Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. This is the second review on the topic of g-ratio mapping using MRI. As such, it summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. Using simulations based on recently published data, this review demonstrates the relevance of the calibration step for three myelin-markers (macromolecular tissue volume, myelin water fraction, and bound pool fraction). It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest Editor

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

BACKGROUND: The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. NEW METHOD: This is the second review on the topic of g-ratio mapping using MRI. RESULTS: This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. COMPARISON WITH EXISTING METHODS: Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. CONCLUSIONS: We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated

On the track of the brain's microstructure : myelin water imaging using quantitative MRI

Conventional quantitative magnetic resonance imaging (MRI), for example monoexponential determination of the relaxation times T1 and T2, is sensitive to the various pathologies of myelinated tissue in the brain. However, it gives relatively unspecific information about the underlying nature of the disease. A parameter that directly correlates with the integrity of the myelin sheath is the so-called myelin water fraction (MWF). Based on multi-component analysis of non-invasive quantitative MRI measurements, mapping of the MWF becomes feasible and proved to be useful for studying demyelination and remyelination processes in the course of multiple sclerosis (MS) and other myelin related pathologies. Common myelin water imaging techniques often suffer from a lack of volume coverage due to their 2D acquisition schemes. This thesis focuses on the development of new myelin water mapping procedures, especially on fast 3D MRI measurements that provide whole brain coverage. In chapter 2, an MWF mapping technique based on balanced steady-state free precession (bSSFP) sequences is introduced. An extended bSSFP signal equation, which is based on a two-pool water model describing brain tissue, is derived to determine typical multi-compartment parameters, including the MWF, of healthy subjects. Possible influences of magnetization transfer effects, infinite radiofrequency pulses and B0/B1 inhomogeneities are discussed extensively. Chapter 3 introduces a 3D acquisition scheme based on multi-gradient-echo (mGRE) pulse sequences that is applied for sampling multi-component T2* decays in the human brain of healthy volunteers and MS patients. Quantitative myelin water maps are generated based on analysis of T2* spectra. Chapter 4 discusses possible adaptations and modifications of the proposed procedure from chapter 3 when moving to higher main magnetic field strengths. The effects of B0 inhomogeneities on the data sets and possible correction methods are additionally covered in this part of the thesis. Finally, the crucial role of accurate B1 and B0 imaging and the influences on myelin water imaging are revisited in chapter 5. A solution to simultaneous mapping of B1 and B0 is presented that might help to overcome systematic error sources in MWF mapping in the future

Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling

Purpose: The effective transverse relaxation rate ( R∗2 ) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fiber orientation with respect to the main field ( θ ) complicate interpretation. The α- and θ -dependence stem from the existence of multiple sub-voxel micro-environments (e.g., myelin and non-myelin water compartments). Ordinarily, it is challenging to quantify these sub-compartments; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a single R∗2 estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartment R∗2 estimates. Methods: We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartment R∗2 estimates. Based on our numeric observations, we introduced a linear model that partitions R∗2 into α-dependent and α-independent components and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence, and reproducibility empirically. Results: R∗2 increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although it suffered from marginally higher noise sensitivity. Conclusion: We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartment R∗2 estimates

On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol

Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases