Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data
Year: 2021
Type: article
Abstract: The development of scanners with ultra-high gradient strength, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.
Source: NeuroImage
Institutions: Harvard University, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Boston University, Philipps University of Marburg, McLean Hospital, Columbia University, Massachusetts Institute of Technology, Northeastern University, University of Rochester Medicine, University of Rochester
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,300
Funders: Massachusetts General Hospital, Centre d'Imagerie BioMédicale, National Institutes of Health, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, NIH Blueprint for Neuroscience Research
Awards: G1360098495, G2227408383, G2483948867, G3549079774, Connectome 2.0: Developing the next generation human MRI scanner for bridging studies of the micro-, meso- and macro-connectome, G393103480, Imaging the neurodevelopmental trajectory of depression and anxiety disorders with HCP protocols, G4143200577, The Human Connectome Project (HCP), Advanced Multimodal Neuroimaging Training Grant, G4892808719, G570765226, Advanced Multimodal Neuroimaging Training Grant, Computeserver Structural &Functional Image Analysis, G6348463337, G7772742281, Center for Functional Imaging Technologies, Multimodal mapping of the neurocircuitry of the human prefrontal cortex, Connectomes Related to Anxiety and Depression in Adolescents, G868551019, A Storage Area Network for Structural and Functional Image Analysis, G9000086603, G9000200654, Silicon Graphics Prism Extreme 128P/1TB
OpenAlex ID: W3214388536