کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8686761 | 1580832 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Signal compartments in ultra-high field multi-echo gradient echo MRI reflect underlying tissue microstructure in the brain
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
Gradient recalled echo magnetic resonance imaging (GRE-MRI) at ultra-high field holds great promise for new contrast mechanisms and delineation of putative tissue compartments that contribute to the multi-echo GRE-MRI signal may aid structural characterization. Several studies have adopted the three water-pool compartment model to study white matter brain regions, associating individual compartments with myelin, axonal and extracellular water. However, the number and identifiability of GRE-MRI signal compartments has not been fully explored. We undertook this task for human brain imaging data. Multiple echo time GRE-MRI data were acquired in five healthy participants, specific anatomical structures were segmented in each dataset (substantia nigra, caudate, insula, putamen, thalamus, fornix, internal capsule, corpus callosum and cerebrospinal fluid), and the signal fitted with models comprising one to six signal compartments using a complex-valued plane wave formulation. Information criteria and cluster analysis methods were used to ascertain the number of distinct compartments within the signal from each structure and to determine their respective frequency shifts. We identified five principal signal compartments with different relative contributions to each structure's signal. Voxel-based maps of the volume fraction of each of these compartments were generated and demonstrated spatial correlation with brain anatomy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 178, September 2018, Pages 403-413
Journal: NeuroImage - Volume 178, September 2018, Pages 403-413
نویسندگان
Shrinath Kadamangudi, David Reutens, Surabhi Sood, Viktor Vegh,