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

Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging

Brain; Protocol design; Quantitative MRI (qMRI)Cerebro; Diseño de protocolo; Resonancia magnética cuantitativa (qMRI)Cervell; Disseny del protocol; Ressonància magnètica quantitativa (qMRI)Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI). Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly. Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons. Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design.This project was funded by the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1, G007748, I027084, N018702). This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541 and 666992, and from: Rosetrees Trust (United Kingdom, funding FG); Prostate Cancer United Kingdom Targeted Call 2014 (Translational Research St.2, project reference PG14-018-TR2); Cancer Research United Kingdom grant ref. A21099; Spinal Research (United Kingdom), Wings for Life (Austria), Craig H. Neilsen Foundation (United States) for jointly funding the INSPIRED study; Wings for Life (#169111); United Kingdom Multiple Sclerosis Society (grants 892/08 and 77/2017); the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Centres and UCLH NIHR Biomedical Research Centre; Champalimaud Centre for the Unknown, Lisbon (Portugal); European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101003390. FG is currently supported by the investigator-initiated PREdICT study at the Vall d’Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and CRIS Cancer Foundation

Assessing Lumbar Plexus and Sciatic Nerve Damage in Relapsing-Remitting Multiple Sclerosis Using Magnetisation Transfer Ratio

Neurografia de ressonància magnètica (MRN); Relació de transferència de magnetització (MTR); Esclerosi múltiple del sistema nerviós perifèric (SNP)Neurografía de resonancia magnética (MRN); Relación de transferencia de magnetización (MTR); Esclerosis múltiple del sistema nervioso periférico (SNP)Magnetic resonance neurography (MRN); Magnetisation transfer ratio (MTR); Multiple sclerosis peripheral nervous system (PNS)Background: Multiple sclerosis (MS) has traditionally been regarded as a disease confined to the central nervous system (CNS). However, neuropathological, electrophysiological, and imaging studies have demonstrated that the peripheral nervous system (PNS) is also involved, with demyelination and, to a lesser extent, axonal degeneration representing the main pathophysiological mechanisms. Aim: The purpose of this study was to assess PNS damage at the lumbar plexus and sciatic nerve anatomical locations in people with relapsing-remitting MS (RRMS) and healthy controls (HCs) in vivo using magnetisation transfer ratio (MTR), which is a known imaging biomarker sensitive to alterations in myelin content in neural tissue, and not previously explored in the context of PNS damage in MS. Method: Eleven HCs (7 female, mean age 33.6 years, range 24-50) and 15 people with RRMS (12 female, mean age 38.5 years, range 30-56) were recruited for this study and underwent magnetic resonance imaging (MRI) investigations together with clinical assessments using the expanded disability status scale (EDSS). Magnetic resonance neurography (MRN) was first used for visualisation and identification of the lumbar plexus and the sciatic nerve and MTR imaging was subsequently performed using identical scan geometry to MRN, enabling straightforward co-registration of all data to obtain global and regional mean MTR measurements. Linear regression models were used to identify differences in MTR values between HCs and people with RRMS and to identify an association between MTR measures and EDSS. Results: MTR values in the sciatic nerve of people with RRMS were found to be significantly lower compared to HCs, but no significant MTR changes were identified in the lumbar plexus of people with RRMS. The median EDSS in people with RRMS was 2.0 (range, 0-3). No relationship between the MTR measures in the PNS and EDSS were identified at any of the anatomical locations studied in this cohort of people with RRMS. Conclusion: The results from this study demonstrate the presence of PNS damage in people with RRMS and support the notion that these changes, suggestive of demyelination, maybe occurring independently at different anatomical locations within the PNS. Further investigations to confirm these findings and to clarify the pathophysiological basis of these alterations are warranted.The UK MS Society and the UCL-UCLH Biomedical Research Centre for ongoing support. CGW-K receives funding from the MS Society (#77), Wings for Life (#169111), BRC (#BRC704/CAP/CGW), UCL Global Challenges Research Fund (GCRF), MRC (#MR/S026088/1), Ataxia UK. FP had a non-clinical Postdoctoral Guarantors of Brain fellowship (2017-2020). FP was supported by the National Institute for Health Research, UCL Hospitals Biomedical Research Centre. CT is being funded by a Junior Leader La Caixa Fellowship (fellowship code is LCF/BQ/PI20/11760008), awarded by la Caixa Foundation (ID 100010434). She has also received the 2021 Merck's Award for the Investigation in MS, awarded by Fundación Merck Salud (Spain). In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK MS Society. She has also received honoraria from Roche and Novartis, and is a steering committee member of the O'HAND trial and of the Consensus group on Follow-on DMTs. This project has received funding under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 634541 and from the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1), funding FG. FG was currently supported by PREdICT, a study at the Vall d'Hebron Institute of Oncology in Barcelona funded by AstraZeneca

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

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

Reduced field-of-view diffusion-weighted imaging of the lumbosacral enlargement: a pilot in vivo study of the healthy spinal cord at 3T

Diffusion tensor imaging (DTI) has recently started to be adopted into clinical investigations of spinal cord (SC) diseases. However, DTI applications to the lower SC are limited due to a number of technical challenges, related mainly to the even smaller size of the SC structure at this level, its position relative to the receiver coil elements and the effects of motion during data acquisition. Developing methods to overcome these problems would offer new means to gain further insights into microstructural changes of neurological conditions involving the lower SC, and in turn could help explain symptoms such as bladder and sexual dysfunction. In this work, the feasibility of obtaining grey and white matter (GM/WM) DTI indices such as axial/radial/mean diffusivity (AD/RD/MD) and fractional anisotropy (FA) within the lumbosacral enlargement (LSE) was investigated using a reduced field-of-view (rFOV) single-shot echo-planar imaging (ss-EPI) acquisition in 14 healthy participants using a clinical 3T MR system. The scan-rescan reproducibility of the measurements was assessed by calculating the percentage coefficient of variation (%COV). Mean FA was higher in WM compared to GM (0.58 and 0.4 in WM and GM respectively), AD and MD were higher in WM compared to GM (1.66 µm2ms-1 and 0.94 µm2ms-1 in WM and 1.2 µm2ms-1 and 0.82 µm2ms-1 in GM for AD and MD respectively) and RD was lower in WM compared to GM (0.58 µm2ms-1 and 0.63 µm2ms-1 respectively). The scan-rescan %COV was lower than 10% in all cases with the highest values observed for FA and the lowest for MD. This pilot study demonstrates that it is possible to obtain reliable tissue-specific estimation of DTI indices within the LSE using a rFOV ss-EPI acquisition. The DTI acquisition and analysis protocol presented here is clinically feasible and may be used in future investigations of neurological conditions implicating the lower SC

SARDU-Net: a new method for model-free, data-driven experiment design in quantitative MRI

This work introduces the “Select and retrieve via direct up-sampling” network (SARDU-Net), a new method for model-free, data-driven quantitative MRI (qMRI) experiment design. SARDU-Net identifies informative measurements within lengthy acquisitions and reconstructs fully-sampled signals from a sub-protocol, without prior information on the MRI contrast. It combines two deep networks: a selector, which selects a signal sub-sample, and a predictor, which retrieves input signals. SARDU-Net can be run with standard computational resources and can increase the clinical appeal of qMRI. Here we demonstrate its potential on qMRI of prostate and spinal cord, two areas where fast acquisitions are key