Microstructural imaging of the human spinal cord with advanced diffusion MRI

The aim of this PhD thesis is to advance the state-of-the-art of spinal cord magnetic resonance imaging (MRI) in multiple sclerosis (MS), a demyelinating, inflammatory and neurodegenerative disease of the central nervous system. Neurite orientation dispersion and density imaging (NODDI) is a recent diffusion-weighted (DW) MRI technique that provides indices of density and orientation dispersion of neuronal processes. These could be new useful biomarkers for the spinal cord, since they could better characterise overall, widespread MS pathology than conventional metrics. In this thesis, we test innovative clinically feasible acquisitions as well as signal analysis methods to study the potential of NODDI for the spinal cord. We also design and run computer simulations that corroborate our in vivo findings. Furthermore, we compare NODDI metrics to quantitative histological features, with the aim of validating their specificity. The thesis is divided in two parts. In the first part, in vivo experiments are described. Specific objectives are: i) to demonstrate the feasibility of performing NODDI in the spinal cord and in clinical settings; ii) to study the possibility of extracting with new approaches such as NODDI more specific microstructural information from standard DW acquisitions; iii) to assess how features typical of spinal cord microstructure, such as presence of large axons, influence NODDI metrics. In the second part of the thesis, ex vivo experiments are discussed. Their objective is the validation of the specificity of NODDI metrics via comparison to quantitative histology in post mortem spinal cord tissue. The experiments required the implementation of high-field DW scans as well as histological procedures and complex analysis pipelines. The results of this thesis contribute to current scientific knowledge. They prove that NODDI offers new opportunities to study how neurodegenerative diseases such as MS alter neural tissue complexity. We showed for the first time that NODDI can be performed in the spinal cord in vivo and in clinical scans. We also demonstrated that NODDI analysis of standard DW data is challenging, and quantified how the presence of large axons in the spinal cord influences NODDI metrics. Lastly, our ex vivo data highlight that unlike routine DW MRI methods, NODDI can detect reliably pathological variations of neurite orientation dispersion. NODDI is also sensitive to the density of axons and dendrites, but can not fully resolve axonal loss and demyelination in MS. We believe that the technique is a key element of a more general multi-modal MRI approach, which is necessary to obtain a complete description of complex diseases such as MS

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications

Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo

Here we present the application of neurite orientation dispersion and density imaging (NODDI) to the healthy spinal cord in vivo. NODDI provides maps such as the intra-neurite tissue volume fraction (vin), the orientation dispersion index (ODI) and the isotropic volume fraction (viso), and here we investigate their potential for spinal cord imaging. We scanned five healthy volunteers, four of whom twice, on a 3 T MRI system with a ZOOM-EPI sequence. In accordance to the published NODDI protocol, multiple b-shells were acquired at cervical level and both NODDI and diffusion tensor imaging (DTI) metrics were obtained and analysed to: i) characterise differences in grey and white matter (GM/WM); ii) assess the scan–rescan reproducibility of NODDI; iii) investigate the relationship between NODDI and DTI; and iv) compare the quality of fit of NODDI and DTI. Our results demonstrated that: i) anatomical features can be identified in NODDI maps, such as clear contrast between GM and WM in ODI; ii) the variabilities of vin and ODI are comparable to that of DTI and are driven by biological differences between subjects for ODI, have similar contribution from measurement errors and biological variation for vin, whereas viso shows higher variability, driven by measurement errors; iii) NODDI identifies potential sources contributing to DTI indices, as in the brain; and iv) NODDI outperforms DTI in terms of quality of fit. In conclusion, this work shows that NODDI is a useful model for in vivo diffusion MRI of the spinal cord, providing metrics closely related to tissue microstructure, in line with findings in the brain

Bayesian image quality transfer

Image quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However,the original framework gives no indication of confidence in its output,which is a significant barrier to adoption in clinical practice and downstream processing. In this article,we present a general Bayesian extension of IQT which enables efficient and accurate quantification of uncertainty,providing users with an essential prediction of the accuracy of enhanced images. We demonstrate the efficacy of the uncertainty quantification through super-resolution of diffusion tensor images of healthy and pathological brains. In addition,the new method displays improved performance over the original IQT and standard interpolation techniques in both reconstruction accuracy and robustness to anomalies in input images

Fast and reproducible in vivo T1 mapping of the human cervical spinal cord

PURPOSE: To develop a fast and robust method for measuring T1 in the whole cervical spinal cord in vivo, and to assess its reproducibility. METHODS: A spatially nonselective adiabatic inversion pulse is combined with zonally oblique-magnified multislice echo-planar imaging to produce a reduced field-of-view inversion-recovery echo-planar imaging protocol. Multi- inversion time data are obtained by cycling slice order throughout sequence repetitions. Measurement of T1 is performed using 12 inversion times for a total protocol duration of 7 min. Reproducibility of regional T1 estimates is assessed in a scan-rescan experiment on five heathy subjects. RESULTS: Regional mean (standard deviation) T1 was: 1108.5 (±77.2) ms for left lateral column, 1110.1 (±83.2) ms for right lateral column, 1150.4 (±102.6) ms for dorsal column, and 1136.4 (±90.8) ms for gray matter. Regional T1 estimates showed good correlation between sessions (Pearson correlation coefficient = 0.89 (P value < 0.01); mean difference = 2 ms, 95% confidence interval ± 20 ms); and high reproducibility (intersession coefficient of variation approximately 1% in all the regions considered, intraclass correlation coefficient = 0.88 (P value < 0.01, confidence interval 0.71-0.95)). CONCLUSIONS: T1 estimates in the cervical spinal cord are reproducible using inversion-recovery zonally oblique-magnified multislice echo-planar imaging. The short acquisition time and large coverage of this method paves the way for accurate T1 mapping for various spinal cord pathologies. Magn Reson Med, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Investigating the effect of selective logging on tree biodiversity and structure of the tropical forests of Papua New Guinea

Abstract: Unsustainable exploitation of tropical forest resources is raising worldwide concern. In Papua New Guinea (PNG) timber harvesting has been identified as a major contributor to deforestation and forest degradation but its impact on biodiversity is still poorly understood. In this study we investigated the effect of selective logging on tree taxonomic composition, structure and diversity of PNG forests. We used data from 101 one-hectare permanent sample plots (PSPs) belonging to two vegetation types: low altitude forests on plains and fans (type P) and low altitude forests on uplands (type H). We used multivariate techniques to test for significant differences in species composition between plots of different vegetation types and disturbance regimes, identifying the tree taxa to which these differences could be ascribed. ANOVA was used to test for differences between logged-over and unlogged forest PSPs with respect to biodiversity (richness, Shannon's diversity, Pielou's evenness) and stand structure (stem density, basal area - BA). Temporal trends of forest features were analyzed using linear regression. Significant differences in taxonomic composition were found between logged-over and unlogged plots of the H type (p = 0.04). No differences were found in richness, diversity and evenness between logged-over and unlogged forest plots, while stem density was higher in the latter (421 ± 153 stems ha-1). Greater BA was found in unlogged forests (30.28 ± 4.45 m2 ha-1) of the H type when compared to the logged-over stands (15.52 ± 4.04 m2 ha-1). We detected positive trends in richness (0.55 ± 0.19 taxa ha-1 yr-1) and diversity after logging. Furthermore, H type forest exhibited positive trends in stem density (9 ± 1 stems ha-1 yr-1) and BA (0.42 ± 0.06 m2 ha-1 yr-1) with elapsed time since harvesting. Our analysis highlights some significant effects of logging activities on biodiversity and structure of PNG forests. Additionally, forests exhibited a significant recovery with respect to richness, diversity and stand structure. These preliminary results will be compared with data collected by the forthcoming National Forest Inventory in order to assess and monitor the effects of human activities and ecological factors on PNG forest biodiversity and develop appropriate conservation measures and sustainable management strategies

Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy

Spinal cord atrophy measurements obtained from structural magnetic resonance imaging (MRI) are associated with disability in many neurological diseases and serve as in vivo biomarkers of neurodegeneration. Longitudinal spinal cord atrophy rate is commonly determined from the numerical difference between two volumes (based on 3D surface fitting) or two cross-sectional areas (CSA, based on 2D edge detection) obtained at different time-points. Being an indirect measure, atrophy rates are susceptible to variable segmentation errors at the edge of the spinal cord. To overcome those limitations, we developed a new registration-based pipeline that measures atrophy rates directly. We based our approach on the generalised boundary shift integral (GBSI) method, which registers 2 scans and uses a probabilistic XOR mask over the edge of the spinal cord, thereby measuring atrophy more accurately than segmentation-based techniques. Using a large cohort of longitudinal spinal cord images (610 subjects with multiple sclerosis from a multi-centre trial and 52 healthy controls), we demonstrated that GBSI is a sensitive, quantitative and objective measure of longitudinal spinal cord volume change. The GBSI pipeline is repeatable, reproducible, and provides more precise measurements of longitudinal spinal cord atrophy than segmentation-based methods in longitudinal spinal cord atrophy studies

Atrophy computation in the spinal cord using the Boundary Shift Integral

In this work, we introduce a new pipeline based on the latest iteration of the BSI for computing atrophy in the SC and compare its results with the most popular atrophy measurements for this region, mean CSA. We demonstrated for the first time the use of BSI in the SC, as a sensitive, quantitative and objective measure of longitudinal tissue volume change. The BSI pipeline presented in this work is repeatable, reproducible and standardises a pipeline for computing SC atrophy