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

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 and , proton density and magnetisation transfer 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.

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics: a framework for tracking neurodegenerative disease

“…Image processing at UHF is also challenging. Current pipelines are typically optimised for T1-weighted images at 3T (Fischl et al, 2002) and to a much lesser extent to 7T MRI or quantitative parameter maps (Choi et al, 2019; Tabelow et al, 2019). Therefore, we had to add processing steps, such as denoising, in order to use existing pipelines.…”

“…For the purposes of prospective motion correction of the high resolution MPM acquisitions, each volunteer was scanned while wearing a mouth guard assembly (with attached passive Moiré pattern markers) moulded to their front teeth (manufactured by the Department of Cardiology, Endodontology and Periodontology, University Medical Center Leipzig; (Papoutsi et al, 2018)). R1, R2* and PD parameter maps were computed using the hMRI toolbox (Tabelow et al, 2019).…”

“…SPM segment (https://www.fil.ion.ucl.ac.uk/spm) was applied to the synthetic image to create a combined grey matter (GM)/white matter (WM)/cerebrospinal fluid (CSF) brain mask with a tissue probability cut-off of 0, which was used to remove the skull from the PD image. The PD image (normalised such that the average white matter intensity is at 69% (Tabelow et al, 2019)) was then subtracted from 100%, inverting the contrast and thus making it more MPRAGE-like and at the same time providing macromolecular tissue volume fraction (MTV) measures (Mezer et al, 2013). Next, Rician denoising (http://www.cs.tut.fi/~foi/GCF-BM3D) (Maggioni et al, 2013) was applied and the resulting image was used for Freesurfer cortical reconstruction.…”

Increased Sensitivity and Signal-to-Noise Ratio in Diffusion-Weighted MRI using Multi-Echo Acquisitions

“…For example, noise informed MLE reconstruction could be advantageous for other quantitative MRI strategies using Multi-Echo acquisitions such as ME-FLASH (43) or ME-MP2RAGE (44). Multi-Echo reconstructions are commonly performed using LLS (45,46). Especially at high resolutions or for post-mortem acquisitions, signal bias effects may be circumvented using advanced regression strategies such as MLE.…”

Reducing bias in dual flip angle T1‐mapping in human brain at 7T

“…A two‐point measurement, that is, a dual flip angle (DFA) approach, is sufficient to determine T 1 as well as the signal amplitude reflecting proton density (PD). The DFA approach is the fastest method to acquire high resolution whole‐brain maps of T 1 and has been applied extensively on clinical MR systems and dedicated processing tools, like the hMRI toolbox have been made available. Besides T 1 , this toolbox can also provide maps of , PD, and magnetization transfer (MT) saturation.…”