New paper for thalamic clustering by E. Najdenovska et al
Block diagram of thalamic nuclei clustering E. Najdenovska © 2016 UNIL
The paper “Robust Thalamic Nuclei Segmentation Method Based on Local Diffusion Magnetic Resonance Properties” has been accepted to be published in the journal Brain Structure and Function.
Robust Thalamic Nuclei Segmentation Method Based on Local Diffusion Magnetic Resonance Properties
Elena Najdenovska, Giovanni Battistella, Philippe Maeder, Naghmeh Ghazaleh, Alessandro Daducci, Jean-Philippe Thiran, Sébastien Jacquemont, Constantin Tuleasca, Marc Levivier, Eleonora Fornari and Meritxell Bach Cuadra
Abstract
The thalamus is an essential relay in the cortical-subcortical connections. It is characterized by a complex anatomical architecture composed of numerous small nuclei, which mediate the involvement of the thalamus in a wide range of neurological functions.
We present a novel framework for segmenting the thalamic nuclei, which explores the orientation distribution functions (ODFs) from diffusion magnetic resonance images at 3 Tesla. The differentiation of the complex intra-thalamic microstructure is improved with the spherical harmonic (SH) representation of the ODFs providing full angular characterization of the diffusion process in each voxel.
The clustering was performed using k-means algorithm initialized in a data-driven manner. The method was tested on 35 healthy volunteers and our results showed a robust, reproducible and accurate segmentation of the thalamus in seven nuclei groups. Six of them closely match the anatomy and were labeled as Anterior, Ventral Anterior, Medio-Dorsal, Ventral Latero-Ventral, Ventral Latero-Dorsal and Pulvinar, while the seventh cluster included the Centro-Lateral and the Latero-Posterior nuclei.
Results were evaluated both qualitatively, by comparing the segmented nuclei to the histological atlas of Morel, and quantitatively, by measuring the clusters extent and the clusters spatial distribution across subjects and hemispheres. Furthermore, we show the robustness of our approach across different sequences and scanners, as well as intra-subject reproducibility of the segmented clusters, on two scan-rescan additional datasets. We also observed an overlap between the path of the main long-connections tracts passing through the thalamus and the spatial distribution of the nuclei identified with our clustering algorithm.
Our approach, based on SH representations of the ODFs outperforms the one based on angular differences between the principle diffusion directions, which is considered so far as state-of-the-art method. Our findings show an anatomically reliable segmentation of the main groups of thalamic nuclei that could be of potential use in many clinical applications.