteers brain. Discover correlation between diffusion and kurtosis parameters, find out if there is correlation between the parameters and aging microstructural changes.
Materials and methods. 14 healthy volunteers were investigated (9 men, 5 women; age from 21 to 55 years, mean 34). The volunteers were classified into two groups by age: 7 subjects who younger 35 (6 men and 1 woman, mean age 25) and 7 subjects who older 35 years (3 men and 4 women, mean age 44). We used 3.0 Tesla MRI (3.0T SignaHDxt, General Electric, USA) with 8 channel head coil, gradient strength 50 mT/m, slew rate 150 T/m/s. Diffusion imaging was based on echo planar “spin echo” sequence (SE EPI), TR = 10000 ms, TEmin = 102 ms, FOV = 240 mm, voxel size 3 × 3 × 3 mm3, 60 non-coplanar diffusion directions and three b-values: 0, 1000, 2500 s/mm2. Acquisition time of diffusion kurtosis imaging was 22 minutes. We excluded extracerebral areas on diffusion and kurtosis parametric maps using semi-automatic approach. After that, brain images were transformed to MNI152 space using affine method. Masks of 9 anatomical structures were applied to the transformed images. Diffusion and kurtosis values were measured in these structures.
Results. Fractional anisotropy (FA) changed from 0.06 in lateral occipital cortex to 0.25 in cerebral white matter, kurtosis anisotropy (KA) changed from 0.03 to 0.14 in the same cerebral structures. Axial (AK), radial (RK) and mean kurtosis (MK) were minimal in superior frontal gyrus and maximal in cerebral white matter. AK changed from 0.55 to 0.72, RK changed from 0.62 to 1.05, MK from 0.59 to 0.88. Axial(AxEAD) and radial extra axonal water diffusion (RadEAD) were minimal in putamen and maximal in superior frontal gyrus. AxEAD was changing from 1.38 • 10–3 to 2.57 • 10–3, RadEAD from 1.03 • 10–3 to 2.34 • 10–3. Axonal water fraction (AWF) had minimal value 0,18 in superior frontal gyrus and maximal value 0.29 in cerebral white matter. Tortuosity (TORT) changed from 1.06 in lateral occipital cortex to 1.43 in cerebral white matter. There was significant difference between age groups in AWF, RK, RadEAD in putamen and in KA in superior temporal gyrus. Maximal correlation with age was in MK in superior temporal gyrus, anterior division. It was equal to 0.562.
Conclusions: Diffusion kurtosis imaging is highly sensitive method of brain tissue microstructure assessment, which detects age-related changes.
About the Authors
E. L. PogosbekyanRussian Federation
Eduard L. Pogosbekyan – med. physicist of Neuroradiology department.
Moscow.
A. M. Turkin
Russian Federation
Alexander M. Turkin – cand. of med. sci., senior researcher of Neuroradiology department.
Moscow.
A. A. Baev
Russian Federation
Alexander A. Baev – med. doctor of Neuroradiology department.
Moscow.
E. I. Shults
Russian Federation
Evgeniy I. Shults – med. doctor of Neuroradiology department.
Moscow.
N. V. Khachanova
Russian Federation
Natalya V. Khachanova – cand. of med. sci., Assistant of Neurology and Neurosurgery Department.
Moscow.
I. I. Maximov
Norway
Ivan I. Maximov – cand. of phys.-math. sci., senior researcher.
Oslo.
L. M. Fadeeva
Russian Federation
Lyudmila M. Fadeeva – lead. engineer of Neuroradiology department.
Moscow.
I. N. Pronin
Russian Federation
Igor N. Pronin – Full Мember of the Russian Academy of Sciences, doct. of med. sci., Professor, Head of Neuroradiology department, Deputy Director.
Moscow.
V. N. Kornienko
Russian Federation
Valeriy N. Kornienko – Full Мember of the Russian Academy of Sciences, doct. of med. sci., Professor, Сonsultant, Neuroradiology department.
Moscow.
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Review
For citations:
Pogosbekyan E.L., Turkin A.M., Baev A.A., Shults E.I., Khachanova N.V., Maximov I.I., Fadeeva L.M., Pronin I.N., Kornienko V.N. DIFFUSION-KURTOSIS IMAGING IN ASSESMENT OF BRAIN MICROSTRUCTURE. HEALTHY VOLUNTEERS MEASURMENTS. Medical Visualization. 2018;(4):108-126. (In Russ.) https://doi.org/10.24835/1607-0763-2018-4-108-126