hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

• Authors: Karsten Tabelow, WIAS Berlin; Evelyne Balteau, University of Liege; John Ashburner, Wellcome Centre for Human Neuroimaging; Martina F. Callaghan, Wellcome Centre for Human Neuroimaging; Bogdan Draganski, Lausanne University Hospital; Gunther Helms, Lund University; Ferath Kherif, Lausanne University Hospital; Tobias Leutritz, Max Planck Institute for Human Cognitive and Brain Sciences; Antoine Lutti, Lausanne University Hospital; Christophe Phillips, University of Liege; Enrico Reimer, Max Planck Institute for Human Cognitive and Brain Sciences; Lars Ruthotto, Emory University; Maryam Seif, University of Zurich; Nikolaus Weiskopf, Max Planck Institute for Human Cognitive and Brain Sciences; Gabriel Ziegler, University of Magdeburg; Siawoosh Mohammadi, Medical Center Hamburg-Eppendorf
• Journal Title: NeuroImage
• Volume: Volume 194
• Publisher: Elsevier | 2019-07-01, Pages 191-210
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1016/j.neuroimage.2019.01.029
• Subjects: Biology, Neuroscience; Health Sciences, Radiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging; Neurosciences & Neurology; Quantitative MRI; In vivo histology; Microstructure; Multi-parameter mapping; Relaxometry; SPM toolbox; HUMAN BRAIN; IN-VIVO; MAGNETIZATION-TRANSFER; FLIP-ANGLE; PROBABILISTIC ATLAS; TRANSMIT FIELD; CEREBRAL-CORTEX; WATER-CONTENT; STEADY-STATE; G-RATIO

Abstract

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R 1 and R 2⋆ , proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.