Abstract
Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.
Highlights
In the field of computational neuroanatomy, a substantial amount of work has been devoted to automated tissue classification and segmentation in the analysis of cerebral cortex with magnetic resonance imaging (Ashburner et al, 2003; May and Gaser, 2006)
Three co-localised 3D multi-echo fast low angle shot (FLASH) datasets were acquired with predominantly proton density weighting (PDw: TR/α = 23.7 ms/6°), T1w (18.7 ms/20°), and MTw (23.7 ms/6°; excitation preceded by an off-resonance Gaussian magnetization transfer (MT) pulse of 4 ms duration, 220° nominal flip angle, 2 kHz frequency offset) in a total acquisition time of approx. 19 min
The MT maps showed a better delineation of white matter (WM) laminae embedded in grey matter (GM) structures, as seen in the thalamus (Fig. 1b)
Summary
In the field of computational neuroanatomy, a substantial amount of work has been devoted to automated tissue classification and segmentation in the analysis of cerebral cortex with magnetic resonance imaging (Ashburner et al, 2003; May and Gaser, 2006). A high iron content of the major midbrain nuclei (SNc, red and subthalamic nuclei, pallidum and putamen, for a review see Haacke et al, 2005) further shortens T1. These structures exhibit reduced contrast from white matter (WM) in T1w images and are often misclassified by segmentation algorithms. There is an emerging need for high-resolution MR images that provide sufficient contrast for reliable automated segmentation of subcortical structures. To this end, we used parameter maps based on magnetization transfer (MT) contrast. Considerable improvements were seen in putamen, pallidum, SNc, and pulvinar
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