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Updated: Aug 2, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and
Jan Brabec1, Filip Szczepankiewicz2, Finn Lennartsson2
1Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden.
Background:
Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach.
Purpose:
To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type.
Materials And Methods:
30 patients with intracranial meningiomas (22 WHO grade I, 8 WHO grade II) underwent MRI prior to surgery. Diffusion MRI was performed with linear and spherical b-tensors with b-values up to 2000 s/mm2. The data were used to estimate mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and its components-the anisotropic and isotropic kurtoses (MKA and MKI). Meningioma consistency was estimated for 16 patients during resection based on ultrasonic aspiration intensity, ease of resection with instrumentation or suction. Grade and type were determined by histopathological analysis. The relation between consistency, grade and type and dMRI parameters was analyzed inside the tumor ("whole-tumor") and within brain tissue in the immediate periphery outside the tumor ("rim") by histogram analysis.
Results:
Lower 10th percentiles of MK and MKA in the whole-tumor were associated with firm consistency compared with pooled soft and variable consistency (n = 7 vs 9; U test, p = 0.02 for MKA 10 and p = 0.04 for MK10) and lower 10th percentile of MD with variable against soft and firm (n = 5 vs 11; U test, p = 0.02). Higher standard deviation of MKI in the rim was associated with lower grade (n = 22 vs 8; U test, p = 0.04) and in the MKI maps we observed elevated rim-like structure that could be associated with grade. Higher median MKA and lower median MKI distinguished psammomatous type from other pooled meningioma types (n = 5 vs 25; U test; p = 0.03 for MKA 50 and p = 0.03 and p = 0.04 for MKI 50).
Conclusion:
Parameters from tensor-valued dMRI can facilitate prediction of consistency, grade and type.
Insights
Tensor-valued diffusion MRI (dMRI) aids in predicting meningioma consistency, grade, and type. Specific dMRI parameters correlate with tumor firmness and histological classification, improving preoperative assessment.
Area of Science:
- Neuroimaging
- Radiology
- Oncology
Background:
- Preoperative radiological assessment of meningioma characteristics is crucial for patient management, counseling, and surgical planning.
- Accurate characterization aids in determining the optimal surgical approach and predicting patient outcomes.
Purpose of the Study:
- To evaluate the utility of tensor-valued diffusion MRI (dMRI) in predicting meningioma consistency, grade, and type before surgery.
- To determine if advanced dMRI metrics can enhance preoperative diagnostic capabilities for meningiomas.
Main Methods:
- 30 patients with intracranial meningiomas underwent preoperative MRI with tensor-valued diffusion sequences.
- Diffusion MRI data were analyzed to derive parameters like mean diffusivity (MD), fractional anisotropy (FA), and mean kurtosis (MK) and its components (MKA, MKI).
- Meningioma consistency was assessed intraoperatively, while grade and type were determined histopathologically; dMRI parameters were correlated with these findings.
Main Results:
- Lower 10th percentiles of MK and MKA correlated with firm meningioma consistency.
- Lower 10th percentile of MD distinguished variable consistency from soft and firm types.
- Higher standard deviation of MKI in the tumor rim was associated with lower tumor grade.
- Higher median MKA and lower median MKI differentiated psammomatous meningioma type.
Conclusions:
- Tensor-valued dMRI parameters show potential for predicting meningioma consistency, grade, and type.
- These advanced dMRI metrics can improve preoperative characterization, aiding surgical planning and patient management.
- dMRI offers a non-invasive tool to gain insights into meningioma tissue properties and subtypes.
