ABSTRACT
OBJECTIVES: The aim of this study was to evaluate the use of a multicontrast deep learning (DL)-reconstructed 4-fold accelerated 2-dimensional (2D) turbo spin echo (TSE) protocol and the feasibility of 3-dimensional (3D) superresolution reconstruction (SRR) of DL-enhanced 6-fold accelerated 2D Dixon TSE magnetic resonance imaging (MRI) for comprehensive knee joint assessment, by comparing image quality and diagnostic performance with a conventional 2-fold accelerated 2D TSE knee MRI protocol. MATERIALS AND METHODS: This prospective, ethics-approved study included 19 symptomatic adult subjects who underwent knee MRI on a clinical 3 T scanner. Every subject was scanned with 3 DL-enhanced acquisition protocols in a single session: a clinical standard 2-fold in-plane parallel imaging (PI) accelerated 2D TSE-based protocol (5 sequences, 11 minutes 23 seconds) that served as a reference, a DL-reconstructed 4-fold accelerated 2D TSE protocol combining 2-fold PI and 2-fold simultaneous multislice acceleration (5 sequences, 6 minutes 24 seconds), and a 3D SRR protocol based on DL-enhanced 6-fold accelerated (ie, 3-fold PI and 2-fold simultaneous multislice) 2D Dixon TSE MRI (6 anisotropic 2D Dixon TSE acquisitions rotated around the phase-encoding axis, 6 minutes 24 seconds). This resulted in a total of 228 knee MRI scans comprising 21,204 images. Three readers evaluated all pseudonymized and randomized images in terms of image quality using a 5-point Likert scale. Two of the readers (musculoskeletal radiologists) additionally evaluated anatomical visibility and diagnostic confidence to assess normal and pathological knee structures with a 5-point Likert scale. They recorded the presence and location of internal knee derangements, including cartilage defects, meniscal tears, tears of ligaments, tendons and muscles, and bone injuries. The statistical analysis included nonparametric Friedman tests, and interreader and intrareader agreement assessment using the weighted Fleiss-Cohen kappa (κ) statistic. P values of less than 0.05 were considered statistically significant. RESULTS: The evaluated DL-enhanced 4-fold accelerated 2D TSE protocol provided very similar image quality and anatomical visibility to the standard 2D TSE protocol, whereas the 3D SRR Dixon TSE protocol scored less in terms of overall image quality due to reduced edge sharpness and the presence of artifacts (P < 0.001). Subjective signal-to-noise ratio, contrast resolution, fluid brightness, and fat suppression were good to excellent for all protocols. For 1 reader, the Dixon method of the 3D SRR protocol provided significantly better fat suppression than the spectral fat saturation applied in the standard 2D TSE protocol (P < 0.05). The visualization of knee structures with 3D SRR Dixon TSE was very similar to the standard protocol, except for cartilage, tendons, and bone, which were affected by the presence of reconstruction and aliasing artifacts (P < 0.001). The diagnostic confidence of both readers was high for all protocols and all knee structures, except for cartilage and tendons. The standard 2D TSE protocol showed a significantly higher diagnostic confidence for assessing tendons than 3D SRR Dixon TSE MRI (P < 0.01). The interreader and intrareader agreement for the assessment of internal knee derangements using any of the 3 protocols was substantial to almost perfect (κ = 0.67-1.00). For cartilage, the interreader agreement was substantial for DL-enhanced accelerated 2D TSE (κ = 0.79) and almost perfect for standard 2D TSE (κ = 0.98) and 3D SRR Dixon TSE (κ = 0.87). For menisci, the interreader agreement was substantial for 3D SRR Dixon TSE (κ = 0.70-0.80) and substantial to almost perfect for standard 2D TSE (κ = 0.80-0.99) and DL-enhanced 2D TSE (κ = 0.87-1.00). Moreover, the total acquisition time was reduced by 44% when using the DL-enhanced accelerated 2D TSE or 3D SRR Dixon TSE protocol instead of the conventional 2D TSE protocol. CONCLUSIONS: The presented DL-enhanced 4-fold accelerated 2D TSE protocol provides image quality and diagnostic performance similar to the standard 2D protocol. Moreover, the 3D SRR of DL-enhanced 6-fold accelerated 2D Dixon TSE MRI is feasible for multicontrast 3D knee MRI as its diagnostic performance is comparable to standard 2-fold accelerated 2D knee MRI. However, reconstruction and aliasing artifacts need to be further addressed to guarantee a more reliable visualization and assessment of cartilage, tendons, and bone. Both the 2D and 3D SRR DL-enhanced protocols enable a 44% faster examination compared with conventional 2-fold accelerated routine 2D TSE knee MRI and thus open new paths for more efficient clinical 2D and 3D knee MRI.
ABSTRACT
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.
Subject(s)
Huntington Disease , Neurodegenerative Diseases , White Matter , Humans , Animals , Mice , Diffusion Tensor Imaging , Huntington Disease/diagnostic imaging , Huntington Disease/genetics , Diffusion Magnetic Resonance Imaging , White Matter/diagnostic imaging , Disease Models, Animal , BiomarkersABSTRACT
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.
Subject(s)
Image Processing, Computer-Assisted , Magnetic Resonance Angiography , Humans , Image Processing, Computer-Assisted/methods , Spin Labels , Bayes Theorem , Magnetic Resonance Angiography/methods , Brain/blood supply , Cerebrovascular Circulation/physiology , Signal-To-Noise Ratio , Magnetic Resonance Imaging/methodsABSTRACT
Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and subsequent lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.Clinical relevance- This study demonstrates the positive impact of super-resolution reconstruction from T2-w FLAIR multi-slice MRI acquisitions on segmentation performance of MS lesions.
Subject(s)
Biological Phenomena , Multiple Sclerosis , Humans , Multiple Sclerosis/diagnostic imaging , Brain/pathology , Magnetic Resonance Imaging/methodsABSTRACT
The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70∘, while CSD performs well down to a crossing angle of 50∘. In general, CSD outperforms the FRT in estimating fiber orientations.
ABSTRACT
The prospect of continued manned space missions warrants an in-depth understanding of how prolonged microgravity affects the human brain. Functional magnetic resonance imaging (fMRI) can pinpoint changes reflecting adaptive neuroplasticity across time. We acquired resting-state fMRI data of cosmonauts before, shortly after, and eight months after spaceflight as a follow-up to assess global connectivity changes over time. Our results show persisting connectivity decreases in posterior cingulate cortex and thalamus and persisting increases in the right angular gyrus. Connectivity in the bilateral insular cortex decreased after spaceflight, which reversed at follow-up. No significant connectivity changes across eight months were found in a matched control group. Overall, we show that altered gravitational environments influence functional connectivity longitudinally in multimodal brain hubs, reflecting adaptations to unfamiliar and conflicting sensory input in microgravity. These results provide insights into brain functional modifications occurring during spaceflight, and their further development when back on Earth.
Subject(s)
Weightlessness , Humans , Brain/diagnostic imaging , Gyrus Cinguli , Magnetic Resonance Imaging/methods , Parietal LobeABSTRACT
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.
Subject(s)
Echo-Planar Imaging , Image Processing, Computer-Assisted , Echo-Planar Imaging/methods , Image Processing, Computer-Assisted/methods , Diffusion Magnetic Resonance Imaging/methods , Computer Simulation , Monte Carlo Method , Brain/diagnostic imagingABSTRACT
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.
Subject(s)
Brain , Research Design , Humans , Brain/diagnostic imaging , Algorithms , Diffusion Magnetic Resonance Imaging/methods , Least-Squares AnalysisABSTRACT
BACKGROUND: Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE: In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS: By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS: Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION: This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.
Subject(s)
Alzheimer Disease , White Matter , Humans , Diffusion Magnetic Resonance Imaging , Alzheimer Disease/diagnostic imaging , Diffusion Tensor Imaging , White Matter/diagnostic imaging , White Matter/anatomy & histology , Brain/diagnostic imaging , Brain/anatomy & histologyABSTRACT
Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic resonance imaging. MS-SRR consists in the estimation of an isotropic high-resolution image from a series of anisotropic MS images with a low through-plane resolution, where the anisotropic low-resolution images can be acquired according to different acquisition schemes. However, it is yet unclear how these schemes compare in terms of statistical performance criteria, especially for regularized MS-SRR. In this work, the estimation performance of two commonly adopted MS-SRR acquisition schemes based on shifted and rotated MS images respectively are evaluated in a Bayesian framework. The maximum a posteriori estimator, which introduces regularization by incorporating prior knowledge in a statistically well-defined way, is put forward as the estimator of choice and its accuracy, precision, and Bayesian mean squared error (BMSE) are used as performance criteria. Analytic calculations as well as Monte Carlo simulation experiments show that the rotated scheme outperforms the shifted scheme in terms of precision, accuracy, and BMSE. Furthermore, the superior performance of the rotated scheme is confirmed in real data experiments and in retrospective simulation experiments with and without inter-image motion. Results show that the rotated scheme allows regularized MS-SRR with a higher accuracy and precision than the shifted scheme, besides being more resilient to motion.
ABSTRACT
Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method's performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI.
Subject(s)
Imaging, Three-Dimensional , Magnetic Resonance Imaging , Bayes Theorem , Brain/diagnostic imaging , Image Processing, Computer-Assisted , Imaging, Three-Dimensional/methods , Magnetic Resonance Imaging/methods , MotionABSTRACT
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.
Subject(s)
Astronauts , Cerebrospinal Fluid , Glymphatic System , Space Flight , Vision Disorders , Cerebrospinal Fluid/diagnostic imaging , Glymphatic System/diagnostic imaging , Humans , Magnetic Resonance Imaging , Vision Disorders/cerebrospinal fluid , Vision Disorders/diagnostic imaging , White Matter/diagnostic imagingABSTRACT
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.
Subject(s)
Diffusion Tensor Imaging , Water , Benchmarking , Brain/metabolism , Diffusion , Diffusion Magnetic Resonance Imaging , Diffusion Tensor Imaging/methods , Humans , Water/metabolismABSTRACT
Humans undergo extreme physiological changes when subjected to long periods of weightlessness, and as we continue to become a space-faring species, it is imperative that we fully understand the physiological changes that occur in the human body, including the brain. In this study, we present findings of brain structural changes associated with long-duration spaceflight based on diffusion magnetic resonance imaging (dMRI) data. Twelve cosmonauts who spent an average of six months aboard the International Space Station (ISS) were scanned in an MRI scanner pre-flight, ten days after flight, and at a follow-up time point seven months after flight. We performed differential tractography, a technique that confines white matter fiber tracking to voxels showing microstructural changes. We found significant microstructural changes in several large white matter tracts, such as the corpus callosum, arcuate fasciculus, corticospinal, corticostriatal, and cerebellar tracts. This is the first paper to use fiber tractography to investigate which specific tracts exhibit structural changes after long-duration spaceflight and may direct future research to investigate brain functional and behavioral changes associated with these white matter pathways.
Subject(s)
Space Flight , Weightlessness , White Matter , Astronauts , Brain/diagnostic imaging , Brain/pathology , Humans , White Matter/diagnostic imaging , White Matter/pathologyABSTRACT
The clinical application of diffusion MRI is practically hindered by its long scan time. In this work, we introduce a novel imaging and parameter estimation framework for time-efficient diffusion MRI. To improve the scan efficiency, we propose ADEPT (Accelerated Diffusion EPI with multi-contrast shoTs), in which diffusion contrast settings are allowed to change between shots in a multi-shot EPI acquisition (i.e. intra-scan modulation). The framework simultaneously corrects for artifacts related to shot-to-shot phase inconsistencies in multi-shot imaging by iteratively estimating the phase map parameters along with the diffusion model parameters directly from the acquired intra-scan modulated k-space data. Monte Carlo simulation experiments show the effective estimation of diffusion tensor parameters in multi-shot EPI diffusion imaging.
Subject(s)
Algorithms , Image Processing, Computer-Assisted , Artifacts , Computer Simulation , Diffusion Magnetic Resonance ImagingABSTRACT
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.
Subject(s)
Diffusion Tensor Imaging/methods , White Matter/diagnostic imaging , Brain Mapping , Healthy Volunteers , Humans , Image Processing, Computer-AssistedABSTRACT
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.
Subject(s)
Brain/diagnostic imaging , Databases, Factual , Diffusion Magnetic Resonance Imaging/methods , Image Processing, Computer-Assisted/methods , Neural Networks, Computer , Animals , Brain/physiology , Humans , MiceABSTRACT
Humans can be motivated by the prospect of gaining a reward. However, the extent to which we are affected by reward information differs from person to person. A possible mechanism underlying these inter-individual differences may be alterations in white matter (WM) microstructure; however, the relationship between WM properties and reward-based behaviour in healthy participants has not yet been explored. Here, we used a fixel-based approach to investigate potential associations between WM tracts and performance in a reward-cuing task. We found that WM properties in the corpus callosum, right uncinate fasciculus, left ventral cingulum, and accumbofrontal tracts were inversely related to reward-triggered performance benefits (indexed by faster reaction times). Moreover, smaller WM property values in the corpus callosum, uncinate fasciculus, and accumbofrontal tracts were associated with higher scores on the Behavioral Inhibition System scale, reflecting greater sensitivity to potential punishment. Finally, we also observed associations between functional hemodynamic activity in the ventral striatum and WM microstructure. The finding that reward-based behavioural benefits are related to lower measures of WM tracts is in contrast to studies linking higher WM metrics to superior cognitive performance. We interpret the current pattern in terms of higher susceptibility to motivationally relevant stimuli, which is in line with the current and previous studies reporting inverse relationships between WM properties and motivational traits. Taking a broader perspective, such propensities may only be beneficial up to a certain point, at which these may become detrimental to performance and even manifest as impulsive and addictive behaviour.
Subject(s)
Reward , White Matter , Corpus Callosum , Humans , Impulsive Behavior , Nerve Net , White Matter/diagnostic imagingABSTRACT
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.
Subject(s)
Brain/diagnostic imaging , Databases, Factual , Diffusion Magnetic Resonance Imaging/methods , Image Processing, Computer-Assisted/methods , Nerve Net/diagnostic imaging , Databases, Factual/statistics & numerical data , HumansABSTRACT
Long-duration spaceflight causes widespread physiological changes, although its effect on brain structure remains poorly understood. In this work, we acquired diffusion magnetic resonance imaging to investigate alterations of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compositions in each voxel, before, shortly after, and 7 months after long-duration spaceflight. We found increased WM in the cerebellum after spaceflight, providing the first clear evidence of sensorimotor neuroplasticity. At the region of interest level, this increase persisted 7 months after return to Earth. We also observe a widespread redistribution of CSF, with concomitant changes in the voxel fractions of adjacent GM. We show that these GM changes are the result of morphological changes rather than net tissue loss, which remained unclear from previous studies. Our study provides evidence of spaceflight-induced neuroplasticity to adapt motor strategies in space and evidence of fluid shift-induced mechanical changes in the brain.