Chapter 7 Estimating chemical and microstructural heterogeneity by correlating relaxation and diffusion

Whereas diffusion NMR can probe the structural configurations configurations of microscopic environments in biological tissue, relaxation can provide complementary information on their chemical composition. This chapter considers experiments in which diffusion diffusion and relaxation properties are sampled simultaneously by varying multiple acquisition parameters. As such, correlations between the diffusion and relaxation can be established, providing an altogether more complete picture of heterogeneous tissue

Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain

The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong () gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from at to at ), while this dependence on b in the corticospinal tract is less pronounced (from at to at ). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing

Imaging Alzheimer's genetic risk using Diffusion MRI: a systematic review

Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to investigate changes in white matter microstructure in Alzheimer’s Disease. This paper aims to systematically review studies that examined the effect of Alzheimer’s risk genes on white matter microstructure. We assimilated findings from 37 studies and reviewed their diffusion pre-processing and analysis methods. Most studies estimate the diffusion tensor (DT) and compare derived quantitative measures such as fractional anisotropy and mean diffusivity between groups. Those with increased AD genetic risk are associated with reduced anisotropy and increased diffusivity across the brain, most notably the temporal and frontal lobes, cingulum and corpus callosum. Structural abnormalities are most evident amongst those with established Alzheimer’s Disease. Recent studies employ signal representations and analysis frameworks beyond DT MRI but show that dMRI overall lacks specificity to disease pathology. However, as the field advances, these techniques may prove useful in pre-symptomatic diagnosis or staging of Alzheimer’s disease

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion- T 1 - T 2 ∗ -weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions

Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices

BACKGROUND AND PURPOSE: To apply and evaluate an intensity‐based interpolation technique, enabling segmentation of motion‐affected neonatal brain MRI. METHODS: Moderate‐late preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderate‐late Preterm infants “BIMP‐study”) between August 2017 and November 2019. T2‐weighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderate‐severe motion (n = 33 [29%]) were included. Motion‐affected slices were re‐estimated using intensity‐based shape‐preserving cubic spline interpolation, and automatically segmented in eight structures. Quality of interpolation and segmentation was visually assessed for errors after interpolation. Reliability was tested using interpolated control group scans (18/54 axial slices). Structural similarity index (SSIM) was used to compare T2‐weighted scans, and Sørensen‐Dice was used to compare segmentation before and after interpolation. Finally, volumes of brain structures of the control group were used assessing sensitivity (absolute mean fraction difference) and bias (confidence interval of mean difference). RESULTS: Visually, segmentation of 25 scans (22%) with motion artifacts improved with interpolation, while segmentation of eight scans (7%) with adjacent motion‐affected slices did not improve. Average SSIM was .895 and Sørensen‐Dice coefficients ranged between .87 and .97. Absolute mean fraction difference was ≤0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14‐3.07 cm(3)), cerebrospinal fluid (0.39‐1.65 cm(3)), deep gray matter (0.74‐1.01 cm(3)), and brainstem volumes (0.07‐0.28 cm(3)) and a negative bias in white matter volumes (–4.47 to –1.65 cm(3)). CONCLUSION: According to qualitative and quantitative assessment, intensity‐based interpolation reduced the percentage of discarded scans from 29% to 7%

Fiber tractography bundle segmentation depends on scanner effects, vendor effects, acquisition resolution, diffusion sampling scheme, diffusion sensitization, and bundle segmentation workflow

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate six potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across vendors (4) across acquisition resolution, (5) across diffusion schemes, and (6) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by vendor-effects, scanner-effects, and finally diffusion scheme and b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods

Surface-based tracking for short association fibre tractography

It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed

Detecting microstructural deviations in individuals with deep diffusion MRI tractometry

Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside