, Volume 58, Issue 12, pp 1217–1231 | Cite as

Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas

  • Rajikha Raja
  • Neelam Sinha
  • Jitender Saini
  • Anita Mahadevan
  • KVL Narasinga Rao
  • Aarthi Swaminathan
Functional Neuroradiology



In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.


Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (n = 19), grade III (n = 20) and grade IV (n = 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.


Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of P = 0.029 (0.0421) for grade II vs. III and P = 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability, P = 0.018 (0.038) for grade II vs. III and P = 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed P values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).


In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.


Diffusion tensor imaging Diffusion kurtosis imaging Glioma grading Textural features 


Compliance with ethical standards

We declare that due to the retrospective nature of this study, informed consent was waived.

Conflict of interest

We declare that we have no conflict of interest.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Rajikha Raja
    • 1
  • Neelam Sinha
    • 1
  • Jitender Saini
    • 2
  • Anita Mahadevan
    • 2
  • KVL Narasinga Rao
    • 2
  • Aarthi Swaminathan
    • 2
  1. 1.International Institute of Information Technology-BangaloreBangaloreIndia
  2. 2.National Institute of Mental Health and NeurosciencesBangaloreIndia

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