Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data

C. Maffei; C. Lee; M. Planich; M. Ramprasad; N. Ravi; D. Trainor; Z. Urban; M. Kim; R.J. Jones; A. Henin; S.G. Hofmann; D.A. Pizzagalli; R.P. Auerbach; J.D.E. Gabrieli; S. Whitfield-Gabrieli; D.N. Greve; S.N. Haber; A. Yendiki

NeuroImage (Dec 2021)

Using diffusion MRI data acquired with ultra-high gradient strength to improve tractography in routine-quality data

C. Maffei,
C. Lee,
M. Planich,
M. Ramprasad,
N. Ravi,
D. Trainor,
Z. Urban,
M. Kim,
R.J. Jones,
A. Henin,
S.G. Hofmann,
D.A. Pizzagalli,
R.P. Auerbach,
J.D.E. Gabrieli,
S. Whitfield-Gabrieli,
D.N. Greve,
S.N. Haber,
A. Yendiki

Affiliations

C. Maffei: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Corresponding author.
C. Lee: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
M. Planich: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
M. Ramprasad: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
N. Ravi: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
D. Trainor: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
Z. Urban: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
M. Kim: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
R.J. Jones: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
A. Henin: Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
S.G. Hofmann: Department of Clinical Psychology, Philipps University Marburg, Germany
D.A. Pizzagalli: McLean Hospital and Harvard Medical School, Belmont, MA, USA
R.P. Auerbach: Columbia University, New York, NY, USA
J.D.E. Gabrieli: Massachusetts Institute of Technology, Cambridge, MA, USA
S. Whitfield-Gabrieli: Northeastern University, Boston, MA, USA
D.N. Greve: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
S.N. Haber: McLean Hospital and Harvard Medical School, Belmont, MA, USA; Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, USA
A. Yendiki: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA

Journal volume & issue: Vol. 245
p. 118706

Abstract

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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.

Published in NeuroImage

ISSN: 1053-8119 (Print); 1095-9572 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: https://www.journals.elsevier.com/neuroimage

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