Diffusion Imaging Techniques for Glioma Assessment

Diffusion imaging exploits the Brownian motion of water molecules to probe the microstructural environment of brain tissue. In glioma assessment, diffusion-weighted imaging (DWI) and derived metrics such as the apparent diffusion coefficient (ADC) have become indispensable for non-invasive characterisation of tumour cellularity and tissue integrity. Diffusion tensor imaging (DTI) extends this by quantifying directional anisotropy, thereby revealing white matter tract infiltration. Diffusion kurtosis imaging (DKI) further captures non-Gaussian water displacement, offering enhanced sensitivity to microstructural heterogeneity in high-grade lesions. Advanced models, including neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP) MRI, provide additional insight into neurite density and dispersion, aiding differentiation of tumour subtypes. When combined in radiomic frameworks with clinical and morphological features, diffusion metrics can predict proliferative indices, guide treatment selection and stratify prognosis. Rapid acquisition protocols now permit routine clinical integration of complex diffusion sequences, while parametric maps assist in surgical planning, radiotherapy contouring and early response assessment to targeted therapies. Collectively, diffusion imaging techniques have transformed the precision and personalization of glioma management worldwide.

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Diffusion Imaging Techniques for Glioma Assessment publication trend

The graph below shows the total number of articles in diffusion imaging techniques for glioma assessment across all publications each year (not limited to Nature Index journals).

Technical terms

Apparent diffusion coefficient (ADC): Quantitative measure of average water diffusion within tissue, inversely related to cellularity.

Diffusion tensor imaging (DTI): MRI technique that models directional water diffusion to assess white matter tract integrity and anisotropy.

Diffusion kurtosis imaging (DKI): Extension of DTI accounting for non-Gaussian water displacement, sensitive to microstructural heterogeneity.

Fractional anisotropy (FA): Scalar metric from DTI reflecting the degree of directional diffusion, indicative of fibre organisation.

Radiomics: High-throughput extraction of quantitative imaging features integrated with clinical data to build predictive models.

References

1. An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma. Journal of Translational Medicine (2023).
2. Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade. European Radiology (2023).
3. Perifocal Zone of Brain Gliomas: Application of Diffusion Kurtosis and Perfusion MRI Values for Tumor Invasion Border Determination. Cancers (2023).