Fiber orientation-dependent white matter contrast in gradient echo MRI

The Larmor frequency shift of a white matter magnetic microstructure model with multiple sources

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to the macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense medium of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here, we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron, and so forth. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI measurements of an ex vivo mouse brain at ultra-high field.

So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging

In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.

Quantum dipole interactions and transient hydrogen bond orientation order in cells, cellular membranes and myelin sheath: Implications for MRI signal relaxation, anisotropy, and T1 magnetic field dependence

PURPOSE

Despite significant impact on the study of human brain, MRI lacks a theory of signal formation that integrates quantum interactions involving proton dipoles (a primary MRI signal source) with brain intricate cellular environment. The purpose of the present study is developing such a theory.

METHODS

We introduce the Transient Hydrogen Bond (THB) model, where THB-mediated quantum dipole interactions between water and protons of hydrophilic heads of amphipathic biomolecules forming cells, cellular membranes and myelin sheath serve as a major source of MR signal relaxation.

RESULTS

The THB theory predicts the existence of a hydrogen-bond-driven structural order of dipole-dipole connections within THBs as a primary factor for the anisotropy observed in MRI signal relaxation. We have also demonstrated that the conventional Lorentzian spectral density function decreases too fast at high frequencies to adequately capture the field dependence of brain MRI signal relaxation. To bridge this gap, we introduced a stretched spectral density function that surpasses the limitations of Lorentzian dispersion. In human brain, our findings reveal that at any time point only about 4% to 7% of water protons are engaged in quantum encounters within THBs. These ultra-short (2 to 3 ns), but frequent quantum spin exchanges lead to gradual recovery of magnetization toward thermodynamic equilibrium, that is, relaxation of MRI signal.

CONCLUSION

By incorporating quantum proton interactions involved in brain imaging, the THB approach introduces new insights on the complex relationship between brain tissue cellular structure and MRI measurements, thus offering a promising new tool for better understanding of brain microstructure in health and disease.

Quantification of the in vivo brain ultrashort-T2* component in healthy volunteers

PURPOSE

Recent work has shown MRI is able to measure and quantify signals of phospholipid membrane-bound protons associated with myelin in the human brain. This work seeks to develop an improved technique for characterizing this brain ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component in vivo accounting forT 1 $$ {\mathrm{T}}_1 $$ weighting.

METHODS

Data from ultrashort echo time scans from 16 healthy volunteers with variable flip angles (VFA) were collected and fitted into an advanced regression model to quantify signal fraction, relaxation time, and frequency shift of the ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component.

RESULTS

The fitted components show intra-subject differences of different white matter structures and significantly elevated ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ signal fraction in the corticospinal tracts measured at 0.09 versus 0.06 in other white matter structures and significantly elevated ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ frequency shift in the body of the corpus callosum at- $$ - $$ 1.5 versus- $$ - $$ 2.0 ppm in other white matter structures.

CONCLUSION

The significantly different measured components and measuredT 1 $$ {\mathrm{T}}_1 $$ relaxation time of the ultrashort-T 2 ∗ $$ {\mathrm{T}}_2\ast $$ component suggest that this method is picking up novel signals from phospholipid membrane-bound protons.

Novel pore size-controlled, susceptibility matched, 3D-printed MRI phantoms

PURPOSE

We report the design concept and fabrication of MRI phantoms, containing blocks of aligned microcapillaires that can be stacked into larger arrays to construct diameter distribution phantoms or fractured, to create a "powder-averaged" emulsion of randomly oriented blocks for vetting or calibrating advanced MRI methods, that is, diffusion tensor imaging, AxCaliber MRI, MAP-MRI, and multiple pulsed field gradient or double diffusion-encoded microstructure imaging methods. The goal was to create a susceptibility-matched microscopically anisotropic but macroscopically isotropic phantom with a ground truth diameter that could be used to vet advanced diffusion methods for diameter determination in fibrous tissues.

METHODS

Two-photon polymerization, a novel three-dimensional printing method is used to fabricate blocks of capillaries. Double diffusion encoding methods were employed and analyzed to estimate the expected MRI diameter.

RESULTS

Susceptibility-matched microcapillary blocks or modules that can be assembled into large-scale MRI phantoms have been fabricated and measured using advanced diffusion methods, resulting in microscopic anisotropy and random orientation.

CONCLUSION

This phantom can vet and calibrate various advanced MRI methods and multiple pulsed field gradient or diffusion-encoded microstructure imaging methods. We demonstrated that two double diffusion encoding methods underestimated the ground truth diameter.

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

A predictor-corrector phase unwrapping algorithm for temporally undersampled gradient-echo MRI

PURPOSE

To develop a method for unwrapping temporally undersampled and nonlinear gradient recalled echo (GRE) phase.

THEORY AND METHODS

Temporal unwrapping is performed as a sequential one step prediction of the echo phase, followed by a correction to the nearest integer wrap-count. A spatio-temporal extension of the 1D predictor corrector unwrapping (PCU) algorithm improves the prediction accuracy, and thereby maintains spatial continuity. The proposed method is evaluated using numerical phantom, physical phantom, and in vivo brain data at both 3 T and 9.4 T. The unwrapping performance is compared with the state-of-the-art temporal and spatial unwrapping algorithms, and the spatio-temporal iterative virtual-echo based Nyquist sampled (iVENyS) algorithm.

RESULTS

Simulation results showed significant reduction in unwrapping errors at higher echoes compared with the state-of-the-art algorithms. Similar to the iVENyS algorithm, the PCU algorithm was able to generate spatially smooth phase images for in vivo data acquired at 3 T and 9.4 T, bypassing the use of additional spatial unwrapping step. A key advantage over iVENyS algorithm is the superior performance of PCU algorithm at higher echoes.

CONCLUSION

PCU algorithm serves as a robust phase unwrapping method for temporally undersampled and nonlinear GRE phase, particularly in the presence of high field gradients.

Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mapping

PURPOSE

This work is to investigate the microstructure-induced frequency shift in white matter (WM) with crossing fibers and to separate the microstructure-related frequency shift from the bulk susceptibility-induced frequency shift by model fitting the gradient-echo (GRE) frequency evolution for potentially more accurate quantitative susceptibility mapping (QSM).

METHODS

A hollow-cylinder fiber model (HCFM) with two fiber populations was developed to investigate GRE frequency evolutions in WM voxels with microstructural orientation dispersion. The simulated and experimentally measured TE-dependent local frequency shift was then fitted to a simplified frequency evolution model to obtain a microstructure-related frequency difference parameter (∆ f $$ \Delta f $$ ) and a TE-independent bulk susceptibility-induced frequency shift (C f $$ {C}_f $$ ). The obtainedC f $$ {C}_f $$ was then used for QSM reconstruction. Reconstruction performances were evaluated using a numerical head phantom and in vivo data and then compared to other multi-echo combination methods.

RESULTS

GRE frequency evolutions and∆ f $$ \Delta f $$ -based tissue parameters in both parallel and crossing fibers determined from our simulations were comparable to those observed in vivo. The TE-dependent frequency fitting method outperformed other multi-echo combination methods in estimatingC f $$ {C}_f $$ in simulations. The fitted∆ f $$ \Delta f $$ ,C f $$ {C}_f $$ , and QSM could be improved further by navigator-based B0 fluctuation correction.

CONCLUSION

A HCFM with two fiber populations can be used to characterize microstructure-induced frequency shifts in WM regions with crossing fibers. HCFM-based TE-dependent frequency fitting provides tissue contrast related to microstructure (∆ f $$ \Delta f $$ ) and in addition may help improve the quantification accuracy ofC f $$ {C}_f $$ and the corresponding QSM.

Evaluation of the Blood-Brain Barrier, Demyelination, and Neurodegeneration in Paramagnetic Rim Lesions in Multiple Sclerosis on 7 Tesla MRI

BACKGROUND

Paramagnetic rim lesions (PRLs) are associated with chronic inflammation in multiple sclerosis (MS). 7-Tesla (7T) magnetic resonance imaging (MRI) can evaluate the integrity of the blood-brain barrier (BBB) in addition to the tissue myelination status and cell loss.

PURPOSE

To use MRI metrics to investigate underlying physiology and clinical importance of PRLs.

STUDY TYPE

Prospective.

SUBJECTS

Thirty-six participants (mean-age 47, 23 females, 13 males) of mixed MS subtypes.

FIELD STRENGTH/SEQUENCE

7T, MP2RAGE, MULTI-ECHO 3D-GRE, FLAIR.

ASSESSMENT

Lesion heterogeneity; longitudinal changes in lesion counts; comparison of T1, R2*, and χ; association between baseline lesion types and disease progression (2-3 annual MRI visits with additional years of annual clinical follow-up).

STATISTICAL TESTS

Two-sample t-test, Wilcoxon Rank-Sum test, Pearson's chi-square test, two-group comparison with linear-mixed-effect model, mixed-effect ANOVA, logistic regression. P-values <0.05 were considered significant.

RESULTS

A total of 58.3% of participants had at least one PRL at baseline. Higher male proportion in PRL+ group was found. Average change in PRL count was 0.20 (SD = 2.82) for PRLs and 0.00 (SD = 0.82) for mottled lesions. Mean and median pre-/post-contrast T1 were longer in PRL+ than in PRL-. No differences in mean χ were seen for lesions grouped by PRL (P = 0.310, pre-contrast; 0.086, post-contrast) or PRL/M presence (P = 0.234, pre-contrast; 0.163, post-contrast). Median χ were less negative in PRL+ and PRL/M+ than in PRL- and PRL/M-. Mean and median pre-/post-contrast R2* were slower in PRL+ compared to PRL-. Mean and median pre-/post-contrast R2* were slower in PRL/M+ than in PRL/M-. PRL presence at baseline was associated with confirmed EDSS Plus progression (OR 3.75 [1.22-7.59]) and PRL/M+ at baseline with confirmed EDSS Plus progression (OR 3.63 [1.14-7.43]).

DATA CONCLUSION

Evidence of BBB breakdown in PRLs was not seen. Quantitative metrics confirmed prior results suggesting greater demyelination, cell loss, and possibly disruption of tissue anisotropy in PRLs.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Myelin water imaging at 0.55 T using a multigradient-echo sequence

PURPOSE

To investigate the prospects of a multigradient-echo (mGRE) acquisition for in vivo myelin water imaging at 0.55 T.

METHODS

Scans were performed on the brain of four healthy volunteers at 0.55 and 3 T, using a 3D mGRE sequence. The myelin water fraction (MWF) was calculated for both field strengths using a nonnegative least squares (NNLS) algorithm, implemented in the qMRLab suite. The quality of these maps as well as single-voxel fits were compared visually for 0.55 and 3 T.

RESULTS

The obtained MWF values at 0.55 T are consistent with previously reported ones at higher field strengths. The MWF maps are a considerable improvement over the ones at 3 T. Example fits show that 0.55 T data is better described by an exponential model than 3 T data, making the assumed multi-exponential model of the NNLS algorithm more accurate.

CONCLUSION

This first assessment shows that mGRE myelin water imaging at 0.55 T is feasible and has the potential to yield better results than at higher fields.

Incorporating the effect of white matter microstructure in the estimation of magnetic susceptibility in ex vivo mouse brain

PURPOSE

To extend quantitative susceptibility mapping to account for microstructure of white matter (WM) and demonstrate its effect on ex vivo mouse brain at 16.4T.

THEORY AND METHODS

Previous studies have shown that the MRI measured Larmor frequency also depends on local magnetic microstructure at the mesoscopic scale. Here, we include effects from WM microstructure using our previous results for the mesoscopic Larmor frequencyΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ of cylinders with arbitrary orientations. We scrutinize the validity of our model and QSM in a digital brain phantom includingΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ from a WM susceptibility tensor and biologically stored iron with scalar susceptibility. We also apply susceptibility tensor imaging to the phantom and investigate how the fitted tensors are biased fromΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ . Last, we demonstrate how to combine multi-gradient echo and diffusion MRI images of ex vivo mouse brains acquired at 16.4T to estimate an apparent scalar susceptibility without sample rotations.

RESULTS

Our new model improves susceptibility estimation compared to QSM for the brain phantom. Applying susceptibility tensor imaging to the phantom withΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ from WM axons with scalar susceptibility produces a highly anisotropic susceptibility tensor that mimics results from previous susceptibility tensor imaging studies. For the ex vivo mouse brain we find theΩ ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ due to WM microstructure to be substantial, changing susceptibility in WM up to 25% root-mean-squared-difference.

CONCLUSION

Ω ‾ Meso $$ {\overline{\Omega}}^{\mathrm{Meso}} $$ impacts susceptibility estimates and biases susceptibility tensor imaging fitting substantially. Hence, it should not be neglected when imaging structurally anisotropic tissue such as brain WM.

Model resolution-based deconvolution for improved quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) utilizes the relationship between the measured local field and the unknown susceptibility map to perform dipole deconvolution. The aim of this work is to introduce and systematically evaluate the model resolution-based deconvolution for improved estimation of the susceptibility map obtained using the thresholded k-space division (TKD). A two-step approach has been proposed, wherein the first step involves the TKD susceptibility map computation and the second step involves the correction of this susceptibility map using the model-resolution matrix. The TKD-estimated susceptibility map can be expressed as the weighted average of the true susceptibility map, where the weights are determined by the rows of the model-resolution matrix, and hence a deconvolution of the TKD susceptibility map using the model-resolution matrix yields a better approximation to the true susceptibility map. The model resolution-based deconvolution is realized using closed-form, iterative, and sparsity-regularized implementations. The proposed approach was compared with L2 regularization, TKD, rescaled TKD in superfast dipole inversion, the modulated closed-form method, and iterative dipole inversion, as well as sparsity-regularized dipole inversion. It was observed that the proposed approach showed a substantial reduction in the streaking artifacts across 94 test volumes considered in this study. The proposed approach also showed better error reduction and edge preservation compared with other approaches. The proposed model resolution-based deconvolution compensates for the truncation of zero coefficients in the dipole kernel at the magic angle and hence provides a closer approximation to the true susceptibility map compared with other direct methods.

Brainstem anatomy with 7-T MRI: in vivo assessment and ex vivo comparison

BACKGROUND

The brainstem contains grey matter nuclei and white matter tracts to be identified in clinical practice. The small size and the low contrast among them make their in vivo visualisation challenging using conventional magnetic resonance imaging (MRI) sequences at high magnetic field strengths. Combining higher spatial resolution, signal- and contrast-to-noise ratio and sensitivity to magnetic susceptibility (χ), susceptibility-weighted 7-T imaging could improve the assessment of brainstem anatomy.

METHODS

We acquired high-resolution 7-T MRI of the brainstem in a 46-year-old female healthy volunteer (using a three-dimensional multi-echo gradient-recalled-echo sequence; spatial resolution 0.3 × 0.3 × 1.2 mm3) and in a brainstem sample from a 48-year-old female body donor that was sectioned and stained. Images were visually assessed; nuclei and tracts were labelled and named according to the official nomenclature.

RESULTS

This in vivo imaging revealed structures usually evaluated through light microscopy, such as the accessory olivary nuclei, oculomotor nucleus and the medial longitudinal fasciculus. Some fibre tracts, such as the medial lemniscus, were visible for most of their course. Overall, in in vivo acquisitions, χ and frequency maps performed better than T2*-weighted imaging and allowed for the evaluation of a greater number of anatomical structures. All the structures identified in vivo were confirmed by the ex vivo imaging and histology.

CONCLUSIONS

The use of multi-echo GRE sequences at 7 T allowed the visualisation of brainstem structures that are not visible in detail at conventional magnetic field and opens new perspectives in the diagnostic and therapeutical approach to brain disorders.

RELEVANCE STATEMENT

In vivo MR imaging at UHF provides detailed anatomy of CNS substructures comparable to that obtained with histology. Anatomical details are fundamentals for diagnostic purposes but also to plan a direct targeting for a minimally invasive brain stimulation or ablation.

KEY POINTS

• The in vivo brainstem anatomy was explored with ultrahigh field MRI (7 T). • In vivo T2*-weighted magnitude, χ, and frequency images revealed many brainstem structures. • Ex vivo imaging and histology confirmed all the structures identified in vivo. • χ and frequency imaging revealed more brainstem structures than magnitude imaging.

Rapid Whole-Knee Quantification of Cartilage Using T1, T2*, and TRAFF2 Mapping With Magnetic Resonance Fingerprinting

OBJECTIVE

Quantitative Magnetic Resonance Imaging (MRI) holds great promise for the early detection of cartilage deterioration. Here, a Magnetic Resonance Fingerprinting (MRF) framework is proposed for comprehensive and rapid quantification of T1, T2*, and TRAFF2 with whole-knee coverage.

METHODS

A MRF framework was developed to achieve quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame of reference ( TRAFF2) along with T1 and T2*. The proposed sequence acquires 65 measurements of 25 high-resolution slices, interleaved with 7 inversion pulses and 40 RAFF2 trains, for whole-knee quantification in a total acquisition time of 3:25 min. Comparison with reference T1, T2*, and TRAFF2 methods was performed in phantom and in seven healthy subjects at 3 T. Repeatability (test-retest) with and without repositioning was also assessed.

RESULTS

Phantom measurements resulted in good agreement between MRF and the reference with mean biases of -54, 2, and 5 ms for T1, T2*, and TRAFF2, respectively. Complete characterization of the whole-knee cartilage was achieved for all subjects, and, for the femoral and tibial compartments, a good agreement between MRF and reference measurements was obtained. Across all subjects, the proposed MRF method yielded acceptable repeatability without repositioning ( R2 ≥ 0.94) and with repositioning ( R2 ≥ 0.57) for T1, T2*, and TRAFF2.

SIGNIFICANCE

The short scan time combined with the whole-knee coverage makes the proposed MRF framework a promising candidate for the early assessment of cartilage degeneration with quantitative MRI, but further research may be warranted to improve repeatability after repositioning and assess clinical value in patients.

Empirical relationship between TEM-derived myelin volume fraction and MRI-R2 values in aging ex vivo rat corpus callosum

Ex vivo ratiometric measurements of short- and long-T2 components using the multiple spin echo sequence of MRI are often employed to evaluate alterations in myelin content in the white matter (WM) of the brain. However, the relationship between absolute MRI-T2 values (long-T2 component) and myelin volumetric information in aged ex vivo rodent WM appears to be influenced by factors such as animal species, field strength, and fixation durations/washing. Here, multiple spin echo sequence-based MRI-R2 (the reciprocal of T2) values were measured in the corpus callosum (CC) region in the post-mortem rat brains (n = 9) of different age groups with common fixation techniques without washing at 7 T. Transmission electron microscopy (TEM)-based quantification of myelin volume fraction (MVF) and corresponding Monte-Carlo simulation to estimate relaxation rates (R2,IE) due to diffusion in the presence of inhomogeneous magnetic field perturbation in intra- and extra-cellular (IE) spaces were respectively performed. To determine whether the short-T2 components originating from myelin water were mixed with long-T2 components from IE water or were undetectable, the MVF values obtained from TEM results were respectively compared with MRI-R2 and R2,IE values. A significant correlation (Pearson's correlation coefficient r = 0.8763; p < 0.01) of average MRI-R2 and MVF values was observed. Estimated R2,IE values from Monte-Carlo simulations in IE water signals were also positively correlated (r = 0.8281; p < 0.01) with MVF values. However, the magnitudes of R2,IE values were much smaller than those observed for MRI-R2 values, indicating that changes in R2 related MVF are likely dominated by myelin water components. Such comparisons between independent parameters from MRI, TEM, and simulations support the suggestion that myelin water signals were indistinguishably mixed to exhibit mono-exponential T2 relaxation, and multiple spin echo sequence-based MRI-R2 values in aging ex vivo rat CC without prolonged washing still reflect the volumetric information of myelin, likely due to enhanced water exchange across the myelin.

Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL)

The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.

Microstructure-Informed Myelin Mapping (MIMM) from Gradient Echo MRI using Stochastic Matching Pursuit

Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative susceptibility source based on the Hollow Cylindrical Fiber Model (HCFM), and iron as isotropic positive susceptibility source in the extracellular region. Voxels with a range of biophysical parameters are simulated to create a dictionary of MR echo time magnitude signals and total susceptibility values. MRI signals measured using a mGRE sequence are then matched voxel-by-voxel to the created dictionary to obtain the spatial distributions of myelin and iron. Three different MIMM versions are presented to deal with the fiber orientation dependent susceptibility effects of the myelin sheaths: a basic variation, which assumes fiber orientation is an unknown to fit, two orientation informed variations, which assume the fiber orientation distribution is available either from a separate diffusion tensor imaging (DTI) acquisition or from a DTI atlas based fiber orientation map. While all showed a significant linear correlation with the reference method based on T2-relaxometry (p < 0.0001), DTI orientation informed and atlas orientation informed variations reduced overestimation at white matter tracts compared to the basic variation. Finally, the implications and usefulness of attaining an additional iron susceptibility distribution map are discussed.

Highlights

novel stochastic matching pursuit algorithm called microstructure-informed myelin mapping (MIMM) is developed to quantify Myelin Volume Fraction (MVF) using Magnetic Resonance Imaging (MRI) and microstructural modeling.utilizes a detailed biophysical model to capture the susceptibility effects on both magnitude and phase to quantify myelin and iron.matter fiber orientation effects are considered for the improved MVF quantification in the major fiber tracts.acquired myelin and iron maps may be utilized to monitor longitudinal disease progress.

The impact of head orientation with respect to B0 on diffusion tensor MRI measures

Diffusion tensor MRI (DT-MRI) remains the most commonly used approach to characterise white matter (WM) anisotropy. However, DT estimates may be affected by tissue orientation w.r.t. B → 0 due to local gradients and intrinsic T 2 orientation dependence induced by the microstructure. This work aimed to investigate whether and how diffusion tensor MRI-derived measures depend on the orientation of the head with respect to the static magnetic field, B → 0 . By simulating WM as two compartments, we demonstrated that compartmental T 2 anisotropy can induce the dependence of diffusion tensor measures on the angle between WM fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and increased axial diffusivity were observed in white matter fibres perpendicular to B → 0 compared to those parallel to B → 0 . Fractional anisotropy varied by up to 20 % as a function of the angle between WM fibres and the orientation of the main magnetic field. To conclude, fibre orientation w.r.t. B → 0 is responsible for up to 7 % variance in diffusion tensor measures across the whole brain white matter from all subjects and head orientations. Fibre orientation w.r.t. B → 0 may introduce additional variance in clinical research studies using diffusion tensor imaging, particularly when it is difficult to control for (e.g., fetal or neonatal imaging, or when the trajectories of fibres change due to, e.g., space occupying lesions).

Fiber-orientation independent component of R2* obtained from single-orientation MRI measurements in simulations and a post-mortem human optic chiasm

The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the fibre-orientation dependence of R2* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R2,iso*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter, β1, can be biophysically related to R2,iso* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted β1 can estimate R2,iso* using meGRE with long maximum-echo time (TEmax ≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for β1 that reduced the error to 12% for ex vivo compartmental R2 values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the ex vivo compartmental R2 values but not for the in vivo values. M2 and the HCFM-based simulations failed to explain the measured R2*-orientation-dependence around the magic angle for a typical in vivo meGRE protocol (with TEmax ≈ 18 ms). In conclusion, further validation and the development of movement-robust in vivo meGRE protocols with TEmax ≈ 54 ms are required before M2 can be used to estimate R2,iso* in subjects.

High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.

DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging

Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor model. STI has the potential to provide information for both the reconstruction of white matter fiber pathways and detection of myelin changes in the brain at mm resolution or less, which would be of great value for understanding brain structure and function in healthy and diseased brain. However, the application of STI in vivo has been hindered by its cumbersome and time-consuming acquisition requirement of measuring susceptibility induced MR phase changes at multiple head orientations. Usually, sampling at more than six orientations is required to obtain sufficient information for the ill-posed STI dipole inversion. This complexity is enhanced by the limitation in head rotation angles due to physical constraints of the head coil. As a result, STI has not yet been widely applied in human studies in vivo. In this work, we tackle these issues by proposing an image reconstruction algorithm for STI that leverages data-driven priors. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that approximates the proximal operator of a regularizer function for STI. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations. Notably, promising reconstruction results are achieved by our method from only one orientation in human in vivo, and we demonstrate a potential application of this technique for estimating lesion susceptibility anisotropy in patients with multiple sclerosis.

Axon fiber orientation as the source of T1 relaxation anisotropy in white matter: A study on corpus callosum in vivo and ex vivo

PURPOSE

Recent studies indicate that T₁ in white matter (WM) is influenced by fiber orientation in B₀ . The purpose of the study was to investigate the interrelationships between axon fiber orientation in corpus callosum (CC) and T₁ relaxation time in humans in vivo as well as in rat brain ex vivo.

METHODS

Volunteers were scanned for relaxometric and diffusion MRI at 3 T and 7 T. Angular T₁ plots from WM were computed using fractional anisotropy and fiber-to-field-angle maps. T₁ and fiber-to-field angle were measured in five sections of CC to estimate the effects of inherently varying fiber orientations on T₁ within the same tracts in vivo. Ex vivo rat-brain preparation encompassing posterior CC was rotated in B₀ and T₁ , and diffusion MRI images acquired at 9.4 T. T₁ angular plots were determined at several rotation angles in B₀ .

RESULTS

Angular T₁ plots from global WM provided reference for estimated fiber orientation-linked T₁ changes within CC. In anterior midbody of CC in vivo, where small axons are dominantly present, a shift in axon orientation is accompanied by a change in T₁ , matching that estimated from WM T₁ data. In CC, where large and giant axons are numerous, the measured T₁ change is about 2-fold greater than the estimated one. Ex vivo rotation of the same midsagittal CC region of interest produced angular T₁ plots at 9.4 T, matching those observed at 7 T in vivo.

CONCLUSION

These data causally link axon fiber orientation in B₀ to the T₁ relaxation anisotropy in WM.

Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T

MR images of the effective relaxation rate R2* and magnetic susceptibility χ derived from multi-echo T2*-weighted (T2*w) MRI can provide insight into iron and myelin distributions in the brain, with the potential of providing biomarkers for neurological disorders. Quantification of R2* and χ at submillimeter resolution in the cortex in vivo has been difficult because of challenges such as head motion, limited signal to noise ratio, long scan time, and motion related magnetic field fluctuations. This work aimed to improve the robustness for quantifying intracortical R2* and χ and analyze the effects from motion, spatial resolution, and cortical orientation. T2*w data was acquired with a spatial resolution of 0.3 × 0.3 × 0.4 mm3 at 7 T and downsampled to various lower resolutions. A combined correction for motion and B0 changes was deployed using volumetric navigators. Such correction improved the T2*w image quality rated by experienced image readers and test-retest reliability of R2* and χ quantification with reduced median inter-scan differences up to 10 s-1 and 5 ppb, respectively. R2* and χ near the line of Gennari, a cortical layer high in iron and myelin, were as much as 10 s-1 and 10 ppb higher than the region at adjacent cortical depth. In addition, a significant effect due to the cortical orientation relative to the static field (B0) was observed in χ with a peak-to-peak amplitude of about 17 ppb. In retrospectively downsampled data, the capability to distinguish different cortical depth regions based on R2* or χ contrast remained up to isotropic 0.5 mm resolution. This study highlights the unique characteristics of R2* and χ along the cortical depth at submillimeter resolution and the need for motion and B0 corrections for their robust quantification in vivo.

Orientation dependent proton transverse relaxation in the human brain white matter: The magic angle effect on a cylindrical helix

PURPOSE

To overcome some limitations of previous proton orientation-dependent transverse relaxation formalisms in human brain white matter (WM) by a generalized magic angle effect function.

METHODS

A cylindrical helix model was developed embracing anisotropic rotational and translational diffusion of restricted molecules in WM, with the former characterized by an axially symmetric system. Transverse relaxation rates R₂ and R₂^∗ were divided into isotropic R₂ⁱ and anisotropic parts, R₂^a ∗ f(α,Φ - ε₀), with α denoting an open angle and ε₀ an orientation (Φ) offset from DTI-derived primary diffusivity direction. The proposed framework (Fit A) was compared to prior models without ε₀ on previously published water and methylene proton transverse relaxation rates from developing, healthy, and pathological WM at 3 T. Goodness of fit was represented by root-mean-square error (RMSE). F-test and linear correlation were used with statistical significance set to P ≤ 0.05.

RESULTS

Fit A significantly (P < 0.01) outperformed prior models as demonstrated by reduced RMSEs, e.g., 0.349 vs. 0.724 in myelin water. Fitted ε₀ was in good agreement with calculated ε₀ from directional diffusivities. Compared with those from healthy adult, the fitted R₂ⁱ, R₂^a, and α from neonates were substantially reduced but ε₀ increased, consistent with developing myelination. Significant positive (R₂ⁱ) and negative (α and R₂^a) correlations were found with aging (demyelination) in elderly.

CONCLUSION

The developed framework can better characterize orientation dependences from a wide range of proton transverse relaxation measurements in the human brain WM, thus shedding new light on myelin microstructural alterations at the molecular level.

Phase-shifted transverse relaxation orientation dependences in human brain white matter

This work aimed to demonstrate an essential phase shift ε 0 $$ {\varepsilon}_0 $$ for better quantifying R 2 $$ {R}_2 $$ and R 2 * $$ {R}_2^{\ast } $$ in human brain white matter (WM), and to further elucidate its origin related to the directional diffusivities from standard diffusion tensor imaging (DTI). ε 0 $$ {\varepsilon}_0 $$ was integrated into a proposed generalized transverse relaxation model for characterizing previously published R 2 $$ {R}_2 $$ and R 2 * $$ {R}_2^{\ast } $$ orientation dependence profiles in brain WM, and then comparisons were made with those without ε 0 $$ {\varepsilon}_0 $$ . It was theorized that anisotropic diffusivity direction ε $$ \varepsilon $$ was collinear with an axon fiber subject to all eigenvalues and eigenvectors from an apparent diffusion tensor. To corroborate the origin of ε 0 $$ {\varepsilon}_0 $$ , R 2 $$ {R}_2 $$ orientation dependences referenced by ε $$ \varepsilon $$ were compared with those referenced by the standard principal diffusivity direction Φ $$ \Phi $$ at b-values of 1000 and 2500 (s/mm² ). These R 2 $$ {R}_2 $$ orientation dependences were obtained from T 2 $$ {T}_2 $$ -weighted images (b = 0) of ultrahigh-resolution Connectome DTI datasets in the public domain. A normalized root-mean-square error ( NRMSE % $$ NRMSE\% $$ ) and an F $$ F $$ -test were used for evaluating curve-fittings, and statistical significance was considered to be a p of 0.05 or less. A phase-shifted model resulted in significantly reduced NRMSE % $$ NRMSE\% $$ compared with that without ε 0 $$ {\varepsilon}_0 $$ in quantifying various R 2 $$ {R}_2 $$ and R 2 * $$ {R}_2^{\ast } $$ profiles, both in vivo and ex vivo at multiple B 0 $$ {B}_0 $$ fields. The R 2 $$ {R}_2 $$ profiles based on Φ $$ \Phi $$ manifested a right-shifted phase ( ε 0 > 0 $$ {\varepsilon}_0>0 $$ ) at two b-values, while those based on ε $$ \varepsilon $$ became free from ε 0 $$ {\varepsilon}_0 $$ . For all phase-shifted R 2 $$ {R}_2 $$ and R 2 * $$ {R}_2^{\ast } $$ profiles, ε 0 $$ {\varepsilon}_0 $$ generally depended on the directional diffusivities by tan - 1 D ⊥ / D ∥ $$ {\tan}^{-1}\left({D}_{\perp }/{D}_{\parallel}\right) $$ , as predicted. In summary, a ubiquitous phase shift ε 0 $$ {\varepsilon}_0 $$ has been demonstrated as a prerequisite for better quantifying transverse relaxation orientation dependences in human brain WM. Furthermore, the origin of ε 0 $$ {\varepsilon}_0 $$ associated with the directional diffusivities from DTI has been elucidated. These findings could have a significant impact on interpretations of prior R 2 $$ {R}_2 $$ and R 2 * $$ {R}_2^{\ast } $$ datasets and on future research.

Larmor frequency shift from magnetized cylinders with arbitrary orientation distribution

The magnetic susceptibility of tissue can provide valuable information about its chemical composition and microstructural organization. However, the relation between the magnetic microstructure and the measurable Larmor frequency shift is understood only for a few idealized cases. Here we analyze the microstructure formed by magnetized, NMR-invisible infinite cylinders suspended in an NMR-reporting fluid. Through simulations, we scrutinize various geometries of mesoscopic Lorentz cavities and inclusions, and show that the cavity size should be approximately one order of magnitude larger than the width of the inclusions. We also analytically derive the Larmor frequency shift for a population of cylinders with arbitrary orientation dispersion and show that it is determined by the l = 2 Laplace expansion coefficients p 2 m of the cylinders' orientation distribution function. Our work underscores the need to account for microstructural organization when estimating magnetic tissue properties.

An improved asymmetric susceptibility tensor imaging model with frequency offset correction

PURPOSE

To improve susceptibility tensor imaging (STI) reconstruction using the asymmetric STI model with the correction of non-bulk-magnetic-susceptibility (NBMS) effects.

METHOD

A frequency offset term was introduced into the asymmetric STI model to account for the bias between measured MRI frequency signals and conventional susceptibility tensor models because of NBMS contributions. Experiments were conducted to compare the proposed model with conventional STI, conventional STI with the proposed frequency offset correction, and asymmetric STI on simulation, ex vivo mouse brain, and in vivo human brain data.

RESULTS

In the simulation where NBMS contributions are head rotation-invariant, the proposed method achieves the lowest errors in mean magnetic susceptibility (MMS) and magnetic susceptibility anisotropy (MSA) and is more robust to noise in the estimation of principal eigenvector (PEV). When considering the head orientation dependency of NBMS contributions, the proposed method shows advantages in estimating MSA and PEV. On the mouse and human brain data, the proposed method produces more reliable MSA maps and more consistent white matter fiber directions when referring to those from DTI than the compared STI methods.

CONCLUSION

The proposed method can reduce the effects of NBMS-related frequency shifts on the susceptibility tensors in the brain white matter. This study inspires STI reconstruction from the perspective of better modeling the sources of frequency shifts.

Multi-echo dipole inversion for magnetic susceptibility mapping

PURPOSE

Reconstructing tissue magnetic susceptibility (QSM) from MRI phase data involves solving multiple consecutive ill-posed inverse problems such as phase unwrapping, background field removal, and field-to-source inversion. Multi-echo acquisitions present an additional challenge, as the magnetization field is typically computed from the multiple phase data prior to reconstructing the susceptibility map. Processing the multiple phase data introduces errors during the field estimation, violating assumptions of the subsequent inverse problems, manifesting as streaking artifacts in the susceptibility map. To address this challenge, we propose a multi-echo field-to-source forward model that forgoes the field estimation step. Moreover, we propose a fully general underestimation correction step to recover susceptibility sources that were regularized away during the field-to-source inversion.

METHODS

The multi-echo forward model and correction step were validated on the QSM Challenge 2.0 datasets and compared to the standard single field-to-source model in in vivo human brains using different types of deconvolution algorithms.

RESULTS

On the QSM Challenge 2.0 datasets the multi-echo forward model and correction step attain state-of-the-art results on all metrics by a wide margin. Experiments in in vivo brains show that the multi-echo model is in agreement with the single field-to-source model and that the proposed forward model and correction step can be used with any available dipole inversion method.

CONCLUSION

A multi-echo field-to-source forward model forgoes the need to fit multi-echo phase data and achieves state-of-the-art results on the QSM Challenge 2.0 data. Underestimated low-frequency susceptibility distributions can be partially recovered using a correction step.

White matter microstructure and longitudinal relaxation time anisotropy in human brain at 3 and 7 T

A high degree of structural order by white matter (WM) fibre tracts creates a physicochemical environment where water relaxations are rendered anisotropic. Recently, angularly dependent longitudinal relaxation has been reported in human WM. We have characterised interrelationships between T1 relaxation and diffusion MRI microstructural indices at 3 and 7 T. Eleven volunteers consented to participate in the study. Multishell diffusion MR images were acquired with b-values of 0/1500/3000 and 0/1000/2000 s/mm² at 1.5 and 1.05 mm³ isotropic resolutions at 3 and 7 T, respectively. DTIFIT was used to compute DTI indices; the fibre-to-field angle (θ_FB ) maps were obtained using the principal eigenvector images. The orientations and volume fractions of multiple fibre populations were estimated using BedpostX in FSL, and the orientation dispersion index (ODI) was estimated using the NODDI protocol. MP2RAGE was used to acquire images for T1 maps at 1.0 and 0.9 mm³ isotropic resolutions at 3 and 7 T, respectively. At 3 T, T1 as a function of θ_FB in WM with high fractional anisotropy and one-fibre orientation volume fraction or low ODI shows a broad peak centred at 50^o , but a flat baseline at 0^o and 90^o . The broad peak amounted up to 7% of the mean T1. At 7 T, the broad peak appeared at 40^o and T1 in fibres running parallel to B0 was longer by up to 75 ms (8.3% of the mean T1) than in those perpendicular to the field. The peak at 40^o was approximately 5% of mean T1 (i.e., proportionally smaller than that at 54^o at 3 T). The data demonstrate T1 anisotropy in WM with high microstructural order at both fields. The angular patterns are indicative of the B0-dependency of T1 anisotropy. Thus myelinated WM fibres influence T1 contrast both by acting as a T1 contrast agent and rendering T1 dependent on fibre orientation with B0.

Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling

PURPOSE

The effective transverse relaxation rate (R 2 * $$ {\mathrm{R}}_2^{\ast } $$ ) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fiber orientation with respect to the main field (θ $$ \uptheta $$ ) complicate interpretation. The α- andθ $$ \uptheta $$ -dependence stem from the existence of multiple sub-voxel micro-environments (e.g., myelin and non-myelin water compartments). Ordinarily, it is challenging to quantify these sub-compartments; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a singleR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

METHODS

We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. Based on our numeric observations, we introduced a linear model that partitionsR 2 * $$ {\mathrm{R}}_2^{\ast } $$ into α-dependent and α-independent components and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence, and reproducibility empirically.

RESULTS

R 2 * $$ {\mathrm{R}}_2^{\ast } $$ increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although it suffered from marginally higher noise sensitivity.

CONCLUSION

We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

Orientation dependence of R2 relaxation in the newborn brain

In MRI the transverse relaxation rate, R₂ = 1/T₂^, shows dependence on the orientation of ordered tissue relative to the main magnetic field. In previous studies, orientation effects of R₂ relaxation in the mature brain's white matter have been found to be described by a susceptibility-based model of diffusion through local magnetic field inhomogeneities created by the diamagnetic myelin sheaths. Orientation effects in human newborn white matter have not yet been investigated. The newborn brain is known to contain very little myelin and is therefore expected to exhibit a decrease in orientation dependence driven by susceptibility-based effects. We measured R₂ orientation dependence in the white matter of human newborns. R₂ data were acquired with a 3D Gradient and Spin Echo (GRASE) sequence and fiber orientation was mapped with diffusion tensor imaging (DTI). We found orientation dependence in newborn white matter that is not consistent with the susceptibility-based model and is best described by a model of residual dipolar coupling. In the near absence of myelin in the newborn brain, these findings suggest the presence of residual dipolar coupling between rotationally restricted water molecules. This has important implications for quantitative imaging methods such as myelin water imaging, and suggests orientation dependence of R₂ as a potential marker in early brain development.

Static and dynamic BOLD fMRI components along white matter fibre tracts and their dependence on the orientation of the local diffusion tensor axis relative to the B0-field

Recent studies have reported functional MRI (fMRI) activation within cerebral white matter (WM) using blood-oxygenation-level-dependent (BOLD) contrast. Many blood vessels in WM run parallel to the fibre bundles, and other studies observed dependence of susceptibility contrast-based measures of blood volume on the local orientation of the fibre bundles relative to the magnetic field or B₀ axis. Motivated by this, we characterized the dependence of gradient-echo BOLD fMRI on fibre orientation (estimated by the local diffusion tensor) relative to the B₀ axis to test whether the alignment between bundles and vessels imparts an orientation dependence on resting-state BOLD fluctuations in the WM. We found that the baseline signal level of the T₂*-weighted data is 11% higher in voxels containing fibres parallel to B₀ than those containing perpendicular fibres, consistent with a static influence of either fibre or vessel orientation on local T₂* values. We also found that BOLD fluctuations in most bundles exhibit orientation effects expected from oxygenation changes, with larger amplitudes from voxels containing perpendicular fibres. Different magnitudes of this orientation effect were observed across the major WM bundles, with inferior fasciculus, corpus callosum and optic radiation exhibiting 14-19% higher fluctuations in voxels containing perpendicular compared to parallel fibres.

Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: a multi-orientation gradient-echo MRI dataset

Recently, deep neural networks have shown great potential for solving dipole inversion of quantitative susceptibility mapping (QSM) with improved results. However, these studies utilized their limited dataset for network training and inference, which may lead to untrustworthy conclusions. Thus, a common dataset is needed for a fair comparison between different QSM reconstruction networks. Additionally, finding an in vivo reference susceptibility map that matches acquired single-orientation phase data remains an open problem. Susceptibility tensor imaging (STI) χ₃₃ and Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) are considered reference susceptibility candidates. However, a large number of multi-orientation GRE data for both STI and COSMOS reconstruction are now unavailable for training supervised neural networks for QSM. In this study, we reported the largest multi-orientation dataset, to the best of our knowledge in the QSM research field, with a total of 144 scans from 8 healthy subjects collected using a 3D GRE sequence from the same MR scanner. In addition, the parcellation of deep gray matter is also provided for automatically extracting susceptibility values. Five recently developed deep neural networks, i.e., xQSM, QSMnet, autoQSM, LPCNN, and MoDL-QSM were performed on this dataset. This potential data source could provide a common framework and labels to test the accuracy and robustness of deep neural networks for QSM reconstruction. This dataset has the potential to provide a benchmark of reference susceptibility for the deep learning-based QSM methods. Additionally, the trained COSMOS-labeled and χ₃₃-labeled networks were tested on the pathological data to explore their potential applications. The data together with deep gray matter parcellation maps are now publicly available via an open repository at https://osf.io/yfms7/, and the raw multi-orientation GRE data were also available at https://osf.io/y6rc3/.

Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation

Quantitative susceptibility mapping (QSM) facilitates mapping of the bulk magnetic susceptibility of tissue from the phase of complex gradient echo (GRE) MRI data. QSM phase processing combined with an R2* model of magnitude of multiecho gradient echo data (R2*QSM) allows separation of dia- and para-magnetic components (e.g., myelin and iron) that contribute constructively to R2* value but destructively to the QSM value of a voxel. This R2*QSM technique is validated against quantitative histology—optical density of myelin basic protein and Perls’ iron histological stains of rim and core of 10 ex vivo multiple sclerosis lesions, as well as neighboring normal appearing white matter. We found that R2*QSM source maps are in good qualitative agreement with histology, e.g., showing increased iron concentration at the edge of the rim+ lesions and myelin loss in the lesions’ core. Furthermore, our results indicate statistically significant correlation between paramagnetic and diamagnetic tissue components estimated with R2*QSM and optical densities of Perls’ and MPB stains. These findings provide direct support for the use of R2*QSM magnetic source separation based solely on GRE complex data to characterize MS lesion composition.

Magnetic susceptibility anisotropy in normal appearing white matter in multiple sclerosis from single-orientation acquisition

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Orientation dependence of QSM is studied in a large cohort of MS patients.

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Apparent magnetic susceptibility anisotropy (MSA) obtained from single-orientation QSM.

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Apparent MSA found decreased in optic radiation (OR) of MS patients.

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Apparent MSA decreases with lesion load in OR and with disease duration in splenium.

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Negative apparent MSA observed in SLF indicates limitations of the proposed method.

Quantitative susceptibility mapping (QSM) has been successfully applied to study changes in deep grey matter nuclei as well as in lesional tissue, but its application to white matter has been complicated by the observed orientation dependence of gradient echo signal. The anisotropic susceptibility tensor is thought to be at the origin of this orientation dependence, and magnetic susceptibility anisotropy (MSA) derived from this tensor has been proposed as a marker of the state and integrity of the myelin sheath and may therefore be of particular interest for the study of demyelinating pathologies such as multiple sclerosis (MS). Reconstruction of the susceptibility tensor, however, requires repeated measurements with multiple head orientations, rendering the approach impractical for clinical applications.

In this study, we combined single-orientation QSM with fibre orientation information to assess apparent MSA in three white matter tracts, i.e., optic radiation (OR), splenium of the corpus callosum (SCC), and superior longitudinal fascicle (SLF), in two cohorts of 64 healthy controls and 89 MS patients. The apparent MSA showed a significant decrease in optic radiation in the MS cohort compared with healthy controls. It decreased in the MS cohort with increasing lesion load in OR and with disease duration in the splenium. All of this suggests demyelination in normal appearing white matter. However, the apparent MSA observed in the SLF pointed to potential systematic issues that require further exploration to realize the full potential of the presented approach. Despite the limitations of such single-orientation ROI-specific estimation, we believe that our clinically feasible approach to study degenerative changes in WM is worthy of further investigation.

Diagnostic accuracy of quantitative susceptibility mapping in multiple system atrophy: The impact of echo time and the potential of histogram analysis

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We performed histogram analysis on χ maps at different TEs on MSA patients and HC.

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We found altered χ distribution in Pu, SN, GP, CN for MSAp and in SN, DN for MSAc.

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QSM diagnostic accuracy is TE-dependent and is enhanced at short TEs.

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Short TEs capture rapidly-decaying contributions of high χ sources.

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Histogram features detect χ spatial heterogeneities improving diagnostic accuracy.

The non-invasive quantification of iron stores via Quantitative Susceptibility Mapping (QSM) could play an important role in the diagnosis and the differential diagnosis of atypical Parkinsonisms. However, the susceptibility (χ) values measured via QSM depend on echo time (TE). This effect relates to the microstructural organization within the voxel, whose composition can be altered by the disease. Moreover, pathological iron deposition in a brain area may not be spatially uniform, and conventional Region of Interest (ROI)-based analysis may fail in detecting alterations. Therefore, in this work we evaluated the impact of echo time on the diagnostic accuracy of QSM on a population of patients with Multiple System Atrophy (MSA) of either Parkinsonian (MSAp) or cerebellar (MSAc) phenotypes. In addition, we tested the potential of histogram analysis to improve QSM classification accuracy.

We enrolled 32 patients (19 MSAp and 13 MSAc) and 16 healthy controls, who underwent a 7T MRI session including a gradient-recalled multi-echo sequence for χ mapping. Nine histogram features were extracted from the χ maps computed for each TE in atlas-based ROIs covering deep brain nuclei, and compared among groups.

Alterations of susceptibility distribution were found in the Putamen, Substantia Nigra, Globus Pallidus and Caudate Nucleus for MSAp and in the Substantia Nigra and Dentate Nucleus for MSAc. Increased iron deposition was observed in a larger number of ROIs for the two shortest TEs and the standard deviation, the 75^th and the 90^th percentile were the most informative features yielding excellent diagnostic accuracy with area under the ROC curve > 0.9.

In conclusion, short TEs may enhance QSM diagnostic performances, as they can capture variations in rapidly-decaying contributions of high χ sources. The analysis of histogram features allowed to reveal fine heterogeneities in the spatial distribution of susceptibility alteration, otherwise undetected by a simple evaluation of ROI χ mean values.

Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R1 and R2 * in healthy young adults

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R₁), and effective transverse relaxation rate (R₂*). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R₁, and R₂* differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R₁ and R₂* showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R₁ and R₂* might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.

HFP-QSMGAN: QSM from homodyne-filtered phase images

PURPOSE

Homodyne filtering is a standard preprocessing step in the estimation of SWI. Unfortunately, SWI is not quantitative, and QSM cannot be accurately estimated from filtered phase images. Compared with gradient-echo sequences suitable for computing QSM, SWI is more readily available and is often the only susceptibility-sensitive sequence acquired in the clinical setting. In this project, we aimed to quantify susceptibility from the homodyne-filtered phase (HFP), acquired for computing susceptibility-weighted images, using convolutional neural networks to solve the compounded problem of (1) computing the solution to the inverse dipole problem, and (2) compensating for the effects of the homodyne filtering.

METHODS

Two convolutional neural networks, the U-Net and a modified QSMGAN architecture (HFP-QSMGAN), were trained to predict QSM maps at different TEs from HFP images. The QSM maps were quantified from a gradient-echo sequence acquired in the same individuals using total generalized variation (TGV)-QSM. The QSM maps estimated directly from the HFP were also included for comparison. Voxel-wise predictions and, importantly, regional predictions of susceptibility with adjustment to a reference region, were compared.

RESULTS

Our results indicate that the U-Net model provides more accurate voxel-wise predictions of susceptibility compared with HFP-QSMGAN and HFP-QSM. However, regional estimates of susceptibility predicted by HFP-QSMGAN are more strongly correlated with the values from TGV-QSM compared with those of U-Net and HFP-QSM.

CONCLUSION

Accurate prediction of susceptibility can be achieved from filtered SWI phase using convolutional neural networks.

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities

Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

Imaging white matter microstructure with gradient-echo phase imaging: Is ex vivo imaging with formalin-fixed tissue a good approximation of the in vivo brain?

Purpose

Ex vivo imaging is a commonly used approach to investigate the biophysical mechanism of orientation‐dependent signal phase evolution in white matter. Yet, how phase measurements are influenced by the structural alteration in the tissue after formalin fixation is not fully understood. Here, we study the effects on magnetic susceptibility, microstructural compartmentalization, and chemical exchange measurement with a postmortem formalin‐fixed whole‐brain human tissue.

Methods

A formalin‐fixed, postmortem human brain specimen was scanned with multiple orientations to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. White matter samples were subsequently excised from the whole‐brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure‐related contributions in the signal phase and to validate the findings of the whole‐brain data.

Results

The bulk isotropic magnetic susceptibility of ex vivo whole‐brain imaging is comparable to in vivo imaging, with noticeable enhanced nonsusceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalization phase effect were considerably reduced in the formalin‐fixed white matter specimens.

Conclusions

Formalin‐fixed postmortem white matter exhibits comparable isotropic magnetic susceptibility to previous in vivo imaging findings. However, the measured phase and magnitude data of the fixed white matter tissue shows a significantly weaker orientation dependency and compartmentalization effect. Alternatives to formalin fixation are needed to better reproduce the in vivo microstructural effects in postmortem samples.

Improved magnetic resonance myelin water imaging using multi-channel denoising convolutional neural networks (MCDnCNN)

BACKGROUND

Myelin water imaging (MWI) is powerful and important for studying and diagnosing neurological and psychiatric diseases. In particular, myelin water fraction (MWF) is derived from MWI data for quantifying myelination. However, MWF estimation is typically sensitive to noise. Improving the accuracy of MWF estimation based on WMI data acquired using a magnetic resonance (MR) multiple gradient recalled echo (mGRE) imaging sequence is desired.

METHODS

The proposed method employs a recently introduced the multi-channel denoising convolutional neural networks (MCDnCNN). Five different MCDnCNN models, denoted as Delevel1, Delevel2, Delevel3, Delevel4 and DelevelMix corresponding to five noise levels (Level1, Level2, Level3, Level4 and LevelMix), were trained using the data of the first echo of the mGRE brain images acquired from 15 healthy human subjects. Using simulated noisy data that employed a hollow cylinder model, we first evaluated the improvement in estimating MWF based on data denoised by the five different MCDnCNNs, by comparing the MWF maps calculated from the denoised data with ground truth. Next, we again evaluated the improvement using real-world in vivo datasets of 11 human participants acquired using the mGRE sequence. The datasets were first denoised by five different MCDnCNNs (Delevel1, 2, 3, 4 and DelevelMix), and subsequently their MWF maps were calculated and compared with the MWF maps directly calculated from the raw mGRE images without being denoised.

RESULTS

Experiments using the simulation data denoised by the appropriate MCDnCNN models showed that the standard deviation (SD) of the absolute error (AE) of the derived MWF results was significantly reduced (maximal reduction =15.5%, Level3 simulated noisy data, orientation angle =0, all the five MCDnCNN models). In the test using in vivo data, estimating MWF based on data particularly denoised by the appropriate MCDnCNN models was found to be the best, compared to otherwise not using the appropriate models. The results demonstrated that the appropriate MCDnCNN models may permit high-quality MWF mapping, i.e., substantial reduction of random variation in estimating MWF-maps while preserving accuracy and structural details.

CONCLUSIONS

Appropriate MCDnCNN models as proposed may improve both the accuracy and precision in estimating MWF maps, thereby making it a more clinically feasible alternative.

Quantitative susceptibility mapping and R1 measurement: Determination of the myelin volume fraction in the aging ex vivo rat corpus callosum

In studies of the white matter (WM) in aging brains, both quantitative susceptibility mapping (QSM) and direct R₁ measurement offer potentially useful ex vivo MRI tools that allow volumetric characterization of myelin content changes. Despite the technical importance of such MRI methods in numerous age-related diseases, the supposed linear relationship between the estimates of either the QSM or R₁ method and age-affected myelin contents has not been validated. In this study, the absolute myelin volume fraction (MVF) was determined by transmission electron microscopy (TEM) as a gold standard measure for comparison with the values obtained by the aforementioned MR methods. To theoretically evaluate and understand the MR signal characteristics, QSM simulations were performed using the finite perturber method (FPM). Specifically, the simulation geometry modeling was based on TEM-derived structures aligned orthogonally to the main magnetic field, the construct of which was used to estimate the magnetic field shift (ΔB) changes arising from the conjectured myelin structures. Experimentally, ex vivo corpus callosum (CC) samples from rat brains obtained at 6 weeks (n = 3), 4 months (n = 3), and 20 months (n = 3) after birth were used to establish the relationship between changes quantified by either QSM or R₁ with the absolute MVF by TEM. From the ex vivo brain samples, the scatterplot of mean MVF versus R₁ was fitted to a linear equation, where R_1mean  = 0.7948 × MVF_mean  + 0.8118 (Pearson's correlation coefficient r = 0.9138; p < 0.01), while the scatterplot of mean MVF versus MRI-derived magnetic susceptibility (χ) was also fitted to a line where χ_{measured,mean}  = -0.1218 × MVF_mean  - 0.006345 (r = -0.8435; p < 0.01). As a result of the FPM-based QSM simulations, a linearly proportional relationship between the simulated magnetic susceptibility, χ_{simulated,mean} , and MVF (r = -0.9648; p < 0.01) was established. Such a statistically significant linear correlation between MRI-derived values by the QSM (or R₁ ) method and MVF demonstrated that variable myelin contents in the WM (i.e., CC) can be quantified across multiple stages of aging. These findings further support that both techniques based on QSM and R₁ provide an efficient means of studying the brain-aging process with accurate volumetric quantification of the myelin content in WM.

Learn Less, Infer More: Learning in the Fourier Domain for Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) aims to evaluate the distribution of magnetic susceptibility from magnetic resonance phase measurements by solving the ill-conditioned dipole inversion problem. Removing the artifacts and preserving the anisotropy of tissue susceptibility simultaneously is still a challenge in QSM. To deal with this issue, a novel k-QSM network is proposed to resolve dipole inversion issues in QSM reconstruction. The k-QSM network converts the results obtained by truncated k-space division (TKD) into the Fourier domain as inputs. After passing through several convolutional and residual blocks, the ill-posed signals of TKD are corrected by making the network output close to the calculation of susceptibility through multiple orientation sampling (COSMOS)-labeled QSM. To evaluate the superiority of k-QSM, comparisons with several state-of-the-art methods are performed in terms of QSM artifacts removing, anisotropy preserving, generalization ability, and clinical applications. Compared to existing methods, the k-QSM achieves a 22.31% lower normalized root mean square error, 10.30% higher peak signal-to-noise ratio (PSNR), 33.10% lower high-frequency error norm, and 1.06% higher structural similarity. In addition, the orientation-dependent susceptibility variation obtained by k-QSM is significant, verifying that k-QSM has the ability to preserve susceptibility anisotropy. When the trained models are tested on the dataset from different centers, our k-QSM shows a strong generalization ability with the highest PSNR. Moreover, by comparing the susceptibility maps between healthy controls and drug addicts with different methods, we found the proposed k-QSM is more sensitive to the susceptibility abnormality in the patients. The proposed k-QSM method learns less—only to fix the ill-posed signals of TKD, but infers more—both COSMOS-like and anisotropy-preserving QSM results. Its generalization ability and great sensitivity to susceptibility changes can make it a potential method for distinguishing some diseases.

Improved Multi-Echo Gradient-Echo-Based Myelin Water Fraction Mapping Using Dimensionality Reduction

Multi-echo gradient-echo (mGRE)-based myelin water fraction (MWF) mapping is a promising myelin water imaging (MWI) modality but is vulnerable to noise and artifact corruption. The linear dimensionality reduction (LDR) method has recently shown improvements with regard to these challenges. However, the magnitude value based low rank operators have been shown to misestimate the MWF for regions with [Formula: see text] anisotropy. This paper presents a nonlinear dimensionality reduction (NLDR) method to estimate the MWF map better by encouraging nonlinear low dimensionality of mGRE signal sources. Specifically, we implemented a fully connected deep autoencoder to extract the low-dimensional features of complex-valued signals and incorporated a sparse regularization to separate the anomaly sources that do not reside in the low-dimensional manifold. Simulations and in vivo experiments were performed to evaluate the accuracy of the MWF map under various situations. The proposed NLDR-based MWF improves the accuracy of the MWF map over the conventional nonlinear least-squares method and the LDR-based MWF and maintains robustness against noise and artifact corruption.

Global cerebrospinal fluid as a zero-reference regularization for brain quantitative susceptibility mapping

BACKGROUND AND PURPOSE

The objective ofthis study was to demonstrate a global cerebrospinal fluid (CSF) method for a consistent and automated zero referencing of brain quantitative susceptibility mapping (QSM).

METHODS

Whole brain CSF mask was automatically segmented by thresholding the gradient echo transverse relaxation ( R2∗) map, and regularization was employed to enforce uniform susceptibility distribution within the CSF volume in the field-to-susceptibility inversion. This global CSF regularization method was compared with a prior ventricular CSF regularization. Both reconstruction methods were compared in a repeatability study of 12 healthy subjects using t-test on susceptibility measurements, and in patient studies of 17 multiple sclerosis (MS) and 10 Parkinson's disease (PD) patients using Wilcoxon rank-sum test on radiological scores.

RESULTS

In scan-rescan experiments, global CSF regularization provided more consistent CSF volume as well as higher repeatability of QSM measurements than ventricular CSF regularization with a smaller bias: -2.7 parts per billion (ppb) versus -0.13 ppb (t-test p<0.05) and a narrower 95% limits of agreement: [-7.25, 6.99] ppb versus [-16.60, 11.19 ppb] (f-test p<0.05). In PD and MS patients, global CSF regularization reduced smoothly varying shadow artifacts and significantly improved the QSM quality score (p<0.001).

CONCLUSIONS

The proposed whole brain CSF method for QSM zero referencing improves repeatability and image quality of brain QSM compared to the ventricular CSF method.

In vivo 3D Reconstruction of the Human Pallidothalamic and Nigrothalamic Pathways With Super-Resolution 7T MR Track Density Imaging and Fiber Tractography

The output network of the basal ganglia plays an important role in motor, associative, and limbic processing and is generally characterized by the pallidothalamic and nigrothalamic pathways. However, these connections in the human brain remain difficult to elucidate because of the resolution limit of current neuroimaging techniques. The present study aimed to investigate the mesoscopic nature of these connections between the thalamus, substantia nigra pars reticulata, and globus pallidus internal segment using 7 Tesla (7T) magnetic resonance imaging (MRI). In this study, track-density imaging (TDI) of the whole human brain was employed to overcome the limitations of observing the pallidothalamic and nigrothalamic tracts. Owing to the super-resolution of the TD images, the substructures of the SN, as well as the associated tracts, were identified. This study demonstrates that 7T MRI and MR tractography can be used to visualize anatomical details, as well as 3D reconstruction, of the output projections of the basal ganglia.

Pure balanced steady-state free precession imaging (pure bSSFP)

Purpose

To show that for tissues the conspicuous asymmetries in the frequency response function of bSSFP can be mitigated by using a short enough TR.

Theory and Methods

Configuration theory indicates that bSSFP becomes apparently “pure” (i.e., exhibiting a symmetric profile) in the limit of TR →0. To this end, the frequency profile of bSSFP was measured as a function of the TR using a manganese‐doped aqueous probe, as well as brain tissue that was shown to exhibit a pronounced asymmetry due to its microstructure. The frequency response function was sampled using N=72 (phantom) and N=36 (in vivo) equally distributed linear RF phase increments in the interval [0,2π). Imaging was performed with 2.0 mm isotropic resolution over a TR range of 1.5–8 ms at 3 and 1.5 T.

Results

As expected, pure substances showed a symmetric TR‐independent frequency profile, whereas brain tissue revealed a pronounced asymmetry. The observed asymmetry for the tissue, however, decreases with decreasing TR and gives strong evidence that the frequency response function of bSSFP becomes symmetric in the limit of TR →0, in agreement with theory. The limit of apparently pure bSSFP imaging can thus be achieved for a TR ∼ 1.5 ms at 1.5 T, whereas at 3 T, tissues still show some residual asymmetry.

Conclusion

In the limit of short enough TR, tissues become apparently pure for bSSFP. This limit can be reached for brain tissue at 1.5 T with TR ∼ 1–2 ms at clinically relevant resolutions.

Quantifying myelin in crossing fibers using diffusion-prepared phase imaging: Theory and simulations

PURPOSE

Myelin has long been the target of neuroimaging research. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fiber pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibers would provide a more specific marker of myelination.

THEORY AND METHODS

One MRI signal property that is sensitive to myelin is the phase accumulation. This sensitivity is used by measuring the phase accumulation of the signal remaining after diffusion-weighting, which is called diffusion-prepared phase imaging (DIPPI). Including diffusion-weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) It increases the relative contribution of intra-axonal water, whose phase is related linearly to the thickness of the surrounding myelin (in particular the log g-ratio); and (2) it gives directional information, which can be used to distinguish between crossing fibers. Here the DIPPI sequence is described, an approach is proposed to estimate the log g-ratio, and simulations are used and DIPPI data acquired in an isotropic phantom to quantify other sources of phase accumulation.

RESULTS

The expected bias is estimated in the log g-ratio for reasonable in vivo acquisition parameters caused by eddy currents (~4%-10%), remaining extra-axonal signal (~15%), and gradients in the bulk off-resonance field (<10% for most of the brain).

CONCLUSION

This new sequence may provide a g-ratio estimate per fiber population crossing within a voxel.