Diffusion Tensor Imaging (DTI) uses single shell gradient encoding scheme for studying brain tissue diffusion. NODDI (Neurite Orientation Dispersion and Density Imaging) incorporates a gradient scheme with multiple b-values which is used to characterize neurite density and coherence of neuron fiber orientations. Similarly, the diffusion kurtosis imaging also uses a multiple shell scheme to quantify non-Gaussian diffusion but does not assume a tissue model like NODDI. In this study we investigate the connection between metrics derived by NODDI and DKI in children with ages from 46 weeks to 6 years. We correlate the NODDI metrics and Kurtosis measures from the same ROIs in multiple brain regions. We compare the range of these metrics between neonates (46 - 47 weeks), infants (2 -10 months) and young children (2 – 6 years). We find that there exists strong correlation between neurite density vs. mean kurtosis, orientation dispersion vs. kurtosis fractional anisotropy (FA) in pediatric brain imaging.
In our previous work, we showed that fractal-based texture features are effective in detection, segmentation
and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an
information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and
ranking different texture features. We further incorporated the feature selection technique with
segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor
(NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting
the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation
robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of
pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation
robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.
Our previous works suggest that fractal-based texture features are very useful for detection, segmentation and
classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. In this work, we investigate and
compare efficacy of our texture features such as fractal and multifractional Brownian motion (mBm), and intensity
along with another useful level-set based shape feature in PF tumor segmentation. We study feature selection and
ranking using Kullback -Leibler Divergence (KLD) and subsequent tumor segmentation; all in an integrated
Expectation Maximization (EM) framework. We study the efficacy of all four features in both multimodality as well
as disparate MRI modalities such as T1, T2 and FLAIR. Both KLD feature plots and information theoretic entropy
measure suggest that mBm feature offers the maximum separation between tumor and non-tumor tissues in T1 and
FLAIR MRI modalities. The same metrics show that intensity feature offers the maximum separation between tumor
and non-tumor tissue in T2 MRI modality. The efficacies of these features are further validated in segmenting PF
tumor using both single modality and multimodality MRI for six pediatric patients with over 520 real MR images.