Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

<div><p>In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.</p></div

A comparison of the CST correlation obtained from using a single-contrast image and multi-contrast images.

<p>A correlation plot between the automated and manual measurements of the sizes of left and right corticospinal tracts (CST). Results from the four automated parcellation methods are compared: 5-contrast (red), FA-only (green), MD-only (blue), and EV-only (yellow).</p

Anatomical changes in 25 subjects for testing the multiple atlases application.

<p>Anatomical changes in 25 subjects for testing the multiple atlases application.</p

Demonstration of the unique anatomical features revealed by multi-contrast images generated in DTI and GMM.

<p>Histograms of the five contrasts, FA, MD, EV-x, EV-y, and EV-z, of five adjacent structures are shown, including two white matter structures (the ALIC and the PLIC), two gray matter structures (the caudate and the thalamus), and the ventricle. In each subplot, blue indicates the histogram of the corresponding contrast within that specific structure, green represents the probability density of each single Gaussian, and red shows the weighted sum of all Gaussians. Abbreviations are: ALIC: Anterior limb of internal capsule and PLIC: Posterior limb of internal capsule.</p

Pearson correlations between manual and four different automated ROI area measures, with bold typesetting indicating that the correlation between the automated and the manual measures is statistically significantly stronger than other methods (<i>p<0.025</i>).

<p>Pearson correlations between manual and four different automated ROI area measures, with bold typesetting indicating that the correlation between the automated and the manual measures is statistically significantly stronger than other methods (<i>p<0.025</i>).</p

Examples of CST parcellations from single- and multi-contrast approaches.

<p>Demonstration of the parcellation accuracy of the CST in three representative cases with different degrees of anatomical abnormalities. Results from five different approaches are compared.</p

A comparison of parcellating using a single-contrast image and multi-contrast images, in terms of overlap accuracy.

<p>The mean Dice overlaps and the standard deviations of the eighteen ROIs obtained from automated parcellations based on five contrasts (red), the single FA contrast (green), the single MD contrast (blue), the vector x y z contrasts (yellow), as well as the inter-rate (patterned). The mean values are calculated across fourteen different subjects. Star marks indicate significant difference among the four sets of Dice results by ANOVA (<i>p</i><<0.05). Abbreviations are: GCC – genu of corpus callosum; BCC – body of corpus callosum; Caud – caudate; Put – putamen; ALIC – anterior limb of internal capsule; PLIC – posterior limb of internal capsule; CG – cingulate gyrus; MCP – middle cerebellar peduncle; SLF – superior longitudinal fasciculus; CST – corticospinal tract.</p

Anatomical changes in the sixteen subjects used as the multiple atlases.

<p>Anatomical changes in the sixteen subjects used as the multiple atlases.</p

Examples of whole brain parcellations.

<p>Results of the whole brain parcellations into 159 structures in three representative cases with large anatomical variability. The parcellation results are superimposed on color (upper row) and MD (bottom row) images.</p

Pearson correlations between the mean FA value within the manual ROI and the automated parcellations, with bold typesetting suggesting that the correlation between the automated and the manual measures is statistically significantly stronger than other methods (<i>p<0.025</i>).

<p>Pearson correlations between the mean FA value within the manual ROI and the automated parcellations, with bold typesetting suggesting that the correlation between the automated and the manual measures is statistically significantly stronger than other methods (<i>p<0.025</i>).</p