Delineation of early brain development from fetuses to infants with diffusion MRI and beyond

Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

PCA-based groupwise image registration for quantitative MRI

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI.

Specific white matter tissue microstructure changes associated with obesity

Obesity-related structural brain alterations point to a consistent reduction in gray matter with increasing body mass index (BMI) but changes in white matter have proven to be more complex and less conclusive. Hence, more recently diffusion tensor imaging (DTI) has been employed to investigate microstructural changes in white matter structure. Altogether, these studies have mostly shown a loss of white matter integrity with obesity-related factors in several brain regions. However, the variety of these obesity-related factors, including inflammation and dyslipidemia, resulted in competing influences on the DTI indices. To increase the specificity of DTI results, we explored specific brain tissue properties by combining DTI with quantitative multi-parameter mapping in lean, overweight and obese young adults. By means of multi-parameter mapping, white matter structures showed differences in MRI parameters consistent with reduced myelin, increased water and altered iron content with increasing BMI in the superior longitudinal fasciculus, anterior thalamic radiation, internal capsule and corpus callosum. BMI-related changes in DTI parameters revealed mainly alterations in mean and axial diffusivity with increasing BMI in the corticospinal tract, anterior thalamic radiation and superior longitudinal fasciculus. These alterations, including mainly fiber tracts linking limbic structures with prefrontal regions, could potentially promote accelerated aging in obese individuals leading to an increased risk for cognitive decline.

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We combined DTI with quantitative multi-parametric mapping (qMPM).

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Obesity was associated with a reduction in myelin displayed by decreased R1.

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Obesity was associated with increased water content (decreased R1 and increased PD*).

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DTI revealed mainly alterations in mean and axial diffusivity with obesity.

Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator

PURPOSE

Systems for magnetic resonance (MR-) guided radiotherapy enable daily MR imaging of cancer patients during treatment, which is of interest for treatment response monitoring and biomarker discovery using quantitative MRI (qMRI). Here, the performance of a 1.5 T MR-linac regarding qMRI was assessed on phantoms. Additionally, we show the feasibility of qMRI in a prostate cancer patient on this system for the first time.

MATERIALS AND METHODS

Four 1.5 T MR-linac systems from four institutes were included in this study. T₁ and T₂ relaxation times, and apparent diffusion coefficient (ADC) maps, as well as dynamic contrast enhanced (DCE) images were acquired. Bland-Altman statistics were used, and accuracy, repeatability, and reproducibility were determined.

RESULTS

Median accuracy for T₁ ranged over the four systems from 2.7 to 14.3%, for T₂ from 10.4 to 14.1%, and for ADC from 1.9 to 2.7%. For DCE images, the accuracy ranged from 12.8 to 35.8% for a gadolinium concentration of 0.5 mM and deteriorated for higher concentrations. Median short-term repeatability for T₁ ranged from 0.6 to 5.1%, for T₂ from 0.4 to 1.2%, and for ADC from 1.3 to 2.2%. DCE acquisitions showed a coefficient of variation of 0.1-0.6% in the signal intensity. Long-term repeatability was 1.8% for T₁, 1.4% for T₂, 1.7% for ADC, and 17.9% for DCE. Reproducibility was 11.2% for T₁, 2.9% for T₂, 2.2% for ADC, and 18.4% for DCE.

CONCLUSION

These results indicate that qMRI on the Unity MR-linac is feasible, accurate, and repeatable which is promising for treatment response monitoring and treatment plan adaptation based on daily qMRI.

χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.

Cartilage T1ρ and T2 relaxation times: longitudinal reproducibility and variations using different coils, MR systems and sites

OBJECTIVE

To evaluate the longitudinal reproducibility and variations of cartilage T1ρ and T2 measurements using different coils, MR systems and sites.

METHODS

Single-Site study: Phantom data were collected monthly for up to 29 months on four GE 3T MR systems. Data from phantoms and human subjects were collected on two MR systems using the same model of coil; and were collected on one MR system using two models of coils. Multi-site study: Three participating sites used the same model of MR systems and coils, and identical imaging protocols. Phantom data were collected monthly. Human subjects were scanned and rescanned on the same day at each site. Two traveling human subjects were scanned at all three sites.

RESULTS

Single-Site Study: The phantom longitudinal RMS-CVs ranged from 1.8% to 2.7% for T1ρ and 1.8-2.8% for T2. Significant differences were found in T1ρ and T2 values using different MR systems and coils. Multi-Site Study: The phantom longitudinal RMS-CVs ranged from 1.3% to 2.6% for T1ρ and 1.2-2.7% for T2. Across three sites (n = 16), the in vivo scan-rescan RMS-CV was 3.1% and 4.0% for T1ρ and T2, respectively. Phantom T1ρ and T2 values were significantly different between three sites but highly correlated (R > 0.99). No significant difference was found in T1ρ and T2 values of traveling controls, with cross-site RMS-CV as 4.9% and 4.4% for T1ρ and T2, respectively.

CONCLUSION

With careful quality control and cross-calibration, quantitative MRI can be readily applied in multi-site studies and clinical trials for evaluating cartilage degeneration.

Dynamic contrast-enhanced MRI in oncology: how we do it

Magnetic resonance imaging (MRI) is particularly attractive for clinical application in perfusion imaging thanks to the absence of ionizing radiation and limited volumes of contrast agent (CA) necessary. Dynamic contrast-enhanced MRI (DCE-MRI) involves sequentially acquiring T1-weighted images through an organ of interest during the passage of a bolus administration of CA. It is a particularly flexible approach to perfusion imaging as the signal intensity time course allows not only rapid qualitative assessment, but also quantitative measures of intrinsic perfusion and permeability parameters. We examine aspects of the T1-weighted image series acquisition, CA administration, post-processing that constitute a DCE-MRI study in clinical practice, before considering some heuristics that may aid in interpreting the resulting contrast enhancement time series. While qualitative DCE-MRI has a well-established role in the diagnostic assessment of a range of tumours, and a central role in MR mammography, clinical use of quantitative DCE-MRI remains limited outside of clinical trials. The recent publication of proposals for standardized acquisition and analysis protocols for DCE-MRI by the Quantitative Imaging Biomarker Alliance may be an opportunity to consolidate and advance clinical practice.

Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort

OBJECTIVE

We aim to study to what extent conventional and deep-learning-based T2 relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA).

METHODS

T2 relaxation time maps were analyzed for 4,384 subjects from the baseline Osteoarthritis Initiative (OAI) Dataset. Voxel Based Relaxometry (VBR) was used for automatic quantification and voxel-based analysis of the differences in T2 between subjects with and without radiographic OA. A Densely Connected Convolutional Neural Network (DenseNet) was trained to diagnose OA from T2 data. For comparison, more classical feature extraction techniques and shallow classifiers were used to benchmark the performance of our algorithm's results. Deep and shallow models were evaluated with and without the inclusion of risk factors. Sensitivity and Specificity values and McNemar test were used to compare the performance of the different classifiers.

RESULTS

The best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC = 77.77%, Sensitivity = 67.01%, Specificity = 71.79%). In comparison, DenseNet trained on raw T2 data obtained AUC = 83.44%, Sensitivity = 76.99%, Specificity = 77.94%. McNemar test on two misclassified proportions form the shallow and deep model showed that the boost in performance was statistically significant (McNemar's chi-squared = 10.33, degree of freedom (DF) = 1, P-value = 0.0013).

CONCLUSION

In this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T2 measurements to characterize radiographic OA. Our results showed that feature learning from T2 maps has potential in uncovering information that can potentially better diagnose OA than simple averages or linear patterns decomposition.

Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH).

METHODS

Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC).

RESULTS

A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74-0.82), for liver fat was 0.78 (95% CI, 0.73-0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78-0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74-0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64-0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75-0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers.

CONCLUSIONS

This study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways.

Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure

Measuring the structural composition of the cortex is critical to understanding typical development, yet few investigations in humans have charted markers in vivo that are sensitive to tissue microstructural attributes. Here, we used a well-validated quantitative MR protocol to measure four parameters (R₁, MT, R₂*, PD*) that differ in their sensitivity to facets of the tissue microstructural environment (R₁, MT: myelin, macromolecular content; R₂*: myelin, paramagnetic ions, i.e., iron; PD*: free water content). Mapping these parameters across cortical regions in a young adult cohort (18–39 years, N = 93) revealed expected patterns of increased macromolecular content as well as reduced tissue water content in primary and primary adjacent cortical regions. Mapping across cortical depth within regions showed decreased expression of myelin and related processes – but increased tissue water content – when progressing from the grey/white to the grey/pial boundary, in all regions. Charting developmental change in cortical microstructure cross-sectionally, we found that parameters with sensitivity to tissue myelin (R₁ & MT) showed linear increases with age across frontal and parietal cortex (change 0.5–1.0% per year). Overlap of robust age effects for both parameters emerged in left inferior frontal, right parietal and bilateral pre-central regions. Our findings afford an improved understanding of ontogeny in early adulthood and offer normative quantitative MR data for inter- and intra-cortical composition, which may be used as benchmarks in further studies.

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We mapped multi-parameter maps (MPMs) across and within cortical regions.

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We charted age effects (ages 18–39) on myelin and related processes.

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MPMs sensitive to myelin (R₁, MT) showed elevated values in primary areas over most cortical depths.

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R₂* map foci tended to overlap MPMs sensitive to myelin (R₁, MT).

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R₁ and MT increased with age (0.5–1.0% per year) at mid-depth in frontal and parietal cortex.

A robust multi-scale approach to quantitative susceptibility mapping

Quantitative Susceptibility Mapping (QSM), best known as a surrogate for tissue iron content, is becoming a highly relevant MRI contrast for monitoring cellular and vascular status in aging, addiction, traumatic brain injury and, in general, a wide range of neurological disorders. In this study we present a new Bayesian QSM algorithm, named Multi-Scale Dipole Inversion (MSDI), which builds on the nonlinear Morphology-Enabled Dipole Inversion (nMEDI) framework, incorporating three additional features: (i) improved implementation of Laplace's equation to reduce the influence of background fields through variable harmonic filtering and subsequent deconvolution, (ii) improved error control through dynamic phase-reliability compensation across spatial scales, and (iii) scalewise use of the morphological prior. More generally, this new pre-conditioned QSM formalism aims to reduce the impact of dipole-incompatible fields and measurement errors such as flow effects, poor signal-to-noise ratio or other data inconsistencies that can lead to streaking and shadowing artefacts. In terms of performance, MSDI is the first algorithm to rank in the top-10 for all metrics evaluated in the 2016 QSM Reconstruction Challenge. It also demonstrated lower variance than nMEDI and more stable behaviour in scan-rescan reproducibility experiments for different MRI acquisitions at 3 and 7 Tesla. In the present work, we also explored new forms of susceptibility MRI contrast making explicit use of the differential information across spatial scales. Specifically, we show MSDI-derived examples of: (i) enhanced anatomical detail with susceptibility inversions from short-range dipole fields (hereby referred to as High-Pass Susceptibility Mapping or HPSM), (ii) high specificity to venous-blood susceptibilities for highly regularised HPSM (making a case for MSDI-based Venography or VenoMSDI), (iii) improved tissue specificity (and possibly statistical conditioning) for Macroscopic-Vessel Suppressed Susceptibility Mapping (MVSSM), and (iv) high spatial specificity and definition for HPSM-based Susceptibility-Weighted Imaging (HPSM-SWI) and related intensity projections.

The influence of brain iron on myelin water imaging

With myelin playing a vital role in normal brain integrity and function and thus in various neurological disorders, myelin sensitive magnetic resonance imaging (MRI) techniques are of great importance. In particular, multi-exponential T₂ relaxation was shown to be highly sensitive to myelin. The myelin water imaging (MWI) technique allows to separate the T₂ decay into short components, specific to myelin water, and long components reflecting the intra- and extracellular water. The myelin water fraction (MWF) is the ratio of the short components to all components. In the brain's white matter (WM), myelin and iron are closely linked via the presence of iron in the myelin generating oligodendrocytes. Iron is known to decrease T₂ relaxation times and may therefore mimic myelin. In this study, we investigated if variations in WM iron content can lead to apparent MWF changes. We performed MWI in post mortem human brain tissue prior and after chemical iron extraction. Histology for iron and myelin confirmed a decrease in iron content and no change in myelin content after iron extraction. In MRI, iron extraction lead to a decrease in MWF by 26%-28% in WM. Thus, a change in MWF does not necessarily reflect a change in myelin content. This observation has important implications for the interpretation of MWI findings in previously published studies and future research.

Metacognitive ability correlates with hippocampal and prefrontal microstructure

The ability to introspectively evaluate our experiences to form accurate metacognitive beliefs, or insight, is an essential component of decision-making. Previous research suggests individuals vary substantially in their level of insight, and that this variation is related to brain volume and function, particularly in the anterior prefrontal cortex (aPFC). However, the neurobiological mechanisms underlying these effects are unclear, as qualitative, macroscopic measures such as brain volume can be related to a variety of microstructural features. Here we leverage a high-resolution (800 µm isotropic) multi-parameter mapping technique in 48 healthy individuals to delineate quantitative markers of in vivo histological features underlying metacognitive ability. Specifically, we examined how neuroimaging markers of local grey matter myelination and iron content relate to insight as measured by a signal-theoretic model of subjective confidence. Our results revealed a pattern of microstructural correlates of perceptual metacognition in the aPFC, precuneus, hippocampus, and visual cortices. In particular, we extend previous volumetric findings to show that right aPFC myeloarchitecture positively relates to metacognitive insight. In contrast, decreased myelination in the left hippocampus correlated with better metacognitive insight. These results highlight the ability of quantitative neuroimaging to reveal novel brain-behaviour correlates and may motivate future research on their environmental and developmental underpinnings.

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Metacognitive ability (insight) differs strongly between individuals.

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We used quantitative MRI to relate metacognition to brain microstructure.

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Insight correlated positively with anterior prefrontal myelination.

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Myelination of the left hippocampus negatively related to insight.

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Iron levels in the visual cortex were negatively associated with metacognition.

Longitudinal evaluation of T1ρ and T2 spatial distribution in osteoarthritic and healthy medial knee cartilage

OBJECTIVE

To investigate longitudinal changes in laminar and spatial distribution of knee articular cartilage magnetic resonance imaging (MRI) T1ρ and T2 relaxation times, in individuals with and without medial compartment cartilage defects.

DESIGN

All subjects (at baseline n = 88, >18 years old) underwent 3-Tesla knee MRI at baseline and annually thereafter for 3 years. The MR studies were evaluated for presence of cartilage defects (modified Whole-Organ Magnetic Resonance Imaging Scoring - mWORMS), and quantitative T1ρ and T2 relaxation time maps. Subjects were segregated into those with (mWORMS ≥2) and without (mWORMS ≤1) cartilage lesions at the medial tibia (MT) or medial femur (MF) at each time point. Laminar (bone and articular layer) and spatial (gray level co-occurrence matrix - GLCM) distribution of the T1ρ and T2 relaxation time maps were calculated. Linear regression models (cross-sectional) and Generalized Estimating Equations (GEEs) (longitudinal) were used.

RESULTS

Global T1ρ, global T2 and articular layer T2 relaxation times at the MF, and global and articular layer T2 relaxation times at the MT, were higher in subjects with cartilage lesions compared to those without lesions. At the MT global T1ρ relaxation times were higher at each time point in subjects with lesions. MT T1ρ and T2 became progressively more heterogeneous than control compartments over the course of the study.

CONCLUSION

Spatial distribution of T1ρ and T2 relaxation time maps in medial knee OA using GLCM technique may be a sensitive indicator of cartilage deterioration, in addition to whole-compartment relaxation time data.

Fast quantitative MRI as a nonlinear tomography problem

Quantitative Magnetic Resonance Imaging (MRI) is based on a two-steps approach: estimation of the magnetic moments distribution inside the body, followed by a voxel-by-voxel quantification of the human tissue properties. This splitting simplifies the computations but poses several constraints on the measurement process, limiting its efficiency. Here, we perform quantitative MRI as a one step process; signal localization and parameter quantification are simultaneously obtained by the solution of a large scale nonlinear inversion problem based on first-principles. As a consequence, the constraints on the measurement process can be relaxed and acquisition schemes that are time efficient and widely available in clinical MRI scanners can be employed. We show that the nonlinear tomography approach is applicable to MRI and returns human tissue maps from very short experiments.

Multiparameter MRI quantification of microstructural tissue alterations in multiple sclerosis

Objectives

Conventional MRI is not sensitive to many pathological processes underpinning multiple sclerosis (MS) ongoing in normal appearing brain tissue (NABT). Quantitative MRI (qMRI) and a multiparameter mapping (MPM) protocol are used to simultaneously quantify magnetization transfer (MT) saturation, transverse relaxation rate R2* (1/T2*) and longitudinal relaxation rate R1 (1/T1), and assess differences in NABT microstructure between MS patients and healthy controls (HC).

Methods

This prospective cross-sectional study involves 36 MS patients (21 females, 15 males; age range 22–63 years; 15 relapsing-remitting MS - RRMS; 21 primary or secondary progressive MS - PMS) and 36 age-matched HC (20 females, 16 males); age range 21–61 years). The qMRI maps are computed and segmented in lesions and 3 normal appearing cerebral tissue classes: normal appearing cortical grey matter (NACGM), normal appearing deep grey matter (NADGM), normal appearing white matter (NAWM). Individual median values are extracted for each tissue class and MR parameter. MANOVAs and stepwise regressions assess differences between patients and HC.

Results

MS patients are characterized by a decrease in MT, R2* and R1 within NACGM (p < .0001) and NAWM (p < .0001). In NADGM, MT decreases (p < .0001) but R2* and R1 remain normal. These observations tend to be more pronounced in PMS. Quantitative MRI parameters are independent predictors of clinical status: EDSS is significantly related to R1 in NACGM and R2* in NADGM; the latter also predicts motor score. Cognitive score is best predicted by MT parameter within lesions.

Conclusions

Multiparametric data of brain microstructure concord with the literature, predict clinical performance and suggest a diffuse reduction in myelin and/or iron content within NABT of MS patients.

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We revisit microstructural alterations of NABT in MS patients by simultaneously quantifying three MRI parameters.

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Data suggest reduction of MT/R2*/R1 in NABT of MS patients, suggesting a reduction in myelin and/or iron content.

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Quantitative MRI parameters in NABT are independent predictors of clinical status.

Abnormal white matter properties in adolescent girls with anorexia nervosa

Anorexia nervosa (AN) is a serious eating disorder that typically emerges during adolescence and occurs most frequently in females. To date, very few studies have investigated the possible impact of AN on white matter tissue properties during adolescence, when white matter is still developing. The present study evaluated white matter tissue properties in adolescent girls with AN using diffusion MRI with tractography and T1 relaxometry to measure R1 (1/T1), an index of myelin content. Fifteen adolescent girls with AN (mean age = 16.6 years ± 1.4) were compared to fifteen age-matched girls with normal weight and eating behaviors (mean age = 17.1 years ± 1.3). We identified and segmented 9 bilateral cerebral tracts (18) and 8 callosal fiber tracts in each participant's brain (26 total). Tract profiles were generated by computing measures for fractional anisotropy (FA) and R1 along the trajectory of each tract. Compared to controls, FA in the AN group was significantly decreased in 4 of 26 white matter tracts and significantly increased in 2 of 26 white matter tracts. R1 was significantly decreased in the AN group compared to controls in 11 of 26 white matter tracts. Reduced FA in combination with reduced R1 suggests that the observed white matter differences in AN are likely due to reductions in myelin content. For the majority of tracts, group differences in FA and R1 did not occur within the same tract. The present findings have important implications for understanding the neurobiological factors underlying white matter changes associated with AN and invite further investigations examining associations between white matter properties and specific physiological, cognitive, social, or emotional functions affected in AN.

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AN girls had both increased and decreased FA in 4 white matter tracts.

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AN girls had increased R1 in 11 white matter tracts.

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White matter differences in AN are likely related to changes in myelin content.

Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence

OBJECTIVES

To evaluate the performance of T2 mapping in discriminating prostate cancer from normal prostate tissue in the peripheral zone using a practical reduced field-of-view MRI sequence requiring less than 3 minutes of scan time.

MATERIALS AND METHODS

Thirty-six patients with biopsy-proven peripheral zone prostate cancer without prior treatment underwent routine multiparametric MRI at 3.0T with an endorectal coil. An Inner-Volume Carr-Purcell-Meiboom-Gill imaging sequence that required 2.8 minutes to obtain data for quantitative T2 mapping covering the entire prostate gland was added to the routine multiparametric protocol. Suspected cancer (SC) and suspected healthy (SH) tissue in the peripheral zone were identified in consensus by three radiologists and were correlated with available biopsy results. Differences in mean T2 values in SC and SH regions-of-interest (ROIs) were tested for significance using unpaired Student's two-tailed t-test. The area under the receiver operating characteristic curve was used to assess the optimal threshold T2 value for cancer discrimination.

RESULTS

ROI analyses revealed significantly (p<0.0001) shorter T2 values in SC (85.4±12.3ms) compared to SH (169.6±38.7ms). An estimated T2 threshold of 99ms yielded a sensitivity of 92% and a specificity of 97% for prostate cancer discrimination.

CONCLUSIONS

Quantitative values derived from this clinically practical T2-mapping sequence allow high precision discrimination between healthy and cancerous peripheral zone in the prostate.

Investigation of the association between cerebral iron content and myelin content in normative aging using quantitative magnetic resonance neuroimaging

Myelin loss and iron accumulation are cardinal features of aging and various neurodegenerative diseases. Oligodendrocytes incorporate iron as a metabolic substrate for myelin synthesis and maintenance. An emerging hypothesis in Alzheimer’s disease research suggests that myelin breakdown releases substantial stores of iron that may accumulate, leading to further myelin breakdown and neurodegeneration. We assessed associations between iron content and myelin content in critical brain regions using quantitative magnetic resonance imaging (MRI) on a cohort of cognitively unimpaired adults ranging in age from 21 to 94 years. We measured whole-brain myelin water fraction (MWF), a surrogate of myelin content, using multicomponent relaxometry, and whole-brain iron content using susceptibility weighted imaging in all individuals. MWF was negatively associated with iron content in most brain regions evaluated indicating that lower myelin content corresponds to higher iron content. Moreover, iron content was significantly higher with advanced age in most structures, with men exhibiting a trend towards higher iron content as compared to women. Finally, relationship between MWF and age, in all brain regions investigated, suggests that brain myelination continues until middle age, followed by degeneration at older ages. This work establishes a foundation for further investigations of the etiology and sequelae of myelin breakdown and iron accumulation in neurodegeneration and may lead to new imaging markers for disease progression and treatment.

Adult brain aging investigated using BMC-mcDESPOT-based myelin water fraction imaging

The relationship between regional brain myelination and aging has been the subject of intense study, with magnetic resonance imaging perhaps the most effective modality for elucidating this. However, most of these studies have used nonspecific methods to probe myelin content, including diffusion tensor imaging, magnetization transfer ratio, and relaxation times. In the present study, we used the BMC-mcDESPOT analysis, a direct and specific method for imaging of myelin water fraction (MWF), a surrogate of myelin content. We investigated age-related differences in MWF in several brain regions in a large cohort of cognitively unimpaired participants, spanning a wide age range. Our results indicate a quadratic, inverted U-shape, relationship between MWF and age in all brain regions investigated, suggesting that myelination continues until middle age followed by decreases at older ages. We also observed that these age-related differences vary across different brain regions, as expected. Our results provide evidence for nonlinear associations between age and myelin in a large sample of well-characterized adults, using a direct myelin content imaging method.

Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation

Multicomponent driven equilibrium steady-state observation of T₁ and T₂ (mcDESPOT) aims to quantify the Myelin Water Fraction (MWF) using a two-pool microstructural model. The MWF has been used to track neurodevelopment and neurodegeneration and has been histologically correlated to myelin content. mcDESPOT has a clinically feasible acquisition time and high signal-to-noise ratio (SNR) relative to other MWF techniques. However, disagreement exists in the literature between experimental studies that show MWF maps with plausible grey matter-white matter (GM-WM) contrast and theoretical work that questions the accuracy and precision of mcDESPOT. We demonstrate that mcDESPOT parameter estimation is inaccurate and imprecise if intercompartmental exchange is included in the microstructural model, but that significant bias results if exchange is neglected. The source of apparent MWF contrast is likely due to the complex convergence behaviour of the Stochastic Region Contraction (SRC) method commonly used to fit the mcDESPOT model. mcDESPOT-derived parameter estimates are hence not directly relatable to the underlying microstructural model and are only comparable to others using similar acquisition schemes and fitting constraints.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Treatment with L-citrulline and metformin in Duchenne muscular dystrophy: study protocol for a single-centre, randomised, placebo-controlled trial

Background

Duchenne muscular dystrophy (DMD) is an X-linked recessive disease that affects 1 in 3500–6000 male births. Despite broad research aiming to improve muscle function as well as heart and brain function, sufficient therapeutic efficacy has not yet been achieved and current therapeutic management is still supportive. In a recent pilot trial, oral treatment with l-arginine and metformin showed consistent changes of muscular metabolism both in vitro and in vivo by raising NO levels and expression of mitochondrial proteins in the skeletal muscle tissue of patients with DMD. This randomised, double-blind, placebo-controlled trial aims to demonstrate the superiority of l-citrulline and metformin therapy over placebo in DMD patients with regard to the Motor Function Measure (MFM) D1 subscore (primary endpoint) as well as additional clinical and subclinical tests.

Methods/Design

A total of 40–50 ambulant patients with DMD will be recruited at the outpatient department of the University of Basel Children’s Hospital (Switzerland), as well as from the DMD patient registries of Switzerland, Germany and Austria. Patients will be randomly allocated to one of the two arms of the study and will receive either a combination of l-citrulline and metformin or placebo for 26 weeks. Co-medication with glucocorticoids is allowed. The primary endpoint is the change of the MFM D1 subscore from baseline to week 26 under l-citrulline and metformin therapy. Secondary endpoints will include the motor function measure (MFM) and its items and subscores, the 6-minute walking test, timed function tests and quantitative muscle testing. Furthermore, quantitative muscle MRI assessment to evaluate the muscle fat fraction as well as safety and biomarker laboratory analyses from blood will be included. For comparison, muscle metabolism and mitochondrial function will be analysed in 10–20 healthy age-matched male children.

Discussion

The aim of this study is to test if a 6-month treatment of a combination of l-citrulline and metformin is more effective than placebo in preventing loss of motor function and muscle degeneration in DMD. The MFM D1 subscore is used as a clinical outcome measure and a quantitative muscle MRI assessment as the surrogate outcome measure of fatty muscle degeneration.

Trial registration

ClinicalTrials.gov: NCT01995032. Registered on 20 November 2013.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1503-1) contains supplementary material, which is available to authorized users.

Automated brain tissue and myelin volumetry based on quantitative MR imaging with various in-plane resolutions

BACKGROUND AND PURPOSE

Segmented brain tissue and myelin volumes can now be automatically calculated using dedicated software (SyMRI), which is based on quantification of R₁ and R₂ relaxation rates and proton density. The aim of this study was to determine the validity of SyMRI brain tissue and myelin volumetry using various in-plane resolutions.

METHODS

We scanned 10 healthy subjects on a 1.5T MR scanner with in-plane resolutions of 0.8, 2.0 and 3.0mm. Two scans were performed for each resolution. The acquisition time was 7-min and 24-sec for 0.8mm, 3-min and 9-sec for 2.0mm and 1-min and 56-sec for 3.0mm resolutions. The volumes of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), brain parenchymal volume (BPV), intracranial volume (ICV) and myelin were compared between in-plane resolutions. Repeatability for each resolution was then analyzed.

RESULTS

No significant differences in volumes measured were found between the different in-plane resolutions, except for NoN between 0.8mm and 2.0mm and between 2.0mm and 3.0mm. The repeatability error value for the WM, GM, CSF, NoN, BPV and myelin volumes relative to ICV was 0.97%, 1.01%, 0.65%, 0.86%, 1.06% and 0.25% in 0.8mm; 1.22%, 1.36%, 0.73%, 0.37%, 1.18% and 0.35% in 2.0mm and 1.18%, 1.02%, 0.96%, 0.45%, 1.36%, and 0.28% in 3.0mm resolutions.

CONCLUSION

SyMRI brain tissue and myelin volumetry with low in-plane resolution and short acquisition times is robust and has a good repeatability so could be useful for follow-up studies.