Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer’s disease participants

By K. Oishi, A. Faria, Hangyi Jiang, Xin Li, K. Akhter, Jiangyang Zhang, Johnny T. C. Hsu, Michael I. Miller, Peter C. M. van Zijl, Marilyn S. Albert and 8 more. NeuroImage, 2009

583 citations recorded by the source corpus.

The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer’s disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting “almost perfect” agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9161e6c19a95d43d8fc4acaa2cba309d6809f416

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In Cognitive Neuroscience

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Citations in the corpus

Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis (2015, methodology): “Although our tool has been tested for pediatric (Faria, 2010) and geriatric populations(Oishi,2009;Djamanakova,2013),protocolimpactshavenot been rigorously tested.”
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces (2019, methodology): “Diffeomorphic transforms have seen extensive methodological development, yielding state-ofthe-art tools, such as Large Diffeomorphic Distance Metric Mapping (LDDMM) [10, 12, 13, 28, 33, 42, 47, 59], DARTEL [3], diffeomorphic Demons [56], and symmetric normalization (SyN) [5].”
Multi-Contrast Human Neonatal Brain Atlas: Application to Normal Neonate Development Analysis (2011, unknown): “The deep white matter structures identified in our previous studies of adult brains (Mori et al., 2008; Mori et al., 2005; Oishi et al., 2009)were readily identified in the neonate brains using DTI contrasts.”
VoxelMorph: A Learning Framework for Deformable Medical Image Registration (2018, methodology): “Popular formulations include Large Diffeomorphic Distance Metric Mapping (LDDMM) [9], [21], [27]–[32], DARTEL [16], diffeomorphic demons [33], and standard symmetric normalization (SyN) [11].”