Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain

Resiliency to Alzheimer's disease neuropathology can be distinguished from dementia using cortical astrogliosis imaging

Despite the presence of significant Alzheimer's disease (AD) pathology, characterized by amyloid β (Aβ) plaques and phosphorylated tau (pTau) tangles, some cognitively normal elderly individuals do not inevitably develop dementia. These findings give rise to the notion of cognitive 'resilience', suggesting maintained cognitive function despite the presence of AD neuropathology, highlighting the influence of factors beyond classical pathology. Cortical astroglial inflammation, a ubiquitous feature of symptomatic AD, shows a strong correlation with cognitive impairment severity, potentially contributing to the diversity of clinical presentations. However, noninvasively imaging neuroinflammation, particularly astrogliosis, using MRI remains a significant challenge. Here we sought to address this challenge and to leverage multidimensional (MD) MRI, a powerful approach that combines relaxation with diffusion MR contrasts, to map cortical astrogliosis in the human brain by accessing sub-voxel information. Our goal was to test whether MD-MRI can map astroglial pathology in the cerebral cortex, and if so, whether it can distinguish cognitive resiliency from dementia in the presence of hallmark AD neuropathological changes. We adopted a multimodal approach by integrating histological and MRI analyses using human postmortem brain samples. Ex vivo cerebral cortical tissue specimens derived from three groups comprised of non-demented individuals with significant AD pathology postmortem, individuals with both AD pathology and dementia, and non-demented individuals with minimal AD pathology postmortem as controls, underwent MRI at 7 T. We acquired and processed MD-MRI, diffusion tensor, and quantitative T 1 and T 2 MRI data, followed by histopathological processing on slices from the same tissue. By carefully co-registering MRI and microscopy data, we performed quantitative multimodal analyses, leveraging targeted immunostaining to assess MD-MRI sensitivity and specificity towards Aβ, pTau, and glial fibrillary acidic protein (GFAP), a marker for astrogliosis. Our findings reveal a distinct MD-MRI signature of cortical astrogliosis, enabling the creation of predictive maps for cognitive resilience amid AD neuropathological changes. Multiple linear regression linked histological values to MRI changes, revealing that the MD-MRI cortical astrogliosis biomarker was significantly associated with GFAP burden (standardized β=0.658, pFDR<0.0001), but not with Aβ (standardized β=0.009, p FDR =0.913) or pTau (standardized β=-0.196, p FDR =0.051). Conversely, none of the conventional MRI parameters showed significant associations with GFAP burden in the cortex. While the extent to which pathological glial activation contributes to neuronal damage and cognitive impairment in AD is uncertain, developing a noninvasive imaging method to see its affects holds promise from a mechanistic perspective and as a potential predictor of cognitive outcomes.

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.

Mapping the individual human cortex using multidimensional MRI and unsupervised learning

Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion-relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion-relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion-relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different levels of white matter damage in rodent optic nerves. 2) We obtained ex vivo DW-MRI data with b-tensor encoding schemes and calculated quantitative metrics using Q-space trajectory imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI data analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.

COnstrained Reference frame diffusion TEnsor Correlation Spectroscopic (CORTECS) MRI: A practical framework for high-resolution diffusion tensor distribution imaging

High-resolution imaging studies have consistently shown that in cortical tissue water diffuses preferentially along radial and tangential orientations with respect to the cortical surface, in agreement with histology. These dominant orientations do not change significantly even if the relative contributions from microscopic water pools to the net voxel signal vary across experiments that use different diffusion times, b-values, TEs, and TRs. With this in mind, we propose a practical new framework for imaging non-parametric diffusion tensor distributions (DTDs) by constraining the microscopic diffusion tensors of the DTD to be diagonalized using the same orthonormal reference frame of the mesoscopic voxel. In each voxel, the constrained DTD (cDTD) is completely determined by the correlation spectrum of the microscopic principal diffusivities associated with the axes of the voxel reference frame. Consequently, all cDTDs are inherently limited to the domain of positive definite tensors and can be reconstructed efficiently using Inverse Laplace Transform methods. Moreover, the cDTD reconstruction can be performed using only data acquired efficiently with single diffusion encoding, although it also supports datasets with multiple diffusion encoding. In tissues with a well-defined architecture, such as the cortex, we can further constrain the cDTD to contain only cylindrically symmetric diffusion tensors and measure the 2D correlation spectra of principal diffusivities along the radial and tangential orientation with respect to the cortical surface. To demonstrate this framework, we perform numerical simulations and analyze high-resolution dMRI data from a fixed macaque monkey brain. We estimate 2D cDTDs in the cortex and derive, in each voxel, the marginal distributions of the microscopic principal diffusivities, the corresponding distributions of the microscopic fractional anisotropies and mean diffusivities along with their 2D correlation spectra to quantify the cDTD shape-size characteristics. Signal components corresponding to specific bands in these cDTD-derived spectra show high specificity to cortical laminar structures observed with histology. Our framework drastically simplifies the measurement of non-parametric DTDs in high-resolution datasets with mesoscopic voxel sizes much smaller than the radius of curvature of the underlying anatomy, e.g., cortical surface, and can be applied retrospectively to analyze existing diffusion MRI data from fixed cortical tissues.

Mapping astrogliosis in the individual human brain using multidimensional MRI

There are currently no non-invasive imaging methods available for astrogliosis assessment or mapping in the central nervous system despite its essential role in the response to many disease states, such as infarcts, neurodegenerative conditions, traumatic brain injury and infection. Multidimensional MRI is an increasingly employed imaging modality that maximizes the amount of encoded chemical and microstructural information by probing relaxation (T1 and T2) and diffusion mechanisms simultaneously. Here, we harness the exquisite sensitivity of this imagining modality to derive a signature of astrogliosis and disentangle it from normative brain at the individual level using machine learning. We investigated ex vivo cerebral cortical tissue specimens derived from seven subjects who sustained blast-induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathological abnormality, and from seven control brain donors. By performing a combined post-mortem radiology and histopathology correlation study we found that astrogliosis induces microstructural and chemical changes that are robustly detected with multidimensional MRI, and which can be attributed to astrogliosis because no axonal damage, demyelination or tauopathy were histologically observed in any of the cases in the study. Importantly, we showed that no one-dimensional T1, T2 or diffusion MRI measurement can disentangle the microscopic alterations caused by this neuropathology. Based on these findings, we developed a within-subject anomaly detection procedure that generates MRI-based astrogliosis biomarker maps ex vivo, which were significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition (r = 0.856, P < 0.0001; r = 0.789, P < 0.0001; r = 0.793, P < 0.0001, for diffusion-T2, diffusion-T1 and T1-T2 multidimensional data sets, respectively). Our findings elucidate the underpinning of MRI signal response from astrogliosis, and the demonstrated high spatial sensitivity and specificity in detecting reactive astrocytes at the individual level, and if reproduced in vivo, will significantly impact neuroimaging studies of injury, disease, repair and aging, in which astrogliosis has so far been an invisible process radiologically.

Nonparametric 5D D-R2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain

In vivo human diffusion MRI is by default performed using single-shot EPI with greater than 50-ms echo times and associated signal loss from transverse relaxation. The individual benefits of the current trends of increasing B₀ to boost SNR and employing more advanced signal preparation schemes to improve the specificity for selected microstructural properties eventually may be cancelled by increased relaxation rates at high B₀ and echo times with advanced encoding. Here, initial attempts to translate state-of-the-art diffusion-relaxation correlation methods from 3 T to 21.1 T are made to identify hurdles that need to be overcome to fulfill the promises of both high SNR and readily interpretable microstructural information.

Histopathological modeling of status epilepticus-induced brain damage based on in vivo diffusion tensor imaging in rats

Non-invasive magnetic resonance imaging (MRI) methods have proved useful in the diagnosis and prognosis of neurodegenerative diseases. However, the interpretation of imaging outcomes in terms of tissue pathology is still challenging. This study goes beyond the current interpretation of in vivo diffusion tensor imaging (DTI) by constructing multivariate models of quantitative tissue microstructure in status epilepticus (SE)-induced brain damage. We performed in vivo DTI and histology in rats at 79 days after SE and control animals. The analyses focused on the corpus callosum, hippocampal subfield CA3b, and layers V and VI of the parietal cortex. Comparison between control and SE rats indicated that a combination of microstructural tissue changes occurring after SE, such as cellularity, organization of myelinated axons, and/or morphology of astrocytes, affect DTI parameters. Subsequently, we constructed a multivariate regression model for explaining and predicting histological parameters based on DTI. The model revealed that DTI predicted well the organization of myelinated axons (cross-validated R = 0.876) and astrocyte processes (cross-validated R = 0.909) and possessed a predictive value for cell density (CD) (cross-validated R = 0.489). However, the morphology of astrocytes (cross-validated R &gt; 0.05) was not well predicted. The inclusion of parameters from CA3b was necessary for modeling histopathology. Moreover, the multivariate DTI model explained better histological parameters than any univariate model. In conclusion, we demonstrate that combining several analytical and statistical tools can help interpret imaging outcomes to microstructural tissue changes, opening new avenues to improve the non-invasive diagnosis and prognosis of brain tissue damage.

Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.

Accelerating joint relaxation-diffusion MRI by integrating time division multiplexing and simultaneous multi-slice (TDM-SMS) strategies

PURPOSE

To accelerate the acquisition of relaxation-diffusion imaging by integrating time-division multiplexing (TDM) with simultaneous multi-slice (SMS) for EPI and evaluate imaging quality and diffusion measures.

METHODS

The time-division multiplexing (TDM) technique and SMS method were integrated to achieve a high slice-acceleration (e.g., 6×) factor for acquiring relaxation-diffusion MRI. Two variants of the sequence, referred to as TDM3e-SMS and TDM2s-SMS, were developed to simultaneously acquire slice groups with three distinct TEs and two slice groups with the same TE, respectively. Both sequences were evaluated on a 3T scanner with in vivo human brains and compared with standard single-band (SB) -EPI and SMS-EPI using diffusion measures and tractography results.

RESULTS

Experimental results showed that the TDM3e-SMS sequence with total slice acceleration of 6 (multiplexing factor (MP) = 3 × multi-band factor (MB) = 2) provided similar image intensity and microstructure measures compared to standard SMS-EPI with MB = 2, and yielded less bias in intensity compared to standard SMS-EPI with MB = 4. The three sequences showed a similar positive correlation between TE and mean kurtosis (MK) and a negative correlation between TE and mean diffusivity (MD) in white matter. Multi-fiber tractography also shows consistency of results in TE-dependent measures between different sequences. The TDM2s-SMS sequence (MP = 2, MB = 2) also provided imaging measures similar to standard SMS-EPI sequences (MB = 2) for single-TE diffusion imaging.

CONCLUSIONS

The TDM-SMS sequence can provide additional 2× to 3× acceleration to SMS without degrading imaging quality. With the significant reduction in scan time, TDM-SMS makes joint relaxation-diffusion MRI a feasible technique in neuroimaging research to investigate new markers of brain disorders.

Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction

Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.

Nonparametric D-R1-R2 distribution MRI of the living human brain

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R₁ and D-R₂ correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R₂ or R₁ in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R₁-R₂ correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R₁-R₂ correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R₁-R₂ distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R₁-, and R₂-weightings based on the rich information contained in the D-R₁-R₂ distributions.

Multi-tissue spherical deconvolution of tensor-valued diffusion MRI

Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.

Combined diffusion-relaxometry microstructure imaging: Current status and future prospects

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

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.

Multidimensional MRI for Characterization of Subtle Axonal Injury Accelerated Using an Adaptive Nonlocal Multispectral Filter

Multidimensional MRI is an emerging approach that simultaneously encodes water relaxation (T₁ and T₂) and mobility (diffusion) and replaces voxel-averaged values with subvoxel distributions of those MR properties. While conventional (i.e., voxel-averaged) MRI methods cannot adequately quantify the microscopic heterogeneity of biological tissue, using subvoxel information allows to selectively map a specific T₁-T₂-diffusion spectral range that corresponds to a group of tissue elements. The major obstacle to the adoption of rich, multidimensional MRI protocols for diagnostic or monitoring purposes is the prolonged scan time. Our main goal in the present study is to evaluate the performance of a nonlocal estimation of multispectral magnitudes (NESMA) filter on reduced datasets to limit the total acquisition time required for reliable multidimensional MRI characterization of the brain. Here we focused and reprocessed results from a recent study that identified potential imaging biomarkers of axonal injury pathology from the joint analysis of multidimensional MRI, in particular voxelwise T₁-T₂ and diffusion-T₂ spectra in human Corpus Callosum, and histopathological data. We tested the performance of NESMA and its effect on the accuracy of the injury biomarker maps, relative to the co-registered histological reference. Noise reduction improved the accuracy of the resulting injury biomarker maps, while permitting data reduction of 35.7 and 59.6% from the full dataset for T₁-T₂ and diffusion-T₂ cases, respectively. As successful clinical proof-of-concept applications of multidimensional MRI are continuously being introduced, reliable and robust noise removal and consequent acquisition acceleration would advance the field towards clinically-feasible diagnostic multidimensional MRI protocols.

Measuring compartmental T2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T2 correlation MRI

The anisotropy of brain white matter microstructure manifests itself in orientational-dependence of various MRI contrasts, and can result in significant quantification biases if ignored. Understanding the origins of this orientation-dependence could enhance the interpretation of MRI signal changes in development, ageing and disease and ultimately improve clinical diagnosis. Using a novel experimental setup, this work studies the contributions of the intra- and extra-axonal water to the orientation-dependence of one of the most clinically-studied parameters, apparent transverse relaxation T2. Specifically, a tiltable receive coil is interfaced with an ultra-strong gradient MRI scanner to acquire multidimensional MRI data with an unprecedented range of acquisition parameters. Using this setup, compartmental T2 can be disentangled based on differences in diffusional-anisotropy, and its orientation-dependence further elucidated by re-orienting the head with respect to the main magnetic field B→0. A dependence of (compartmental) T2 on the fibre orientation w.r.t. B→0 was observed, and further quantified using characteristic representations for susceptibility- and magic angle effects. Across white matter, anisotropy effects were dominated by the extra-axonal water signal, while the intra-axonal water signal decay varied less with fibre-orientation. Moreover, the results suggest that the stronger extra-axonal T2 orientation-dependence is dominated by magnetic susceptibility effects (presumably from the myelin sheath) while the weaker intra-axonal T2 orientation-dependence may be driven by a combination of microstructural effects. Even though the current design of the tiltable coil only offers a modest range of angles, the results demonstrate an overall effect of tilt and serve as a proof-of-concept motivating further hardware development to facilitate experiments that explore orientational anisotropy. These observations have the potential to lead to white matter microstructural models with increased compartmental sensitivity to disease, and can have direct consequences for longitudinal and group-wise T2- and diffusion-MRI data analysis, where the effect of head-orientation in the scanner is commonly ignored.

Accelerated diffusion and relaxation-diffusion MRI using time-division multiplexing EPI

PURPOSE

To develop a time-division multiplexing echo-planar imaging (TDM-EPI) sequence for approximately two- to threefold acceleration when acquiring joint relaxation-diffusion MRI data with multiple TEs.

METHODS

The proposed TDM-EPI sequence interleaves excitation and data collection for up to 3 separate slices at different TEs and uses echo-shifting gradients to disentangle the overlapping echo signals during the readout period. By properly arranging the sequence event blocks for each slice and adjusting the echo-shifting gradients, diffusion-weighted images from separate slices can be acquired. Therefore, we present 2 variants of the sequence. A single-TE TDM-EPI is presented to demonstrate the concept. Next, a multi-TE TDM-EPI is presented to highlight the advantages of the TDM approach for relaxation-diffusion imaging. These sequences were evaluated on a 3 Tesla scanner with a water phantom and in vivo human brain data.

RESULTS

The single-TE TDM-EPI sequence can simultaneously acquire 2 slices with a maximum b value of 3000 s/mm² and 2.5 mm isotropic resolution using interleaved readout windows with TE ≈ 78 ms. With the same b value and resolution, the multi-TE TDM-EPI sequence can simultaneously acquire 2 or 3 separate slices using interleaved readout sections with shortest TE ≈ 70 ms and ΔTE ≈ 30 ms. Phantom and in vivo experiments have shown that the proposed TDM-EPI sequences can provide similar image quality and diffusion measures as conventional EPI readouts with multiple echoes but can reduce the overall relaxation-diffusion protocol scan time by approximately two- to threefold.

CONCLUSION

TDM-EPI is a novel approach to acquire diffusion imaging data at multiple TEs. This enables a significant reduction in acquisition time for relaxation-diffusion MRI experiments but without compromising image quality and diffusion measurements, thus removing a significant barrier to the adoption of relaxation-diffusion MRI in clinical research studies of neurological and mental disorders.

Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain

PURPOSE

To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types.

METHODS

A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles.

RESULTS

Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup.

CONCLUSIONS

Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.

Efficient whole-brain tract-specific T1 mapping at 3T with slice-shuffled inversion-recovery diffusion-weighted imaging

PURPOSE

Most voxels in white matter contain multiple fiber populations with different orientations and levels of myelination. Conventional T₁ mapping measures 1 T₁ value per voxel, representing a weighted average of the multiple tract T₁ times. Inversion-recovery diffusion-weighted imaging (IR-DWI) allows the T₁ times of multiple tracts in a voxel to be disentangled, but the scan time is prohibitively long. Recently, slice-shuffled IR-DWI implementations have been proposed to significantly reduce scan time. In this work, we demonstrate that we can measure tract-specific T₁ values in the whole brain using simultaneous multi-slice slice-shuffled IR-DWI at 3T.

METHODS

We perform simulations to evaluate the accuracy and precision of our crossing fiber IR-DWI signal model for various fiber parameters. The proposed sequence and signal model are tested in a phantom consisting of crossing asparagus pieces doped with gadolinium to vary T₁ , and in 2 human subjects.

RESULTS

Our simulations show that tract-specific T₁ times can be estimated within 5% of the nominal fiber T₁ values. Tract-specific T₁ values were resolved in subvoxel 2 fiber crossings in the asparagus phantom. Tract-specific T₁ times were resolved in 2 different tract crossings in the human brain where myelination differences have previously been reported; the crossing of the cingulum and genu of the corpus callosum and the crossing of the corticospinal tract and pontine fibers.

CONCLUSION

Whole-brain tract-specific T₁ mapping is feasible using slice-shuffled IR-DWI at 3T. This technique has the potential to improve the microstructural characterization of specific tracts implicated in neurodevelopment, aging, and demyelinating disorders.