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Updated: Sep 28, 2025
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
The Application of Diffusion Kurtosis Imaging on the Heterogeneous White Matter in Relapsing-Remitting Multiple
Qiyuan Zhu1, Qiao Zheng1, Dan Luo1
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Objectives:
To evaluate the microstructural damage in the heterogeneity of different white matter areas in relapsing-remitting multiple sclerosis (RRMS) patients by using diffusion kurtosis imaging (DKI) and its correlation with clinical and cognitive status.
Materials And Methods:
Kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD) in T1-hypointense lesions (T1Ls), pure T2-hyperintense lesions (pure-T2Ls), normal-appearing white matter (NAWM), and white matter in healthy controls (WM in HCs) were measured in 48 RRMS patients and 26 sex- and age-matched HCs. All the participants were assessed with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Symbol Digit Modalities Test (SDMT) scores as the cognitive status. The Kurtzke Expanded Disability Status Scale (EDSS) scores were used to evaluate the clinical status in RRMS patients.
Results:
The lowest KFA, FA, and MK values and the highest MD values were found in T1Ls, followed by pure-T2Ls, NAWM, and WM in HCs. The T1Ls and pure-T2Ls were significantly different in FA (p = 0.002) and MK (p = 0.013), while the NAWM and WM in HCs were significantly different in KFA, FA, and MK (p < 0.001; p < 0.001; p = 0.001). The KFA, FA, MK, and MD values in NAWM (r = 0.360, p = 0.014; r = 0.415, p = 0.004; r = 0.369, p = 0.012; r = -0.531, p < 0.001) were correlated with the MMSE scores and the FA, MK, and MD values in NAWM (r = 0.423, p = 0.003; r = 0.427, p = 0.003; r = -0.359, p = 0.014) were correlated with the SDMT scores.
Conclusion:
Applying DKI to the imaging-based white matter classification has the potential to reflect the white matter damage and is correlated with cognitive impairment.
Insights
Diffusion kurtosis imaging (DKI) reveals significant white matter damage in relapsing-remitting multiple sclerosis (RRMS) patients, correlating with cognitive impairment. This technique aids in understanding disease progression and impact.
Area of Science:
- Neuroimaging
- Radiology
- Neurology
Background:
- Relapsing-remitting multiple sclerosis (RRMS) involves white matter damage, impacting neurological function.
- Assessing microstructural changes in different white matter areas is crucial for understanding RRMS heterogeneity.
- Diffusion kurtosis imaging (DKI) offers advanced insights into white matter microstructural integrity.
Purpose of the Study:
- To evaluate microstructural damage in various white matter areas of RRMS patients using DKI.
- To correlate DKI-derived metrics with clinical and cognitive status in RRMS.
- To differentiate white matter damage in lesions and normal-appearing white matter (NAWM) compared to healthy controls (HCs).
Main Methods:
- DKI metrics including kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD) were measured.
- Analysis included T1-hypointense lesions (T1Ls), pure T2-hyperintense lesions (pure-T2Ls), NAWM, and white matter in HCs.
- Cognitive status was assessed using the MMSE, MoCA, and SDMT; clinical status by the EDSS.
Main Results:
- T1Ls and pure-T2Ls showed significantly lower KFA, FA, MK, and higher MD compared to NAWM and HCs.
- NAWM in RRMS patients exhibited significant differences in KFA, FA, and MK compared to HCs.
- DKI metrics in NAWM correlated significantly with cognitive scores (MMSE and SDMT).
Conclusions:
- DKI effectively characterizes white matter microstructural damage in RRMS.
- DKI-derived measures in NAWM are associated with cognitive impairment in RRMS patients.
- DKI holds potential for imaging-based classification of white matter damage and monitoring disease progression in RRMS.
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