Diffusional Kurtosis Imaging and Motor Outcome in Acute Ischemic Stroke

Abstract

Background and Purpose To evaluate whether diffusional kurtosis imaging (DKI) can detect corticospinal tract (CST) microstructural changes in the acute phase for patients with first-ever ischemic stroke and motor impairment, and to assess the correlations between DKI-derived diffusion metrics for the CST and motor impairment 3 months post-stroke. Materials and Methods We evaluated seventeen stroke patients who underwent a brain MRI including DKI within four days after the onset of symptoms. Neurological evaluation included the Fugl-Meyer Motor Upper Extremity scale (FM-UE) in the acute phase and 3 months post-stroke. For the CST in the lesioned and contralateral hemispheres, we estimated with DKI both pure diffusion metrics, such as the mean diffusivity and mean kurtosis, as well as model-dependent quantities, such as the axonal water fraction. We evaluated the correlations between CST diffusion metrics and FM-UE at 3 months. Results Among all the diffusion metrics, the largest percent signal changes of the lesioned hemisphere CST were observed using axial kurtosis, with an average 12% increase compared to the contralateral CST. The strongest associations between 3-month FM-UE and diffusion metrics were found for the lesioned / contralateral hemisphere CST mean kurtosis (rho = − 0.85) and axial kurtosis (rho = − 0.78) ratios. Conclusion This study was designed to be one of hypothesis generation. Diffusion metrics related to the kurtosis were found to be more sensitive than conventional diffusivity metrics to early post-stroke CST microstructural changes and may have potential value in the prediction of motor impairment at 3 months.

Diffusion kurtosis imaging and pathological comparison of early hypoxic–ischemic brain damage in newborn piglets

“…Due to the strong homology between pig www.nature.com/scientificreports/ and human brain tissues 24 , newborn piglets were selected as the subjects of this experiment, and the HIBD model was successfully established in piglets in the previous experiment, and corresponding results were obtained 22 . Currently, kurtosis parameters are considered more sensitive than diffusion parameters for the evaluation of ischemic lesions by DKI 18,25 . MD is a comprehensive imaging measure of the dispersion of certain voxels or a region of a tissue.…”

“…The swelling and structural destruction of organelles make the water molecule diffusion more limited. The MK value is a more sensitive indicator of damage than the MD value, which suggests that the former can be used to represent the microenvironment complexity index and water molecule heterogeneity in the tissues 16,18,28,29 . In calculating the rate of change of MK and MD values, and to avoid bilateral infarction and the complication of side infarction and the influence of the contralateral perfusion pressure increase, we used measurements in a normal control group, which provided a more accurate scale for lesion evaluation.…”

“…Currently, kurtosis parameters are considered more sensitive than diffusion parameters for the evaluation of ischemic lesions by DKI 18 , 25 . MD is a comprehensive imaging measure of the dispersion of certain voxels or a region of a tissue.…”

In vivo Diffusion Tensor Imaging, Diffusion Kurtosis Imaging, and Tractography of a Sciatic Nerve Injury Model in Rat at 9.4T

“…inflammation, demyelination etc) 12 . Here, DKI has proved more promising as it is less sensitive to interference by local edema 12 and has also shown increased sensitivity in detecting certain pathological conditions in the central nervous system 38 as compared to DTI. Our results suggest that information concerning FA of the structures of interest can be detected at the entire range of b-values (600–1200 s/mm2), but the minute changes in the proximal stump were only statistically significant at 1000 s/mm2, whereas proximal stump changes of MD, AD and RD where most readily detected at 600 s/mm2.…”

Brain networks and their relevance for stroke rehabilitation

“…This motivated novel developments based on i) higher angular estimation of fibre orientation by constrained spherical deconvolution (Tournier et al, 2004) optimized tractography (see below) and ii) a more complex modelling of microstructure. Latter includes the usage of multi-tensor models (Malcolm et al, 2010, Chu et al, 2015 or diffusion kurtosis imaging (Jensen et al, 2005a), a model free fitting measuring deviation from a Gaussian distributed signal, which has been used for clinical prediction studies evaluating e.g., the corticospinal tract in stroke (Hui et al, 2012, Spampinato et al, 2017. A very promising approach, especially for the usage in clinical research, is the compartment model framework.…”