On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm

Diffusion functional MRI (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 intra-/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 times series of the apparent diffusion coefficient (ADC-fMRI) and of the perfusion signal fraction (IVIM-fMRI) were derived. ADC-fMRI provided ADC estimates sensitive to changes in perfusion and free-water volume, but not to T2/T2* values. With IVIM-fMRI we isolated the perfusion contribution to ADC, while suppressing T2 effects. Compared to conventional gradient-echo BOLD 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 ROIs. Taken together, our results suggest that the sensitivity to task of dfMRI derives from a decrease of hindered diffusion and an increase of the pseudo-diffusion signal fraction, leading to different, more confined spatial activation patterns compared to classic functional MRI.Comment: Submitted to peer-reviewed journa

Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

In diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to gray matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR≥50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue

The importance of correcting for signal drift in diffusion MRI

Purpose To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift. Methods We investigated the signal magnitude of non-diffusion-weighted EPI volumes in a series of diffusion-weighted imaging experiments to determine whether signal magnitude changes over time. Different scan protocols and scanners from multiple vendors were used to verify this on phantom data, and the effects on diffusion kurtosis tensor estimation in phantom and in vivo data were quantified. Scalar metrics (eigenvalues, fractional anisotropy, mean diffusivity, mean kurtosis) and directional information (first eigenvectors and tractography) were investigated. Results Signal drift, a global signal decrease with subsequently acquired images in the scan, was observed in phantom data on all three scanners, with varying magnitudes up to 5% in a 15-min scan. The signal drift has a noticeable effect on the estimation of diffusion parameters. All investigated quantitative parameters as well as tractography were affected by this artifactual signal decrease during the scan. Conclusion By interspersing the non-diffusion-weighted images throughout the session, the signal decrease can be estimated and compensated for before data analysis; minimizing the detrimental effects on subsequent MRI analyses. Magn Reson Med 77:285–299, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

Evaluation of interrater reliability of different muscle segmentation techniques in diffusion tensor imaging

Introduction: Muscle diffusion tensor imaging (mDTI) is a quantitative MRI technique that can provide information about muscular microstructure and integrity. Ultrasound and DTI studies have shown intramuscular differences, and therefore separation of different muscles for analysis is essential. The commonly used methods to assess DTI metrics in muscles are manual segmentation and tract-based analysis. Recently methods such as volume-based tractography have been applied to optimize muscle architecture estimation, but can also be used to assess DTI metrics. Purpose: To evaluate diffusion metrics obtained using three different methods—volume-based tractography, manual segmentation-based analysis and tract-based analysis—with respect to their interrater reliability and their ability to detect intramuscular variance. Materials and methods: 30 volunteers underwent an MRI examination in a 3 T scanner using a 16-channel Torso XL coil. Diffusion-weighted images were acquired to obtain DTI metrics. These metrics were evaluated in six thigh muscles using volume-based tractography, manual segmentation and standard tractography. All three methods were performed by two independent raters to assess interrater reliability by ICC analysis and Bland-Altman plots. Ability to assess intramuscular variance was compared using an ANOVA with muscle as a between-subjects factor. Results: Interrater reliability for all methods was found to be excellent. The highest interrater reliability was found for volume-based tractography (ICC ≥ 0.967). Significant differences for the factor muscle in all examined diffusion parameters were shown in muscles using all methods (main effect p < 0.001). Conclusions: Diffusion data can be assessed by volume tractography, standard tractography and manual segmentation with high interrater reliability. Each method produces different results for the investigated DTI parameters. Volume-based tractography was superior to conventional manual segmentation and tractography regarding interrater reliability and detection of intramuscular variance, while tract-based analysis showed the lowest coefficients of variation

Dynamic brain ADC variations over the cardiac cycle and their relation to tissue strain assessed with DENSE at high-field MRI

PURPOSE: The ADC of brain tissue slightly varies over the cardiac cycle. This variation could reflect physiology, including mixing of the interstitial fluid, relevant for brain waste clearance. However, it is known from cardiac diffusion imaging that tissue deformation by itself affects the magnitude of the MRI signal, leading to artificial ADC variations as well. This study investigates to what extent tissue deformation causes artificial ADC variations in the brain. THEORY AND METHODS: We implemented a high-field MRI sequence with stimulated echo acquisition mode that simultaneously measures brain tissue deformation and ADC. Based on the measured tissue deformation, we simulated the artificial ADC variation by combining established theoretical frameworks and compared the results with the measured ADC variation. We acquired data in 8 healthy volunteers with diffusion weighting b = 300 and b = 1000 s/mm2 . RESULTS: Apparent diffusion coefficient variation was largest in the feet-to-head direction and showed the largest deviation from the mean ADC at peak systole. Artificial ADC variation estimated from tissue deformation was 1.3 ± 0.37·10-5 mm2 /s in the feet-to-head direction for gray matter, and 0.75 ± 0.29·10-5 mm2 /s for white matter. The measured ADC variation in the feet-to-head direction was 5.6·10-5  ± 1.5·10-5 mm2 /s for gray matter and 3.2·10-5  ± 1.0·10-5 mm2 /s for white matter, which was a factor of 3.5 ± 0.82 and 3.4 ± 0.57 larger than the artificial diffusion variations. The measured diffusion variations in the right-to-left/anterior-to-posterior direction were a factor of 1.5 ± 1.0/1.7 ± 1.4 and 2.0 ± 0.91/2.5 ± 0.94 larger than the artificial diffusion variations for gray matter and white matter, respectively. CONCLUSION: Apparent diffusion coefficient variations in the brain likely largely reflect physiology

3d automated segmentation of lower leg muscles using machine learning on a heterogeneous dataset

Quantitative MRI combines non-invasive imaging techniques to reveal alterations in muscle pathophysiology. Creating muscle-specific labels manually is time consuming and requires an experienced examiner. Semi-automatic and fully automatic methods reduce segmentation time significantly. Current machine learning solutions are commonly trained on data from healthy subjects using homogeneous databases with the same image contrast. While yielding high Dice scores (DS), those solutions are not applicable to different image contrasts and acquisitions. Therefore, the aim of our study was to evaluate the feasibility of automatic segmentation of a heterogeneous database. To create a heterogeneous dataset, we pooled lower leg muscle images from different studies with different contrasts and fields-of-view, containing healthy controls and diagnosed patients with various neuromuscular diseases. A second homogenous database with uniform contrasts was created as a subset of the first database. We trained three 3D-convolutional neuronal networks (CNN) on those databases to test performance as compared to manual segmentation. All networks, training on heterogeneous data, were able to predict seven muscles with a minimum average DS of 0.75. U-Net performed best when trained on the heterogeneous dataset (DS: 0.80 ± 0.10, AHD: 0.39 ± 0.35). ResNet and DenseNet yielded higher DS, when trained on a heterogeneous dataset (both DS: 0.86), as compared to a homogeneous dataset (ResNet DS: 0.83, DenseNet DS: 0.76). In conclusion, a CNN trained on a heterogeneous dataset achieves more accurate labels for predicting a heterogeneous database of lower leg muscles than a CNN trained on a homogenous dataset. We propose that a large heterogeneous database is needed, to make automated segmentation feasible for different kinds of image acquisitions

PCA denoising and Wiener deconvolution of 31P 3D CSI data to enhance effective SNR and improve point spread function

Purpose: This study evaluates the performance of 2 processing methods, that is, principal component analysis-based denoising and Wiener deconvolution, to enhance the quality of phosphorus 3D chemical shift imaging data. Methods: Principal component analysis-based denoising increases the SNR while maintaining spectral information. Wiener deconvolution reduces the FWHM of the voxel point spread function, which is increased by Hamming filtering or Hamming-weighted acquisition. The proposed methods are evaluated using simulated and in vivo 3D phosphorus chemical shift imaging data by 1) visual inspection of the spatial signal distribution; 2) SNR calculation of the PCr peak; and 3) fitting of metabolite basis functions. Results: With the optimal order of processing steps, we show that the effective SNR of in vivo phosphorus 3D chemical shift imaging data can be increased. In simulations, we show we can preserve phosphorus-containing metabolite peaks that had an SNR < 1 before denoising. Furthermore, using Wiener deconvolution, we were able to reduce the FWHM of the voxel point spread function with only partially reintroducing Gibb-ringing artifacts while maintaining the SNR. After data processing, fitting of the phosphorus-containing metabolite signals improved. Conclusion: In this study, we have shown that principal component analysis-based denoising in combination with regularized Wiener deconvolution allows increasing the effective spectral SNR of in vivo phosphorus 3D chemical shift imaging data, with reduction of the FWHM of the voxel point spread function. Processing increased the effective SNR by at least threefold compared to Hamming weighted acquired data and minimized voxel bleeding. With these methods, fitting of metabolite amplitudes became more robust with decreased fitting residuals

High inter-rater reliability of manual segmentation and volume-based tractography in healthy and dystrophic human calf muscle

Background: Muscle diffusion tensor imaging (mDTI) is a promising surrogate biomarker in the evaluation of muscular injuries and neuromuscular diseases. Since mDTI metrics are known to vary between different muscles, separation of different muscles is essential to achieve musclespecific diffusion parameters. The commonly used technique to assess DTI metrics is parameter maps based on manual segmentation (MSB). Other techniques comprise tract-based approaches, which can be performed in a previously defined volume. This so-called volume-based tractography (VBT) may offer a more robust assessment of diffusion metrics and additional information about muscle architecture through tract properties. The purpose of this study was to assess DTI metrics of human calf muscles calculated with two segmentation techniques—MSB and VBT—regarding their inter-rater reliability in healthy and dystrophic calf muscles. Methods: 20 healthy controls and 18 individuals with different neuromuscular diseases underwent an MRI examination in a 3T scanner using a 16-channel Torso XL coil. DTI metrics were assessed in seven calf muscles using MSB and VBT. Coefficients of variation (CV) were calculated for both techniques. MSB and VBT were performed by two independent raters to assess inter-rater reliability by ICC analysis and Bland-Altman plots. Next to analysis of DTI metrics, the same assessments were also performed for tract properties extracted with VBT. Results: For both techniques, low CV were found for healthy controls (≤13%) and neuromuscular diseases (≤17%). Significant differences between methods were found for all diffusion metrics except for λ1 . High inter-rater reliability was found for both MSB and VBT (ICC ≥ 0.972). Assessment of tract properties revealed high inter-rater reliability (ICC ≥ 0.974). Conclusions: Both segmentation techniques can be used in the evaluation of DTI metrics in healthy controls and different NMD with low rater dependency and high precision but differ significantly from each other. Our findings underline that the same segmentation protocol must be used to ensure comparability of mDTI data

T2* mapping in an equine articular groove model - visualizing changes in collagen orientation

T2* mapping is promising for the evaluation of articular cartilage collagen. In this work, a groove model in a large animal is used as a model for post-traumatic arthritis. We hypothesized that T2* mapping could be employed to differentiate between healthy and (subtly) damaged cartilage. Eight carpal joints were obtained from four adult Shetland ponies that had been included in the groove study. In this model, grooves were surgically created on the proximal articular surface of the intermediate carpal bone (radiocarpal joint) and the radial facet of the third carpal bone (middle carpal joint) by either coarse disruption or sharp incision. After nine months, T2* mapping of the entire carpal joint was carried out on a 7.0T whole body magnetic resonance imaging (MRI) scanner by means of a gradient echo multi echo sequence. Afterwards, assessment of collagen orientation was carried out based on Picrosirius Red-stained histological sections, visualized by polarized light microscopy (PLM). The average T2* relaxation time in grooved samples was lower than in contralateral control sites. Opposite to the grooved areas, the "kissing sites" had a higher average T2* relaxation time than the grooved sites. PLM showed mild changes in orientation of the collagen fibers, particularly around blunt grooves. This work shows that T2* relaxation times are different in healthy cartilage versus (early) damaged cartilage, as induced by the equine groove model. Additionally, the average T2* relaxation times are different in kissing lesions versus the grooved sites. This article is protected by copyright. All rights reserved

T2* mapping in an equine articular groove model: Visualizing changes in collagen orientation

T2* mapping is promising for the evaluation of articular cartilage collagen. In this work, a groove model in a large animal is used as a model for posttraumatic arthritis. We hypothesized that T2* mapping could be employed to differentiate between healthy and (subtly) damaged cartilage. Eight carpal joints were obtained from four adult Shetland ponies that had been included in the groove study. In this model, grooves were surgically created on the proximal articular surface of the intermediate carpal bone (radiocarpal joint) and the radial facet of the third carpal bone (middle carpal joint) by either coarse disruption or sharp incision. After 9 months, T2* mapping of the entire carpal joint was carried out on a 7.0-T whole-body magnetic resonance imaging (MRI) scanner by means of a gradient echo multi-echo sequence. Afterwards, assessment of collagen orientation was carried out based on Picrosirius Red-stained histological sections, visualized by polarized light microscopy (PLM). The average T2* relaxation time in grooved samples was lower than in contralateral control sites. Opposite to the grooved areas, the "kissing sites" had a higher average T2* relaxation time than the grooved sites. PLM showed mild changes in orientation of the collagen fibers, particularly around blunt grooves. This work shows that T2* relaxation times are different in healthy cartilage vs (early) damaged cartilage, as induced by the equine groove model. Additionally, the average T2* relaxation times are different in kissing lesions vs the grooved sites