Background:
Diffusion-weighted 1H magnetic resonance spectroscopy (DW-MRS) allows to quantify creatine-phosphocreatine brain diffusivity (ADC(tCr)), whose reduction in multiple sclerosis (MS) has ...been proposed as a proxy of energy dysfunction.
Objective:
To investigate whether thalamic ADC(tCr) changes are associated with thalamo-cortical tract damage in MS.
Methods:
Twenty patients with MS and 13 healthy controls (HC) were enrolled in a DW-MRS and DW imaging (DWI) study. From DW-MRS, ADC(tCr) and total N-acetyl-aspartate diffusivity (ADC(tNAA)) were extracted in the thalami. Three thalamo-cortical tracts and one non-thalamic control tract were reconstructed from DWI. Fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD), reflecting microstructural integrity, were extracted for each tract. Associations between thalamic ADC(tCr) and tract metrics were assessed using linear regression models adjusting for age, sex, thalamic volume, thalamic ADC(tNAA), and tract-specific lesion load.
Results:
Lower thalamic ADC(tCr) was associated with higher MD and RD of thalamo-cortical projections in MS (MD: p = 0.029; RD: p = 0.017), but not in HC (MD: p = 0.625, interaction term between thalamic ADC(tCr) and group = 0.019; RD: p = 0.320, interaction term = 0.05). Thalamic ADC(tCr) was not associated with microstructural changes of the control tract.
Conclusion:
Reduced thalamic ADC(tCr) correlates with thalamo-cortical tract damage in MS, showing that pathologic changes in thalamic energy metabolism are associated with structural degeneration of connected fibers.
•Cardiac-induced 3D brain tissue strain tensor measured using MRI.•Single-shot simultaneous multi-slice DENSE MRI is suitable to acquire tissue strain for voxel-wise assessment.•Strain tensor field ...is consistent over healthy subjects and has good repeatability.•Brain tissue shows the Poisson effect, where longitudinal expansion is accompanied by transverse compression.
The cardiac cycle induces blood volume pulsations in the cerebral microvasculature that cause subtle deformation of the surrounding tissue. These tissue deformations are highly relevant as a potential source of information on the brain's microvasculature as well as of tissue condition. Besides, cyclic brain tissue deformations may be a driving force in clearance of brain waste products. We have developed a high-field magnetic resonance imaging (MRI) technique to capture these tissue deformations with full brain coverage and sufficient signal-to-noise to derive the cardiac-induced strain tensor on a voxel by voxel basis, that could not be assessed non-invasively before. We acquired the strain tensor with 3 mm isotropic resolution in 9 subjects with repeated measurements for 8 subjects. The strain tensor yielded both positive and negative eigenvalues (principle strains), reflecting the Poison effect in tissue. The principle strain associated with expansion followed the known funnel shaped brain motion pattern pointing towards the foramen magnum. Furthermore, we evaluate two scalar quantities from the strain tensor: the volumetric strain and octahedral shear strain. These quantities showed consistent patterns between subjects, and yielded repeatable results: the peak systolic volumetric strain (relative to end-diastolic strain) was 4.19⋅10−4 ± 0.78⋅10−4 and 3.98⋅10−4 ± 0.44⋅10−4 (mean ± standard deviation for first and second measurement, respectively), and the peak octahedral shear strain was 2.16⋅10−3 ± 0.31⋅10−3 and 2.31⋅10−3 ± 0.38⋅10−3, for the first and second measurement, respectively. The volumetric strain was typically highest in the cortex and lowest in the periventricular white matter, while anisotropy was highest in the subcortical white matter and basal ganglia. This technique thus reveals new, regional information on the brain's cardiac-induced deformation characteristics, and has the potential to advance our understanding of the role of microvascular pulsations in health and disease.
Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the ...in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.
Purpose
Aim of this study is to compare Quantitative Magnetic Resonance Imaging (qMRI) measures between Becker Muscular Dystrophy (BMD) and Healthy Subjects (HS) and to correlate these parameters ...with clinical scores.
Methods
Ten BMD patients (mean age ±standard deviation: 38.7 ± 15.0 years) and ten age-matched HS, were investigated through magnetic resonance imaging (MRI) at thigh and calf levels, including: 1) a standard axial T1-weighted sequence; 2) a volumetric T2-weighted sequence; 3) a multiecho spin-echo sequence; 4) a 2-point Dixon sequence; 5) a Diffusion Tensor Imaging (DTI) sequence.
Results
Mean Fat Fraction (FF), T2-relaxation time and Fractional Anisotropy (FA) DTI at thigh and calf levels were significantly higher in BMD patients than in HS (
p
-values < 0.01). FF at thigh and calf levels significantly correlated with North Star Ambulatory Assessment (NSAA) score (
p
-values < 0.01) and6 Minutes Walking Test (6MWT) (
p
-values < 0.01), whereas only calf muscle FF was significantly associated with time to get up from floor (
p
-value = 0.01). T2 significantly correlated with NSAA score (
p
-value < 0.01), 6MWT (
p
-value = 0.02) and time to get up from floor (
p
-value < 0.01) only at calf level. Among DTI values, only FA in thigh and calf muscles significantly correlated with NSAA score, 6MWT and 10-m walk (all
p
-values < 0.05); only FA in calf muscles significantly correlated with time to get up from floor (
p
= 0.01).
Conclusions
Muscle FF, T2-relaxometry and DTI, seem to be a promising biomarker to assess BMD disease severity, although further studies are needed to evaluate changes over the time.
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome ...Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
Diffusion functional magnetic resonance imaging (dfMRI) is a promising technique to map functional activations by acquiring diffusion‐weighed spin‐echo images. In previous studies, dfMRI showed ...higher spatial accuracy at activation mapping compared to classic functional MRI approaches. However, it remains unclear whether dfMRI measures result from changes in the intracellular/extracellular environment, perfusion, and/or T2 values. We designed an acquisition/quantification scheme to disentangle such effects in the motor cortex during a finger‐tapping paradigm. dfMRI was acquired at specific diffusion weightings to selectively suppress perfusion and free‐water diffusion, then time series of the apparent diffusion coefficient (ADC‐fMRI) and of intravoxel incoherent motion (IVIM) effects were derived. ADC‐fMRI provided ADC estimates sensitive to changes in perfusion and free‐water volume, but not to T2/T2* values. With IVIM modeling, we isolated the perfusion contribution to ADC, while suppressing T2 effects. Compared to conventional gradient‐echo blood oxygenation level‐dependent fMRI, activation maps obtained with dfMRI and ADC‐fMRI had smaller clusters, and the spatial overlap between the three techniques was below 50%. Increases of perfusion fractions were observed during task in both dfMRI and ADC‐fMRI activations. Perfusion effects were more prominent with ADC‐fMRI than with dfMRI but were significant in less than 25% of activation regions. IVIM modeling suggests that the sensitivity to task of dfMRI derives from a decrease of intracellular/extracellular diffusion and an increase of the pseudo‐diffusion signal fraction, leading to different, more confined spatial activation patterns compared to classic functional MRI.
This study aims to assess the integrity of white matter in various segments of the corpus callosum in Alzheimer's disease (AD) by using metrics derived from diffusion tensor imaging (DTI), diffusion ...kurtosis imaging (DKI) and white matter tract integrity model (WMTI) and compare these findings to healthy controls (HC).
The study was approved by the institutional ethics board. 12 AD patients and 12 HC formed the study population. All AD patients were recruited from a tertiary neurology memory clinic. A standardized battery of neuropsychological assessments was administered to the study participants by a trained rater. MRI scans were performed with a Philips Ingenia 3.0T scanner equipped with a 32-channel head coil. The protocol included a T1-weighted sequence, FLAIR and a dMRI acquisition. The dMRI scan included a total of 71 volumes, 8 at b = 0 s/mm
, 15 at b = 1,000 s/mm
and 48 at b = 2,000 s/mm
. Diffusion data fit was performed using DKI REKINDLE and WMTI models.
We detected changes suggesting demyelination and axonal degeneration throughout the corpus callosum of patients with AD, most prominent in the mid-anterior and mid-posterior segments of CC. Axial kurtosis was the most significantly altered metric, being reduced in AD patients in almost all segments of corpus callosum. Reduced axial kurtosis in the CC segments correlated with poor cognition scores in AD patients in the visuospatial, language and attention domains.
Early warning systems for debris flows are low cost measures for mitigating this kind of hazard. The early warning systems provide a timely alert for upcoming events in order to take protective ...measures, such as closing railways-roads, evacuating people from the threatened areas, and put rescue forces into readiness. These systems usually are sensor-based, and the alert time is the interval between the timing of the first detachment of debris flow by a sensor and its arrival into the threatened area. At the purpose of increasing the alert time, we propose an early warning system based on a model-cascade: nowcasting, hydrological- and triggering models. Nowcasting anticipates rainfall pattern that is transformed into runoff by the hydrological model. The triggering model estimates the volume of sediments that the runoff can entrain, and compares it with a critical threshold. If this is exceeded the alert is launched. The proposed early warning system is tested against the available data of the Rovina di Cancia (Northeast Italy) site.
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