Single-scan R2⁎ measurement with macroscopic field inhomogeneity correction

In Vivo Characterization of Magnetic Inclusions in the Subcortex From Nonexponential Transverse Relaxation Decay

According to theoretical studies, MRI signal decay due to transverse relaxation in brain tissue with magnetic inclusions (e.g., blood vessels and iron-rich cells) is expected to follow a transition from Gaussian behaviour at short echo times to exponential behaviour at longer times. The decay parameters carry information about the inclusions (e.g., size and volume fraction) and provide unique insights into brain microstructure. However, gradient-echo decays typically only capture the long-time exponential behaviour. We provide experimental evidence of nonexponential transverse relaxation decay in human subcortical grey matter from in vivo MRI data acquired at 3 T, allowing the subsequent characterization of the magnetic inclusions. Gradient-echo data were collected with short interecho spacings, minimal echo time (1.25 ms) and novel acquisition strategies to mitigate motion and cardiac-induced effects. The data were fitted using exponential and nonexponential models that describe the impact of magnetic inclusions on the MRI signal. Nonexponential models provided superior fits. The strongest deviations from exponential were detected in the substantia nigra and globus pallidus. Numerical simulations of the signal decay from histological maps of iron concentration in the substantia nigra replicated the experimental data, highlighting that non-haem iron can be at the source of the nonexponential decay. To investigate the potential of nonexponential decays to characterize brain microstructure, we estimated the properties of the underlying inclusions using two analytical models. Under the static dephasing regime, the magnetic susceptibility and volume fractions of the inclusions ranged between 1.8-4 and 0.02-0.04 ppm, respectively. Alternatively, under the diffusion narrowing regime, the typical inclusion size was ~2.4 μm. Both simulations and experimental data suggest an intermediate regime with a non-negligible effect of water diffusion. Nonexponential transverse relaxation decay allows to characterize the spatial distribution of magnetic material within subcortical tissue with increased specificity, with potential applications for Parkinson's disease and other pathologies.

Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation

Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1⁺ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm³ and 1.7×1.7×3 mm³, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1-1.04, R²>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R²>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.

Adaptive slice-specific z-shimming for 2D spoiled gradient-echo sequences

Purpose

To reduce the misbalance between compensation gradients and macroscopic field gradients, we introduce an adaptive slice‐specific z‐shimming approach for 2D spoiled multi‐echo gradient‐echoe sequences in combination with modeling of the signal decay.

Methods

Macroscopic field gradients were estimated for each slice from a fast prescan (15 seconds) and then used to calculate slice‐specific compensation moments along the echo train. The coverage of the compensated field gradients was increased by applying three positive and three negative moments. With a forward model, which considered the effect of the slice profile, the z‐shim moment, and the field gradient, R2∗ maps were estimated. The method was evaluated in phantom and in vivo measurements at 3 T and compared with a spoiled multi‐echo gradient‐echo and a global z‐shimming approach without slice‐specific compensation.

Results

The proposed method yielded higher SNR in R2∗ maps due to a broader range of compensated macroscopic field gradients compared with global z‐shimming. In global white matter, the mean interquartile range, proxy for SNR, could be decreased to 3.06 s⁻¹ with the proposed approach, compared with 3.37 s⁻¹ for global z‐shimming and 3.52 s⁻¹ for uncompensated multi‐echo gradient‐echo.

Conclusion

Adaptive slice‐specific compensation gradients between echoes substantially improved the SNR of R2∗ maps, and the signal could also be rephased in anatomical areas, where it has already been completely dephased.

Mechanisms of T2 * anisotropy and gradient echo myelin water imaging

In MRI, structurally aligned molecular or micro-organization (e.g. axonal fibers) can be a source of substantial signal variations that depend on the structural orientation and the applied magnetic field. This signal anisotropy gives us a unique opportunity to explore information that exists at a resolution several orders of magnitude smaller than that of typical MRI. In this review, one of the signal anisotropies, T₂ * anisotropy in white matter, and a related imaging method, gradient echo myelin water imaging (GRE-MWI), are explored. The T₂ * anisotropy has been attributed to isotropic and anisotropic magnetic susceptibility of myelin and compartmentalized microstructure of white matter fibers (i.e. axonal, myelin, and extracellular space). The susceptibility and microstructure create magnetic frequency shifts that change with the relative orientation of the fiber and the main magnetic field, generating the T₂ * anisotropy. The resulting multi-component magnitude decay and nonlinear phase evolution have been utilized for GRE-MWI, assisting in resolving the signal fraction of the multiple compartments in white matter. The GRE-MWI method has been further improved by signal compensation techniques including physiological noise compensation schemes. The T₂ * anisotropy and GRE-MWI provide microstructural information on a voxel (e.g. fiber orientation and tissue composition), and may serve as sensitive biomarkers for microstructural changes in the brain. Copyright © 2016 John Wiley & Sons, Ltd.

The γ-parameter of anomalous diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences

Motivated by previous results obtained in vitro, we investigated the dependence of the anomalous diffusion (AD) MRI technique on local magnetic susceptibility differences (Δχ) driven by magnetic field inhomogeneity in human brains. The AD-imaging contrast investigated here is quantified by the stretched-exponential parameter γ, extracted from diffusion weighted (DW) data collected by varying diffusion gradient strengths. We performed T₂* and DW experiments in eight healthy subjects at 3.0T. T₂*-weighted images at different TEs=(10,20,35,55)ms and DW-EPI images with fourteen b-values from 0 to 5000s/mm² were acquired. AD-metrics and Diffusion Tensor Imaging (DTI) parameters were compared and correlated to R₂* and to Δχ values taken from literature for the gray (GM) and the white (WM) matter. Pearson's correlation test and Analysis of Variance with Bonferroni post-hoc test were used. Significant strong linear correlations were found between AD γ-metrics and R₂* in both GM and WM of the human brain, but not between DTI-metrics and R₂*. Depending on Δχ driven magnetic field inhomogeneity, the new contrast provided by AD-γ imaging reflects Δχ due to differences in myelin orientation and iron content within selected regions in the WM and GM, respectively. This feature of the AD-γ imaging due to the fact that γ is quantified by using MRI, may be an alternative strategy to investigate, at high magnetic fields, microstructural changes in myelin, and alterations due to iron accumulation. Possible clinical applications might be in the field of neurodegenerative diseases.

Improved estimation of myelin water fraction using complex model fitting

In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.

Improved susceptibility weighted imaging method using multi-echo acquisition

PURPOSE

To introduce novel acquisition and postprocessing approaches for susceptibility weighted imaging (SWI) to remove background field inhomogeneity artifacts in both magnitude and phase data.

METHODS

The proposed method acquires three echoes in a three-dimensional gradient echo (GRE) sequence, with a field compensation gradient (z-shim gradient) applied to the third echo. The artifacts in the magnitude data are compensated by signal estimation from all three echoes. The artifacts in phase signals are removed by modeling the background phase distortions using Gaussians. The method was applied in vivo and compared with conventional SWI.

RESULTS

The method successfully compensates for background field inhomogeneity artifacts in magnitude and phase images, and demonstrated improved SWI images. In particular, vessels in frontal lobe, which were not observed in conventional SWI, were identified in the proposed method.

CONCLUSION

The new method improves image quality in SWI by restoring signal in the frontal and temporal regions.