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

Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping

Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters -- such as the apparent transverse relaxation rate R2*, the longitudinal relaxation rate R1 and the magnetisation transfer saturation -- that can be compared across sites and scanners and carry important information about the underlying microstructure. The multi-parameter mapping (MPM) protocol takes advantage of multi-echo acquisitions with variable flip angles to extract these parameters in a clinically acceptable scan time. In this context, ESTATICS performs a joint loglinear fit of multiple echo series to extract R2* and multiple extrapolated intercepts, thereby improving robustness to motion and decreasing the variance of the estimators. In this paper, we extend this model in two ways: (1) by introducing a joint total variation (JTV) prior on the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a posteriori} estimate. We evaluated the proposed algorithm by predicting left-out echoes in a rich single-subject dataset. In this validation, we outperformed other state-of-the-art methods and additionally showed that the proposed approach greatly reduces the variance of the estimated maps, without introducing bias.Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI 202

In vivo multi-parameter mapping of the habenula using MRI

The habenula is a small, epithalamic brain structure situated between the mediodorsal thalamus and the third ventricle. It plays an important role in the reward circuitry of the brain and is implicated in psychiatric conditions, such as depression. The importance of the habenula for human cognition and mental health make it a key structure of interest for neuroimaging studies. However, few studies have characterised the physical properties of the human habenula using magnetic resonance imaging because its challenging visualisation in vivo, primarily due to its subcortical location and small size. To date, microstructural characterization of the habenula has focused on quantitative susceptibility mapping. In this work, we complement this previous characterisation with measures of longitudinal and effective transverse relaxation rates, proton density and magnetisation transfer saturation using a high-resolution quantitative multi-parametric mapping protocol at 3T, in a cohort of 26 healthy participants. The habenula had consistent boundaries across the various parameter maps and was most clearly visualised on the longitudinal relaxation rate maps. We have provided a quantitative multi-parametric characterisation that may be useful for future sequence optimisation to enhance visualisation of the habenula, and additionally provides reference values for future studies investigating pathological differences in habenula microstructure

Conduction velocity along a key white matter tract is associated with autobiographical memory recall ability

Conduction velocity is the speed at which electrical signals travel along axons and is a crucial determinant of neural communication. Inferences about conduction velocity can now be made in vivo in humans using a measure called the magnetic resonance (MR) g-ratio. This is the ratio of the inner axon diameter relative to that of the axon plus the myelin sheath that encases it. Here, in the first application to cognition, we found that variations in MR g-ratio, and by inference conduction velocity, of the parahippocampal cingulum bundle were associated with autobiographical memory recall ability in 217 healthy adults. This tract connects the hippocampus with a range of other brain areas. We further observed that the association seemed to be with inner axon diameter rather than myelin content. The extent to which neurites were coherently organised within the parahippocampal cingulum bundle was also linked with autobiographical memory recall ability. Moreover, these findings were specific to autobiographical memory recall and were not apparent for laboratory-based memory tests. Our results offer a new perspective on individual differences in autobiographical memory recall ability, highlighting the possible influence of specific white matter microstructure features on conduction velocity when recalling detailed memories of real-life past experiences

From in situ to ex vivo: the effect of autolysis and fixation on quantitative MRI markers for myelin

Ex vivo histology remains the gold standard against which MRI biophysical models, e.g. the MR g-ratio which characterises the fraction of a fibre’s diameter that is myelinated, are evaluated. The MR g-ratio model requires a measure of myelin density, for which magnetization transfer saturation (MT) has been used as a biomarker. However, changes occurring post mortem, e.g. autolysis, temperature changes and fixation, significantly alter the MRI signal. Here we investigate how these changes impact MT. We found that MT decreased post mortem but greatlyincreased upon fixation. These effects are similar to reported changes of other established MRI myelin-markers

Model-based multi-parameter mapping

Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch.Comment: 20 pages, 6 figures, accepted at Medical Image Analysi

An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI

Quantitative imaging aims to provide in vivo neuroimaging biomarkers with high research and diagnostic value that are sensitive to underlying tissue microstructure. In order to use these data to examine intra-cortical differences or to define boundaries between different myelo-architectural areas, high resolution data are required. The quality of such measurements is degraded in the presence of motion hindering insight into brain microstructure. Correction schemes are therefore vital for high resolution, whole brain coverage approaches that have long acquisition times and greater sensitivity to motion. Here we evaluate the use of prospective motion correction (PMC) via an optical tracking system to counter intra-scan motion in a high resolution (800 μm isotropic) multi-parameter mapping (MPM) protocol. Data were acquired on six volunteers using a 2 × 2 factorial design permuting the following conditions: PMC on/off and motion/no motion. In the presence of head motion, PMC-based motion correction considerably improved the quality of the maps as reflected by fewer visible artifacts and improved consistency. The precision of the maps, parameterized through the coefficient of variation in cortical sub-regions, showed improvements of 11–25% in the presence of deliberate head motion. Importantly, in the absence of motion the PMC system did not introduce extraneous artifacts into the quantitative maps. The PMC system based on optical tracking offers a robust approach to minimizing motion artifacts in quantitative anatomical imaging without extending scan times. Such a robust motion correction scheme is crucial in order to achieve the ultra-high resolution required of quantitative imaging for cutting edge in vivo histology applications

Multivariate Age-related Analysis of Variance in quantitative MRI maps: Widespread age-related differences revisited

AbstractThis study utilized multivariate ANOVA analysis to investigate age-related microstructural changes in the brain tissues driven primarily by myelin, iron, and water content. Voxel-wise analyses were performed on gray matter (GM) and white matter (WM), in addition to region of interest (ROI) analyses. The multivariate approach identified brain regions showing coordinated alterations in multiple tissue properties and demonstrated bidirectional correlations between age and all examined modalities in various brain regions, including the caudate nucleus, putamen, insula, cerebellum, lingual gyri, hippocampus, and olfactory bulb. The multivariate model was more sensitive than univariate analyses as evidenced by detecting a larger number of significant voxels within clusters in the supplementary motor area, frontal cortex, hippocampus, amygdala, occipital cortex, and cerebellum bilaterally. The examination of normalized, smoothed, and z-transformed maps within the ROIs revealed age-dependent differences in myelin, iron, and water content. These findings contribute to our understanding of age-related brain differences and provide insights into the underlying mechanisms of aging. The study emphasizes the importance of multivariate analysis for detecting subtle microstructural changes associated with aging that may motivate interventions to mitigate cognitive decline in older adults

Simultaneous adaptive smoothing of relaxometry and quantitative magnetization transfer mapping

Attempts for in-vivo histology require a high spatial resolution that comes with the price of a decreased signal-to-noise ratio. We present a novel iterative and multi-scale smoothing method for quantitative Magnetic Resonance Imaging (MRI) data that yield proton density, apparent transverse and longitudinal relaxation, and magnetization transfer maps. The method is based on the propagation-separation approach. The adaptivity of the procedure avoids the inherent bias from blurring subtle features in the calculated maps that is common for non-adaptive smoothing approaches. The characteristics of the methods were evaluated on a high-resolution data set (500 μ isotropic) from a single subject and quantified on data from a multi-subject study. The results show that the adaptive method is able to increase the signal-to-noise ratio in the calculated quantitative maps while largely avoiding the bias that is otherwise introduced by spatially blurring values across tissue borders. As a consequence, it preserves the intensity contrast between white and gray matter and the thin cortical ribbon

Analysis of the Precision of Variable Flip Angle T1 Mapping with Emphasis on the Noise Propagated from RF Transmit Field Maps

In magnetic resonance imaging, precise measurements of longitudinal relaxation time (T1) is crucial to acquire useful information that is applicable to numerous clinical and neuroscience applications. In this work, we investigated the precision of T1 relaxation time as measured using the variable flip angle method with emphasis on the noise propagated from radiofrequency transmit field (B1+) measurements. The analytical solution for T1 precision was derived by standard error propagation methods incorporating the noise from the three input sources: two spoiled gradient echo (SPGR) images and a B1+ map. Repeated in vivo experiments were performed to estimate the total variance in T1 maps and we compared these experimentally obtained values with the theoretical predictions to validate the established theoretical framework. Both the analytical and experimental results showed that variance in the B1+ map propagated comparable noise levels into the T1 maps as either of the two SPGR images. Improving precision of the B1+ measurements significantly reduced the variance in the estimated T1 map. The variance estimated from the repeatedly measured in vivoT1 maps agreed well with the theoretically-calculated variance in T1 estimates, thus validating the analytical framework for realistic in vivo experiments. We concluded that for T1 mapping experiments, the error propagated from the B1+ map must be considered. Optimizing the SPGR signals while neglecting to improve the precision of the B1+ map may result in grossly overestimating the precision of the estimated T1 values