The effect of prolonged spaceflight on cerebrospinal fluid and perivascular spaces of astronauts and cosmonauts.

Long-duration spaceflight induces changes to the brain and cerebrospinal fluid compartments and visual acuity problems known as spaceflight-associated neuro-ocular syndrome (SANS). The clinical relevance of these changes and whether they equally affect crews of different space agencies remain unknown. We used MRI to analyze the alterations occurring in the perivascular spaces (PVS) in NASA and European Space Agency astronauts and Roscosmos cosmonauts after a 6-mo spaceflight on the International Space Station (ISS). We found increased volume of basal ganglia PVS and white matter PVS (WM-PVS) after spaceflight, which was more prominent in the NASA crew than the Roscosmos crew. Moreover, both crews demonstrated a similar degree of lateral ventricle enlargement and decreased subarachnoid space at the vertex, which was correlated with WM-PVS enlargement. As all crews experienced the same environment aboard the ISS, the differences in WM-PVS enlargement may have been due to, among other factors, differences in the use of countermeasures and high-resistive exercise regimes, which can influence brain fluid redistribution. Moreover, NASA astronauts who developed SANS had greater pre- and postflight WM-PVS volumes than those unaffected. These results provide evidence for a potential link between WM-PVS fluid and SANS.

Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling.

Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.

Diffusion MRI marks progressive alterations in fiber integrity in the zQ175DN mouse model of Huntington's disease.

Huntington's disease (HD) is a progressive neurodegenerative disease affecting motor and cognitive abilities. Multiple studies have found white matter anomalies in HD-affected humans and animal models of HD. The identification of sensitive white-matter-based biomarkers in HD animal models will be important in understanding disease mechanisms and testing the efficacy of therapeutic interventions. Here we investigated the progression of white matter deficits in the knock-in zQ175DN heterozygous (HET) mouse model of HD at 3, 6 and 11 months of age (M), reflecting different states of phenotypic progression. We compared findings from traditional diffusion tensor imaging (DTI) and advanced fixel-based analysis (FBA) diffusion metrics for their sensitivity in detecting white matter anomalies in the striatum, motor cortex, and segments of the corpus callosum. FBA metrics revealed progressive and widespread reductions of fiber cross-section and fiber density in myelinated bundles of HET mice. The corpus callosum genu was the most affected structure in HET mice at 6 and 11 M based on the DTI and FBA metrics, while the striatum showed the earliest progressive differences starting at 3 M based on the FBA metrics. Overall, FBA metrics detected earlier and more prominent alterations in myelinated fiber bundles compared to the DTI metrics. Luxol fast blue staining showed no loss in myelin density, indicating that diffusion anomalies could not be explained by myelin reduction but diffusion anomalies in HET mice were accompanied by increased levels of neurofilament light chain protein at 11 M. Altogether, our findings reveal progressive alterations in myelinated fiber bundles that can be measured using diffusion MRI, representing a candidate noninvasive imaging biomarker to study phenotype progression and the efficacy of therapeutic interventions in zQ175DN mice. Moreover, our study exposed higher sensitivity of FBA than DTI metrics, suggesting a potential benefit of adopting these advanced metrics in other contexts, including biomarker development in humans.

Optimal experimental design and estimation for q-space trajectory imaging.

Tensor-valued diffusion encoding facilitates data analysis by q-space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision-optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.

ADEPT: Accurate Diffusion Echo-Planar imaging with multi-contrast shoTs.

PURPOSE: To introduce a novel imaging and parameter estimation framework for accurate multi-shot diffusion MRI. THEORY AND METHODS: We propose a new framework called ADEPT (Accurate Diffusion Echo-Planar imaging with multi-contrast shoTs) that enables fast diffusion MRI by allowing diffusion contrast settings to change between shots in a multi-shot EPI acquisition (i.e., intra-scan modulation). The framework estimates diffusion parameter maps directly from the acquired intra-scan modulated k-space data, while simultaneously accounting for shot-to-shot phase inconsistencies. The performance of the estimation framework is evaluated using Monte Carlo simulation studies and in-vivo experiments and compared to that of reference methods that rely on parallel imaging for shot-to-shot phase correction. RESULTS: Simulation and real-data experiments show that ADEPT yields more accurate and more precise estimates of the diffusion metrics in multi-shot EPI data in comparison with the reference methods. CONCLUSION: ADEPT allows fast multi-shot EPI diffusion MRI without significantly degrading the accuracy and precision of the estimated diffusion maps.

Improved diffusion parameter estimation by incorporating T2 relaxation properties into the DKI-FWE model.

The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T2-DKI-FWE model that exploits the T2 relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T2 of tissue). In our approach, the T2 of tissue is estimated as an unknown parameter, whereas the T2 of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T2 of free water is studied. Next, the improved conditioning of T2-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T2-DKI-FWE model is compared to that of the DKI-FWE and T2-DKI models on both simulated and real datasets. The error due to a biased approximation of the T2 of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T2-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T2-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T2 relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.

Brain ventricular volume changes induced by long-duration spaceflight.

Long-duration spaceflight induces detrimental changes in human physiology. Its residual effects and mechanisms remain unclear. We prospectively investigated the changes in cerebrospinal fluid (CSF) volume of the brain ventricular regions in space crew by means of a region of interest analysis on structural brain scans. Cosmonaut MRI data were investigated preflight (n = 11), postflight (n = 11), and at long-term follow-up 7 mo after landing (n = 7). Post hoc analyses revealed a significant difference between preflight and postflight values for all supratentorial ventricular structures, i.e., lateral ventricle (mean % change ± SE = 13.3 ± 1.9), third ventricle (mean % change ± SE = 10.4 ± 1.1), and the total ventricular volume (mean % change ± SE = 11.6 ± 1.5) (all P < 0.0001), with higher volumes at postflight. At follow-up, these structures did not quite reach baseline levels, with still residual increases in volume for the lateral ventricle (mean % change ± SE = 7.7 ± 1.6; P = 0.0009), the third ventricle (mean % change ± SE = 4.7 ± 1.3; P = 0.0063), and the total ventricular volume (mean % change ± SE = 6.4 ± 1.3; P = 0.0008). This spatiotemporal pattern of CSF compartment enlargement and recovery points to a reduced CSF resorption in microgravity as the underlying cause. Our results warrant more detailed and longer longitudinal follow-up. The clinical impact of our findings on the long-term cosmonauts' health and their relation to ocular changes reported in space travelers requires further prospective studies.

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.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

Constrained spherical deconvolution of nonspherically sampled diffusion MRI data.

Constrained spherical deconvolution (CSD) of diffusion-weighted MRI (DW-MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW-MRI signal on the sphere can be represented as the spherical convolution of a single-fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW-MRI data is sampled shell-wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b-values. On shell-wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell-wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.

Modeling brain dynamics after tumor resection using The Virtual Brain.

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation.

MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation.

MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.

Diffusion MRI fiber tractography of the brain.

The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.

A three-dimensional digital neurological atlas of the mustached bat (Pteronotus parnellii).

Substantial knowledge of auditory processing within mammalian nervous systems emerged from neurophysiological studies of the mustached bat (Pteronotus parnellii). This highly social and vocal species retrieves precise information about the velocity and range of its targets through echolocation. Such high acoustic processing demands were likely the evolutionary pressures driving the over-development at peripheral (cochlea), metencephalic (cochlear nucleus), mesencephalic (inferior colliculus), diencephalic (medial geniculate body of the thalamus), and telencephalic (auditory cortex) auditory processing levels in this species. Auditory researchers stand to benefit from a three dimensional brain atlas of this species, due to its considerable contribution to auditory neuroscience. Our MRI-based atlas was generated from 2 sets of image data of an ex-vivo male mustached bat's brain: a detailed 3D-T2-weighted-RARE scan [(59 × 63 x 85) µm3] and track density images based on super resolution diffusion tensor images [(78) µm3] reconstructed from a set of low resolution diffusion weighted images using Super-Resolution-Reconstruction (SRR). By surface-rendering these delineations and extrapolating from cortical landmarks and data from previous studies, we generated overlays that estimate the locations of classic functional subregions within mustached bat auditory cortex. This atlas is freely available from our website and can simplify future electrophysiological, microinjection, and neuroimaging studies in this and related species.

White matter microstructural organisation of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults.

The ability to learn new motor skills is crucial for activities of daily living, especially in older adults. Previous work in younger adults has indicated fast and slow stages for motor learning that were associated with changes in functional interactions within and between brain hemispheres. However, the impact of the structural scaffolds of these functional interactions on different stages of motor learning remains elusive. Using diffusion-weighted imaging and probabilistic constrained spherical deconvolution-based tractography, we reconstructed transcallosal white matter pathways between the left and right primary motor cortices (M1-M1), left dorsal premotor cortex and right primary motor cortex (LPMd-RM1) and right dorsal premotor cortex and left primary motor cortex (RPMd-LM1) in younger and older adults trained in a set of bimanual coordination tasks. We used fractional anisotropy (FA) to assess microstructural organisation of the reconstructed white matter pathways. Older adults showed lower behavioural performance than younger adults and improved their performance more in the fast but less in the slow stage of learning. Linear mixed models predicted that individuals with higher FA of M1-M1 pathways improve more in the fast but less in the slow stage of bimanual learning. Individuals with higher FA of RPMd-LM1 improve more in the slow but less in the fast stage of bimanual learning. These predictions did not differ significantly between younger and older adults suggesting that, in both younger and older adults, the M1-M1 and RPMd-LM1 pathways are important for the fast and slow stage of bimanual learning, respectively.

The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density.

PURPOSE: To investigate whether diffusion MRI can be used to study cortical segregation based on a contrast related to neurite density, thus providing a complementary tool to myelin-based MRI techniques used for myeloarchitecture. METHODS: Several myelin-sensitive MRI methods (e.g., based on T1 , T2 , and T2*) have been proposed to parcellate cortical areas based on their myeloarchitecture. Recent improvements in hardware, acquisition, and analysis methods have opened the possibility of achieving a more robust characterization of cortical microstructure using diffusion MRI. High-quality diffusion MRI data from the Human Connectome Project was combined with recent advances in fiber orientation modeling. The orientational average of the fiber orientation distribution was used as a summary parameter, which was displayed as inflated brain surface views. RESULTS: Diffusion MRI identifies cortical patterns consistent with those previously seen by MRI methods used for studying myeloarchitecture, which have shown patterns of high myelination in the sensorimotor strip, visual cortex, and auditory areas and low myelination in frontal and anterior temporal areas. CONCLUSION: In vivo human diffusion MRI provides a useful complementary noninvasive approach to myelin-based methods used to study whole-brain cortical parcellation, by exploiting a contrast based on tissue microstructure related to neurite density, rather than myelin itself. Magn Reson Med 79:2738-2744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Diffusion kurtosis imaging with free water elimination: A bayesian estimation approach.

PURPOSE: Diffusion kurtosis imaging (DKI) is an advanced magnetic resonance imaging modality that is known to be sensitive to changes in the underlying microstructure of the brain. Image voxels in diffusion weighted images, however, are typically relatively large making them susceptible to partial volume effects, especially when part of the voxel contains cerebrospinal fluid. In this work, we introduce the "Diffusion Kurtosis Imaging with Free Water Elimination" (DKI-FWE) model that separates the signal contributions of free water and tissue, where the latter is modeled using DKI. THEORY AND METHODS: A theoretical study of the DKI-FWE model, including an optimal experiment design and an evaluation of the relative goodness of fit, is carried out. To stabilize the ill-conditioned estimation process, a Bayesian approach with a shrinkage prior (BSP) is proposed. In subsequent steps, the DKI-FWE model and the BSP estimation approach are evaluated in terms of estimation error, both in simulation and real data experiments. RESULTS: Although it is shown that the DKI-FWE model parameter estimation problem is ill-conditioned, DKI-FWE was found to describe the data significantly better compared to the standard DKI model for a large range of free water fractions. The acquisition protocol was optimized in terms of the maximally attainable precision of the DKI-FWE model parameters. The BSP estimator is shown to provide reliable DKI-FWE model parameter estimates. CONCLUSION: The combination of the DKI-FWE model with BSP is shown to be a feasible approach to estimate DKI parameters, while simultaneously eliminating free water partial volume effects. Magn Reson Med 80:802-813, 2018. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Exploring sex differences in the adult zebra finch brain: In vivo diffusion tensor imaging and ex vivo super-resolution track density imaging.

Zebra finches are an excellent model to study the process of vocal learning, a complex socially-learned tool of communication that forms the basis of spoken human language. So far, structural investigation of the zebra finch brain has been performed ex vivo using invasive methods such as histology. These methods are highly specific, however, they strongly interfere with performing whole-brain analyses and exclude longitudinal studies aimed at establishing causal correlations between neuroplastic events and specific behavioral performances. Therefore, the aim of the current study was to implement an in vivo Diffusion Tensor Imaging (DTI) protocol sensitive enough to detect structural sex differences in the adult zebra finch brain. Voxel-wise comparison of male and female DTI parameter maps shows clear differences in several components of the song control system (i.e. Area X surroundings, the high vocal center (HVC) and the lateral magnocellular nucleus of the anterior nidopallium (LMAN)), which corroborate previous findings and are in line with the clear behavioral difference as only males sing. Furthermore, to obtain additional insights into the 3-dimensional organization of the zebra finch brain and clarify findings obtained by the in vivo study, ex vivo DTI data of the male and female brain were acquired as well, using a recently established super-resolution reconstruction (SRR) imaging strategy. Interestingly, the SRR-DTI approach led to a marked reduction in acquisition time without interfering with the (spatial and angular) resolution and SNR which enabled to acquire a data set characterized by a 78µm isotropic resolution including 90 diffusion gradient directions within 44h of scanning time. Based on the reconstructed SRR-DTI maps, whole brain probabilistic Track Density Imaging (TDI) was performed for the purpose of super resolved track density imaging, further pushing the resolution up to 40µm isotropic. The DTI and TDI maps realized atlas-quality anatomical maps that enable a clear delineation of most components of the song control and auditory systems. In conclusion, this study paves the way for longitudinal in vivo and high-resolution ex vivo experiments aimed at disentangling neuroplastic events that characterize the critical period for vocal learning in zebra finch ontogeny.

Super-resolution T1 estimation: Quantitative high resolution T1 mapping from a set of low resolution T1 -weighted images with different slice orientations.

PURPOSE: Quantitative T1 mapping is a magnetic resonance imaging technique that estimates the spin-lattice relaxation time of tissues. Even though T1 mapping has a broad range of potential applications, it is not routinely used in clinical practice as accurate and precise high resolution T1 mapping requires infeasibly long acquisition times. METHOD: To improve the trade-off between the acquisition time, signal-to-noise ratio and spatial resolution, we acquire a set of low resolution T1 -weighted images and directly estimate a high resolution T1 map by means of super-resolution reconstruction. RESULTS: Simulation and in vivo experiments show an increased spatial resolution of the T1 map, while preserving a high signal-to-noise ratio and short scan time. Moreover, the proposed method outperforms conventional estimation in terms of root-mean-square error. CONCLUSION: Super resolution T1 estimation enables resolution enhancement in T1 mapping with the use of standard (inversion recovery) T1 acquisition sequences. Magn Reson Med 77:1818-1830, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Diffusion tensor imaging of the anterior cruciate ligament graft.

PURPOSE: A great need exists for objective biomarkers to assess graft healing following ACL reconstruction to guide the time of return to sports. The purpose of this study was to evaluate the feasibility and reliability of diffusion tensor imaging (DTI) to delineate the anterior cruciate ligament (ACL) graft and to investigate its diffusion properties using a clinical 3T scanner. MATERIALS AND METHODS: DTI of the knee (b = 0, 400, and 800 s/mm2 , 10 diffusion directions, repeated 16 times for a total of 336 diffusion-weighted volumes) was performed at 3T in 17 patients between 3 and 7 months (mean, 4 months) following ACL reconstruction. Tractography was performed by two independent observers to delineate the ACL graft. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated within the graft. Interrater reliability was assessed using the intraclass correlation coefficient (ICC) and the scan-rescan reproducibility was evaluated based on the percentage coefficient of variance (%CV) across 20 repetition bootknife samples. RESULTS: In all subjects, tractography of the ACL graft was feasible. Quantitative evaluation of the diffusion properties of the ACL graft yielded the following mean ± SD values: FA = 0.23 ± 0.04; MD = 1.30 ± 0.11 × 10-3 mm2 /s; AD = 1.61 ± 0.12 × 10-3 mm2 /s, and RD = 1.15 ± 0.11 × 10-3 mm2 /s. Interrater reliability for the DTI parameters was excellent (ICC = 0.91-0.98). Mean %CVs for FA, MD, AD, and RD were 4.6%, 3.5%, 3.7%, and 4.4%, respectively. CONCLUSION: We demonstrated the feasibility and reliability of DTI for the visualization and quantitative evaluation of the ACL graft at 3T. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1423-1432.