Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI

Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

FlexDTI: flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

Objective. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach. FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results. Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error by about 50% on fractional anisotropy and 15% on mean diffusivity, compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance. FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.

Ultrafast diffusion tensor imaging based on deep learning and multi-slice information sharing

Objective. Diffusion tensor imaging (DTI) is excellent for non-invasively quantifying tissue microstructure. Theoretically DTI can be achieved with six different diffusion weighted images and one reference image, but the tensor estimation accuracy is poor in this case. Increasing the number of diffusion directions has benefits for the tensor estimation accuracy, which results in long scan time and makes DTI sensitive to motion. It would be beneficial to decrease the scan time of DTI by using fewer diffusion-weighted images without compromising reconstruction quality.Approach. A novel DTI scan scheme was proposed to achieve fast DTI, where only three diffusion directions per slice was required under a specific direction switching manner, and a deep-learning based reconstruction method was utilized using multi-slice information sharing and correspondingT1-weighted image for high-quality DTI reconstruction. A network with two encoders developed from U-Net was implemented for better utilizing the diffusion data redundancy between neighboring slices. The method performed direct nonlinear mapping from diffusion-weighted images to diffusion tensor.Main results. The performance of the proposed method was verified on the Human Connectome Project public data and clinical patient data. High-quality mean diffusivity, fractional anisotropy, and directionally encoded colormap can be achieved with only three diffusion directions per slice.Significance. High-quality DTI-derived maps can be achieved in less than one minute of scan time. The great reduction of scan time will help push the wider application of DTI in clinical practice.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation of FODs requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results to standard methods such as Constrained Spherical Deconvolution. We demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical datasets of newborns and fetuses. Additionally, we compute agreement metrics within the HARDI newborn dataset, and validate fetal FODs with post-mortem histological data. The results of this study show the advantage of deep learning in inferring the microstructure of the developing brain from in-vivo dMRI measurements that are often very limited due to subject motion and limited acquisition times, but also highlight the intrinsic limitations of dMRI in the analysis of the developing brain microstructure. These findings, therefore, advocate for the need for improved methods that are tailored to studying the early development of human brain.

Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat)

Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.

Current State of Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Traumatic Brain Injury Prognostication

Advanced imaging techniques are needed to assist in providing a prognosis for patients with traumatic brain injury (TBI), particularly mild TBI (mTBI). Diffusion tensor imaging (DTI) is one promising advanced imaging technique, but has shown variable results in patients with TBI and is not without limitations, especially when considering individual patients. Efforts to resolve these limitations are being explored and include developing advanced diffusion techniques, creating a normative database, improving study design, and testing machine learning algorithms. This article will review the fundamentals of DTI, providing an overview of the current state of its utility in evaluating and providing prognosis in patients with TBI.

High-resolution imaging of the excised porcine heart at a whole-body 7 T MRI system using an 8Tx/16Rx pTx coil

INTRODUCTION

MRI of excised hearts at ultra-high field strengths ([Formula: see text]≥7 T) can provide high-resolution, high-fidelity ground truth data for biomedical studies, imaging science, and artificial intelligence. In this study, we demonstrate the capabilities of a custom-built, multiple-element transceiver array customized for high-resolution imaging of excised hearts.

METHOD

A dedicated 16-element transceiver loop array was implemented for operation in parallel transmit (pTx) mode (8Tx/16Rx) of a clinical whole-body 7 T MRI system. The initial adjustment of the array was performed using full-wave 3D-electromagnetic simulation with subsequent final fine-tuning on the bench.

RESULTS

We report the results of testing the implemented array in tissue-mimicking liquid phantoms and excised porcine hearts. The array demonstrated high efficiency of parallel transmits characteristics enabling efficient pTX-based B₁⁺-shimming.

CONCLUSION

The receive sensitivity and parallel imaging capability of the dedicated coil were superior to that of a commercial 1Tx/32Rx head coil in both SNR and T₂*-mapping. The array was successfully tested to acquire ultra-high-resolution (0.1 × 0.1 × 0.8 mm voxel) images of post-infarction scar tissue. High-resolution (isotropic 1.6 mm³ voxel) diffusion tensor imaging-based tractography provided high-resolution information about normal myocardial fiber orientation.

Accelerated cardiac diffusion tensor imaging using deep neural network

Cardiac diffusion tensor imaging (DTI) is a noninvasive method for measuring the microstructure of the myocardium. However, its long scan time significantly hinders its wide application. In this study, we developed a deep learning framework to obtain high-quality DTI parameter maps from six diffusion-weighted images (DWIs) by combining deep-learning-based image generation and tensor fitting, and named the new framework FG-Net. In contrast to frameworks explored in previous deep-learning-based fast DTI studies, FG-Net generates inter-directional DWIs from six input DWIs to supplement the loss information and improve estimation accuracy for DTI parameters. FG-Net was evaluated using two datasets ofex vivohuman hearts. The results showed that FG-Net can generate fractional anisotropy, mean diffusivity maps, and helix angle maps from only six raw DWIs, with a quantification error of less than 5%. FG-Net outperformed conventional tensor fitting and black-box network fitting in both qualitative and quantitative metrics. We also demonstrated that the proposed FG-Net can achieve highly accurate fractional anisotropy and helix angle maps in DWIs with differentb-values.

Diffusion tensor estimation with transformer neural networks

Diffusion tensor imaging (DTI) is a widely used method for studying brain white matter development and degeneration. However, standard DTI estimation methods depend on a large number of high-quality measurements. This would require long scan times and can be particularly difficult to achieve with certain patient populations such as neonates. Here, we propose a method that can accurately estimate the diffusion tensor from only six diffusion-weighted measurements. Our method achieves this by learning to exploit the relationships between the diffusion signals and tensors in neighboring voxels. Our model is based on transformer networks, which represent the state of the art in modeling the relationship between signals in a sequence. In particular, our model consists of two such networks. The first network estimates the diffusion tensor based on the diffusion signals in a neighborhood of voxels. The second network provides more accurate tensor estimations by learning the relationships between the diffusion signals as well as the tensors estimated by the first network in neighboring voxels. Our experiments with three datasets show that our proposed method achieves highly accurate estimations of the diffusion tensor and is significantly superior to three competing methods. Estimations produced by our method with six diffusion-weighted measurements are comparable with those of standard estimation methods with 30-88 diffusion-weighted measurements. Hence, our method promises shorter scan times and more reliable assessment of brain white matter, particularly in non-cooperative patients such as neonates and infants.

Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder

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Overlap has been noted disorders which fall on the psychotic spectrum.

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Univariate studies may miss joint brain features across diagnostic categories.

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mCCA with jICA is paired with features across the psychotic spectrum to produce joint components.

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One joint component displayed a significant relationship with cognitive scores.

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The replicate trends of cortical-subcortical irregularity in psychotic spectrum disorders.

Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB).

Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

The direction-dependence of apparent water exchange rate in human white matter

Transmembrane water exchange is a potential biomarker in the diagnosis and understanding of cancers, brain disorders, and other diseases. Filter-exchange imaging (FEXI), a special case of diffusion exchange spectroscopy adapted for clinical applications, has the potential to reveal different physiological water exchange processes. However, it is still controversial whether modulating the diffusion encoding gradient direction can affect the apparent exchange rate (AXR) measurements of FEXI in white matter (WM) where water diffusion shows strong anisotropy. In this study, we explored the diffusion-encoding direction dependence of FEXI in human brain white matter by performing FEXI with 20 diffusion-encoding directions on a clinical 3T scanner in-vivo. The results show that the AXR values measured when the gradients are perpendicular to the fiber orientation (0.77 ± 0.13 s ^- 1, mean ± standard deviation of all the subjects) are significantly larger than the AXR estimates when the gradients are parallel to the fiber orientation (0.33 ± 0.14 s ^- 1, p < 0.001) in WM voxels with coherently-orientated fibers. In addition, no significant correlation is found between AXRs measured along these two directions, indicating that they are measuring different water exchange processes. What's more, only the perpendicular AXR rather than the parallel AXR shows dependence on axonal diameter, indicating that the perpendicular AXR might reflect transmembrane water exchange between intra-axonal and extra-cellular spaces. Further finite difference (FD) simulations having three water compartments (intra-axonal, intra-glial, and extra-cellular spaces) to mimic WM micro-environments also suggest that the perpendicular AXR is more sensitive to the axonal water transmembrane exchange than parallel AXR. Taken together, our results show that AXR measured along different directions could be utilized to probe different water exchange processes in WM.

Optimal Diffusion Gradient Encoding Scheme for Diffusion Tensor Imaging Based on Golden Ratio

BACKGROUND

The accuracy of the estimated diffusion tensor elements can be improved by using a well-chosen magnetic resonance imaging (MRI) diffusion gradient encoding scheme (DGES). Conversely, diffusion tensor imaging (DTI) is typically challenged by the subject's motion during data acquisition and results in corrupted image data.

PURPOSE

To identify a reliable DGES based on the golden ratio (GR) that can generate an arbitrary number of uniformly distributed directions to precisely estimate the DTI parameters of partially acquired datasets owing to subject motion.

STUDY TYPE

Prospective.

POPULATION

Simulations study; three healthy volunteers.

FIELD STRENGTH/SEQUENCE

3 T/DTI data were obtained using a single-shot echo planar imaging sequence.

STATISTICAL TESTS

A paired sample t-test and the Wilcoxon test were used, P < 0.05 was considered statistically significant.

ASSESSMENT

Two corrupted scenarios A and B were considered and evaluated. For the simulation study, the GR DGES and generated subsets were compared with the Jones and spiral DGESs by electric potential (EP) and condition number (CN). For the human study, the specific subsets A and B selected from scenarios A and B were used for MRI to evaluate fractional anisotropic (FA) map.

RESULTS

For the simulation study, the EPs of the GR (14034.25 ± 12957.24) DGES were significantly lower than the Jones (15112.81 ± 13926.08) and spiral (14297.49 ± 13232.94) DGESs. CN variations of GR (1.633 ± 0.024) DGES were significantly lower than Jones (1.688 ± 0.119) and spiral (4.387 ± 2.915) DGESs. For the human study, GR (0.008 ± 0.020) DGES performed similarly with Jones (0.008 ± 0.022) DGES and was superior to spiral (0.022 ± 0.054) DGES in the FA map error.

DATA CONCLUSION

The GR DGES ensured that directions of the complete sets and subsets were uniform. The GR DGES had lower error propagation sensitivity, which can help image infants or patients who cannot stay still during scanning.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

SuperDTI: Ultrafast DTI and fiber tractography with deep learning

PURPOSE

To develop a deep learning-based reconstruction framework for ultrafast and robust diffusion tensor imaging and fiber tractography.

METHODS

SuperDTI was developed to learn the nonlinear relationship between DWIs and the corresponding diffusion tensor parameter maps. It bypasses the tensor fitting procedure, which is highly susceptible to noises and motions in DWIs. The network was trained and tested using data sets from the Human Connectome Project and patients with ischemic stroke. Results from SuperDTI were compared against widely used methods for tensor parameter estimation and fiber tracking.

RESULTS

Using training and testing data acquired using the same protocol and scanner, SuperDTI was shown to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six raw DWIs, with a quantification error of less than 5% in all white-matter and gray-matter regions of interest. It was robust to noises and motions in the testing data. Furthermore, the network trained using healthy volunteer data showed no apparent reduction in lesion detectability when directly applied to stroke patient data.

CONCLUSIONS

Our results demonstrate the feasibility of superfast DTI and fiber tractography using deep learning with as few as six DWIs directly, bypassing tensor fitting. Such a significant reduction in scan time may allow the inclusion of DTI into the clinical routine for many potential applications.

A new framework for MR diffusion tensor distribution

The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.

Alterations of functional connectivity in auditory and sensorimotor neural networks: A case report in a patient with cortical deafness after bilateral putaminal hemorrhagic stroke

RATIONALE

Cortical deafness is a rare auditory dysfunction caused by damage to brain auditory networks. The aim was to report alterations of functional connectivity in intrinsic auditory, motor, and sensory networks in a cortical deafness patient.

PATIENT CONCERNS

A 41-year-old woman suffered a right putaminal hemorrhage. Eight years earlier, she had suffered a left putaminal hemorrhage and had minimal sequelae. She had quadriparesis, imbalance, hypoesthesia, and complete hearing loss.

DIAGNOSES

She was diagnosed with cortical deafness. After 6 months, resting-state functional magnetic resonance imaging (rs-fMRI) and diffuse tensor imaging (DTI) were performed. DTI revealed that the acoustic radiation was disrupted while the corticospinal tract and somatosensory track were intact using deterministic tracking methods. Furthermore, the patient showed decreased functional connectivity between auditory and sensorimotor networks.

INTERVENTIONS

The patient underwent in-patient stroke rehabilitation therapy for 2 months.

OUTCOMES

Gait function and ability for activities of daily living were improved. However, complete hearing impairment persisted in 6 months after bilateral putaminal hemorrhagic stroke.

LESSONS

Our case report seems to suggest that functional alterations of spontaneous neuronal activity in auditory and sensorimotor networks are related to motor and sensory impairments in a patient with cortical deafness.

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b ​= ​0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b ​= ​0 images and 21 DWIs for the primary eigenvector derived from DTI and two b ​= ​0 images and 26-30 DWIs for various scalar metrics derived from DTI, achieving 3.3-4.6 × ​acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 ​b ​= ​0 images and 90 DWIs measures around 1-1.5 ​mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

Development of Helical Myofiber Tracts in the Human Fetal Heart: Analysis of Myocardial Fiber Formation in the Left Ventricle From the Late Human Embryonic Period Using Diffusion Tensor Magnetic Resonance Imaging

Background

Detection of the fiber orientation pattern of the myocardium using diffusion tensor magnetic resonance imaging lags ≈12 weeks of gestational age (WGA) behind fetal myocardial remodeling with invasion by the developing coronary vasculature (8 WGA). We aimed to use diffusion tensor magnetic resonance imaging tractography to characterize the evolution of fiber architecture in the developing human heart from the later embryonic period.

Methods and Results

Twenty human specimens (8–24 WGA) from the Kyoto Collection of Human Embryos and Fetuses, including specimens from the embryonic period (Carnegie stages 20–23), were used. Diffusion tensor magnetic resonance imaging data were acquired with a 7T magnetic resonance system. Fractional anisotropy and helix angle were calculated using standard definitions. In all samples, the fibers ran helically in an organized pattern in both the left and right ventricles. A smooth transmural change in helix angle values (from positive to negative) was detected in all 16 directions of the ventricles. This feature was observed in almost all small (Carnegie stage 23) and large samples. A higher fractional anisotropy value was detected at the outer side of the anterior wall and septum at Carnegie stage 20 to 22, which spread around the ventricular wall at Carnegie stage 23 and in the early fetal samples (11–12 WGA). The fractional anisotropy value of the left ventricular walls decreased in samples with ≥13 WGA, which remained low (≈0.09) in larger samples.

Conclusions

From the human late embryonic period (from 8 WGA), the helix angle arrangement of the myocardium is comparable to that of the adult, indicating that the myocardial structure blueprint, organization, and integrity are already formed.

Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker

PURPOSE

To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques.

METHODS

An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off.

RESULTS

Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased.

CONCLUSIONS

Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.

Comparing perturbation models for evaluating stability of neuroimaging pipelines

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

Brain MRI Diffusion Encoding Direction Number Affects Tract-Based Spatial Statistics Results in Multiple Sclerosis

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) is a promising approach to detect the underlying brain pathology. These alterations can be seen in several diseases such as multiple sclerosis. Tract-based spatial statistics (TBSS) is an easy to use and robust way for analyzing diffusion data. The effect of acquisition parameters of DTI on TBSS has not been evaluated, especially the number of diffusion encoding directions (NDED), which is directly proportional with scan time.

METHODS

We analyzed a large set of DTI data of healthy controls (N = 126) and multiple sclerosis patients (N = 78). The highest NDED (60 directions) was reduced and a tensor calculation was done separately for every subset. We calculated the mean and standard deviation of DTI parameters under the white matter mask. Moreover, the FMRIB Software Library TBSS pipeline was used on DTI images with 15, 30, 45, and 60 directions to compare differences between groups. Mean DTI parameters were compared between groups as a function of NDED.

RESULTS

The mean value of FA and AD decreased with increasing number of directions. This was more pronounced in areas with smaller FA values. RD and MD were constant. The skeleton size reduced with elevating NDED along with the number of significant voxels. The TBSS analysis showed significant differences between groups throughout the majority of the skeleton and the group difference was associated with NDED.

CONCLUSION

Our results suggested that results of TBSS depended on the NDED, which should be considered when comparing DTI data with varying protocols.

Graph theoretical quantification of white matter reorganization after cortical stroke in mice

Stroke is a devastating disease leading to cell death and disconnection between neurons both locally and remote, often resulting in severe long-term disability. Spontaneous reorganization of areas and pathways not primarily affected by ischemia is, however, associated with albeit limited recovery of function. Quantitative mapping of whole-brain changes of structural connectivity concerning the ischemia-induced sensorimotor deficit and recovery thereof would help to target structural plasticity in order to improve rehabilitation. Currently, only in vivo diffusion MRI can extract the structural whole-brain connectome noninvasively. This approach is, however, used primarily in human studies. Here, we applied atlas-based MRI analysis and graph theory to DTI in wild-type mice with cortical stroke lesions. Using a DTI network approach and graph theory, we aimed at gaining insights into the dynamics of the spontaneous reorganization after stroke related to the recovery of function. We found evidence for altered structural integrity of connections of specific brain regions, including the breakdown of connections between brain regions directly affected by stroke as well as long-range rerouting of intra- and transhemispheric connections related to improved sensorimotor behavior.

Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions

Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ~0.6 μm in the mouse and ~0.65-1.05 μm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-μm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-μm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-μm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 μm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86 μm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-μm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri)

Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

Spin echo based cardiac diffusion imaging at 7T: An ex vivo study of the porcine heart at 7T and 3T

Purpose of this work was to assess feasibility of cardiac diffusion tensor imaging (cDTI) at 7 T in a set of healthy, unfixed, porcine hearts using various parallel imaging acceleration factors and to compare SNR and derived cDTI metrics to a reference measured at 3 T. Magnetic resonance imaging was performed on 7T and 3T whole body systems using a spin echo diffusion encoding sequence with echo planar imaging readout. Five reference (b = 0 s/mm2) images and 30 diffusion directions (b = 700 s/mm2) were acquired at both 7 T and 3 T using a GRAPPA acceleration factor R = 1. Scans at 7 T were repeated using R = 2, R = 3, and R = 4. SNR evaluation was based on 30 reference (b = 0 s/mm2) images of 30 slices of the left ventricle and cardiac DTI metrics were compared within AHA segmentation. The number of hearts scanned at 7 T and 3 T was n = 11. No statistically significant differences were found for evaluated helix angle, secondary eigenvector angle, fractional anisotropy and apparent diffusion coefficient at the different field strengths, given sufficiently high SNR and geometrically undistorted images. R≥3 was needed to reduce susceptibility induced geometric distortions to an acceptable amount. On average SNR in myocardium of the left ventricle was increased from 29±3 to 44±6 in the reference image (b = 0 s/mm2) when switching from 3 T to 7 T. Our study demonstrates that high resolution, ex vivo cDTI is feasible at 7 T using commercial hardware.

Neuroimaging of Traumatic Brain Injury

The purpose of this article is to review conventional and advanced neuroimaging techniques performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of patients with suspected TBI is to prevent secondary injury. In the setting of a moderate to severe TBI, the most appropriate initial neuroimaging examination is a noncontrast head computed tomography (CT), which can reveal life-threatening injuries and direct emergent neurosurgical intervention. We will focus much of the article on advanced neuroimaging techniques including perfusion imaging and diffusion tensor imaging and discuss their potentials and challenges. We believe that advanced neuroimaging techniques may improve the accuracy of diagnosis of TBI and improve management of TBI.

Diffusion tensor imaging of articular cartilage using a navigated radial imaging spin-echo diffusion (RAISED) sequence

OBJECTIVE

To validate a radial imaging spin-echo diffusion tensor (RAISED) sequence for high-resolution diffusion tensor imaging (DTI) of articular cartilage at 3 T.

METHODS

The RAISED sequence implementation is described, including the used non-linear motion correction algorithm. The robustness to eddy currents was tested on phantoms, and accuracy of measurement was assessed with measurements of temperature-dependent diffusion of free water. Motion correction was validated by comparing RAISED with single-shot diffusion-weighted echo-planar imaging (EPI) measures. DTI was acquired in asymptomatic subjects (n = 6) and subjects with doubtful (Kellgren-Lawrence [KL] grade 1, n = 9) and mild (KL = 2, n = 9) symptomatic knee osteoarthritis (OA). MD and FA values without correction, and after all corrections, were calculated. A test-retest evaluation of the DTI acquisition on three asymptomatic and three OA subjects was also performed.

RESULTS

The root mean squared coefficient of variation of the global test-restest reproducibility was 3.54% for MD and 5.34% for FA. MD was significantly increased in both femoral condyles (7-9%) of KL 1 and in the medial (11-17%) and lateral (10-12%) compartments of KL 2 subjects. Averaged FA presented a trend of lower values with increasing KL grade, which was significant for the medial femoral condyle (-11%) of KL 1 and all three compartments in KL 2 subjects (-18 to -11%). Group differences in MD and FA were only significant after motion correction.

CONCLUSION

The RAISED sequence with the proposed reconstruction framework provides reproducible assessment of DTI parameters in vivo at 3 T and potentially the early stages of the disease in large regions of interest.

KEY POINTS

• DTI of articular cartilage is feasible at 3T with a multi-shot RAISED sequence with non-linear motion correction. • RAISED sequence allows estimation of the diffusion indices MD and FA with test-retest errors below 4% (MD) and 6% (FA). • RAISED-based measurement of DTI of articular cartilage with non-linear motion correction holds potential to differentiate healthy from OA subjects.

Water Mobility within Compacted Clay Samples: Multi-Scale Analysis Exploiting 1H NMR Pulsed Gradient Spin Echo and Magnetic Resonance Imaging of Water Density Profiles

¹H NMR pulsed gradient spin echo attenuation and water density profile analysis by magnetic resonance imaging are both used to determine the mobility of water molecules confined within a porous network of compacted kaolinite clay sample (total porosity of ∼50%). These two complementary experimental procedures efficiently probe molecular diffusion within time scales varying between milliseconds and few hours, filling the gap between the time scale of diffusion dynamics measured by traditional quasi elastic neutron scattering and through-diffusion methods. Furthermore, magnetic resonance imaging is a nondestructive investigation tool that is able to assess the effect of the local structure on the macroscopic mobility of the diffusing probe.

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

Diffusion tensor imaging using multiple coils for mouse brain connectomics

The correlation between brain connectivity and psychiatric or neurological diseases has intensified efforts to develop brain connectivity mapping techniques on mouse models of human disease. The neural architecture of mouse brain specimens can be shown non-destructively and three-dimensionally by diffusion tensor imaging, which enables tractography, the establishment of a connectivity matrix and connectomics. However, experiments on cohorts of animals can be prohibitively long. To improve throughput in a 7-T preclinical scanner, we present a novel two-coil system in which each coil is shielded, placed off-isocenter along the axis of the magnet and connected to a receiver circuit of the scanner. Preservation of the quality factor of each coil is essential to signal-to-noise ratio (SNR) performance and throughput, because mouse brain specimen imaging at 7 T takes place in the coil-dominated noise regime. In that regime, we show a shielding configuration causing no SNR degradation in the two-coil system. To acquire data from several coils simultaneously, the coils are placed in the magnet bore, around the isocenter, in which gradient field distortions can bias diffusion tensor imaging metrics, affect tractography and contaminate measurements of the connectivity matrix. We quantified the experimental alterations in fractional anisotropy and eigenvector direction occurring in each coil. We showed that, when the coils were placed 12 mm away from the isocenter, measurements of the brain connectivity matrix appeared to be minimally altered by gradient field distortions. Simultaneous measurements on two mouse brain specimens demonstrated a full doubling of the diffusion tensor imaging throughput in practice. Each coil produced images devoid of shading or artifact. To further improve the throughput of mouse brain connectomics, we suggested a future expansion of the system to four coils. To better understand acceptable trade-offs between imaging throughput and connectivity matrix integrity, studies may seek to clarify how measurement variability, post-processing techniques and biological variability impact mouse brain connectomics.

Diffusion Tensor Imaging (DTI)

Diffusion Tensor Imaging is an MRI technique that allows in vivo noninvasive measurement of the translational motion of water, providing information about its anisotropy (or lack of it) in different tissues. DTI has been commonly used to quantitatively measure the integrity of tissues which may be compromised by neurological disease, such as white matter tracks of the brain, which normally impart significant anisotropy to water motion in healthy brains. However, this anisotropic effect is diminished when axonal or neuronal damage is present. This chapter describes a standard protocol for DTI data acquisition in preclinical studies.

Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress

Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.

The impact of signal-to-noise ratio, diffusion-weighted directions and image resolution in cardiac diffusion tensor imaging - insights from the ex-vivo rat heart

BACKGROUND

Cardiac diffusion tensor imaging (DTI) is limited by scan time and signal-to-noise (SNR) restrictions. This invariably leads to a trade-off between the number of averages, diffusion-weighted directions (ND), and image resolution. Systematic evaluation of these parameters is therefore important for adoption of cardiac DTI in clinical routine where time is a key constraint.

METHODS

High quality reference DTI data were acquired in five ex-vivo rat hearts. We then retrospectively set 2 ≤ SNR ≤ 97, 7 ≤ ND ≤ 61, varied the voxel volume by up to 192-fold and investigated the impact on the accuracy and precision of commonly derived parameters.

RESULTS

For maximal scan efficiency, the accuracy and precision of the mean diffusivity is optimised when SNR is maximised at the expense of ND. With typical parameter settings used clinically, we estimate that fractional anisotropy may be overestimated by up to 13% with an uncertainty of ±30%, while the precision of the sheetlet angles may be as poor as ±31°. Although the helix angle has better precision of ±14°, the transmural range of helix angles may be under-estimated by up to 30° in apical and basal slices, due to partial volume and tapering myocardial geometry.

CONCLUSIONS

These findings inform a baseline of understanding upon which further issues inherent to in-vivo cardiac DTI, such as motion, strain and perfusion, can be considered. Furthermore, the reported bias and reproducibility provides a context in which to assess cardiac DTI biomarkers.

Tensor estimation for double-pulsed diffusional kurtosis imaging

Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.

Diffusion tensor imaging of articular cartilage at 3T correlates with histology and biomechanics in a mechanical injury model

PURPOSE

We establish a mechanical injury model for articular cartilage to assess the sensitivity of diffusion tensor imaging (DTI) in detecting cartilage damage early in time. Mechanical injury provides a more realistic model of cartilage degradation compared with commonly used enzymatic degradation.

METHODS

Nine cartilage-on-bone samples were obtained from patients undergoing knee replacement. The 3 Tesla DTI (0.18 × 0.18 × 1 mm3 ) was performed before, 1 week, and 2 weeks after (zero, mild, and severe) injury, with a clinical radial spin-echo DTI (RAISED) sequence used in our hospital. We performed stress-relaxation tests and used a quasilinear-viscoelastic (QLV) model to characterize cartilage mechanical properties. Serial histology sections were dyed with Safranin-O and given an OARSI grade. We then correlated the changes in DTI parameters with the changes in QLV-parameters and OARSI grades.

RESULTS

After severe injury the mean diffusivity increased after 1 and 2 weeks, whereas the fractional anisotropy decreased after 2 weeks (P < 0.05). The QLV-parameters and OARSI grades of the severe injury group differed from the baseline with statistical significance. The changes in mean diffusivity across all the samples correlated with the changes in the OARSI grade (r = 0.72) and QLV-parameters (r = -0.75).

CONCLUSION

DTI is sensitive in tracking early changes after mechanical injury, and its changes correlate with changes in biomechanics and histology. Magn Reson Med 78:69-78, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

A prospective microstructure imaging study in mixed-martial artists using geometric measures and diffusion tensor imaging: methods and findings

Although diffusion magnetic resonance imaging (dMRI) has been widely used to characterize the effects of repetitive mild traumatic brain injury (rmTBI), to date no studies have investigated how novel geometric models of microstructure relate to more typical diffusion tensor imaging (DTI) sequences. Moreover, few studies have evaluated the sensitivity of different registration pipelines (non-linear, linear and tract-based spatial statistics) for detecting dMRI abnormalities in clinical populations. Results from single-subject analyses in healthy controls (HC) indicated a strong negative relationship between fractional anisotropy (FA) and orientation dispersion index (ODI) in both white and gray matter. Equally important, only moderate relationships existed between all other estimates of free/intracellular water volume fractions and more traditional DTI metrics (FA, mean, axial and radial diffusivity). These findings suggest that geometric measures provide differential information about the cellular microstructure relative to traditional DTI measures. Results also suggest greater sensitivity for non-linear registration pipelines that maximize the anatomical information available in T1-weighted images. Clinically, rmTBI resulted in a pattern of decreased FA and increased ODI, largely overlapping in space, in conjunction with increased intracellular and free water fractions, highlighting the potential role of edema following repeated head trauma. In summary, current results suggest that geometric models of diffusion can provide relatively unique information regarding potential mechanisms of pathology that contribute to long-term neurological damage.

A robust diffusion tensor model for clinical applications of MRI to cartilage

PURPOSE

Diffusion tensor imaging (DTI) of articular cartilage is a promising technique for the early diagnosis of osteoarthritis (OA). However, in vivo diffusion tensor (DT) measurements suffer from low signal-to-noise ratio (SNR) that can result in bias when estimating the six parameters of the full DT, thus reducing sensitivity. This study seeks to validate a simplified four-parameter DT model (zeppelin) for obtaining more robust and sensitive in vivo DTI biomarkers of cartilage.

METHODS

We use simulations in a substrate to mimic changes during OA; and analytic simulations of the DT drawn from a range of fractional anisotropies (FA) measured with high-quality DT data from ex vivo human cartilage. We also use in vivo data from the knees of a healthy subject and two OA patients with Kellgren-Lawrence (KL) grades 1 and 2.

RESULTS

For simulated in vivo cartilage SNR (∼25) and anisotropy levels, the estimated mean values of MD from the DT and zeppelin models were identical to the ground truth values. However, zeppelin's FA is more accurate in measuring water restriction. More specifically, the FA estimations of the DT model were additionally biased by between +2% and +48% with respect to zeppelin values. Additionally, both mean diffusivity (MD) and FA of the zeppelin had lower parameter variance compared to the full DT (F-test, P < 0.05). We observe the same trends from in vivo values of patient data.

CONCLUSION

The zeppelin is more robust than the full DT for cartilage diffusion anisotropy and SNR at levels typically encountered in clinical applications of articular cartilage. Magn Reson Med 79:1157-1164, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Numerical analysis of computational-cannula microscopy

Microscopy in hard-to-reach parts of a sample, such as the deep brain, can be enabled by computational-cannula microscopy (CCM), where light is transported from one end to the other end of a solid-glass cannula. Computational methods are applied to unscramble the recorded signal to obtain the object details. Since the cannula itself can be microscopic (∼250  μm in diameter), CCM can enable minimally invasive imaging. Here, we describe a full-scale simulation model for CCM and apply it to not only explore the limits of the technology, but also use it to improve the imaging performance. Specifically, we show that the complexity of the inverse problem to recover CCM images increases with the aspect ratio (length/diameter) of the cannula geometry. We also perform noise tolerance simulations, which indicate that the smaller aspect ratio cannula tolerate noise better than the longer ones. Analysis on noise tolerance using the proposed simulation model showed 2-3× improvement in noise tolerance when the aspect ratio is reduced in half. We can utilize these simulation tools to further improve the performance of CCM and extend the reach of computational microscopy.

Application of fiber tractography and diffusion tensor imaging to evaluate spinal cord diseases in dogs

Fiber tractography is a technique capable of depicting the three-dimensional structure and connectivity of nerve fibers using serial magnetic resonance diffusion tensor imaging (DTI). To establish fiber tractography and DTI methods in veterinary clinical medicine, we evaluated fiber tractography and DTI parameters: apparent diffusion coefficient (ADC) values and fractional anisotropy (FA) values, in various spinal cord diseases. Spinal cord DTI was examined in 28 dogs with spinal cord diseases. The ADC and FA values were measured at lesion sites and cranial normal sites on spinal cords, and both values of lesion sites were compared with normal sites. In thoracolumbar intervertebral disk herniation (IVDH) cases, depending on their neurologic grades, fiber tractography indicated rupture of fiber trajectories, loss of neuronal bundles and disorder of fiber directions. In these cases, the average ADC values at lesion sites significantly decreased compared with normal sites (P=0.016). In the progressive myelomalacia case, the average ADC and FA values of hyperintense swollen regions in T2WI decreased compared to both values in other disease cases. Finally, in the meningioma case, the continuity of fiber trajectories improved after the administration of an anticancer agent. This study suggests that fiber tractography and DTI are useful in the diagnosis and prognosis of veterinary spinal cord diseases.

The effect of diffusion gradient direction number on corticospinal tractography in the human brain: an along-tract analysis

OBJECTIVES

We evaluated diffusion imaging measures of the corticospinal tract obtained with a probabilistic tractography algorithm applied to data of two acquisition protocols based on different numbers of diffusion gradient directions (NDGDs).

MATERIALS AND METHODS

The corticospinal tracts (CST) of 18 healthy subjects were delineated using 22 and 66-NDGD data. An along-tract analysis of diffusion metrics was performed to detect possible local differences due to NDGD.

RESULTS

FA values at 22-NDGD showed an increase along the central portion of the CST. The mean of partial volume fraction of the orientation of the second fiber (f2) was higher at 66-NDGD bilaterally, because for 66-NDGD data the algorithm more readily detects dominant fiber directions beyond the first, thus the increase in FA at 22-NDGD is due to a substantially reduced detection of crossing fiber volume. However, the good spatial correlation between the tracts drawn at 22 and 66 NDGD shows that the extent of the tract can be successfully defined even at lower NDGD.

CONCLUSIONS

Given the spatial tract localization obtained even at 22-NDGD, local analysis of CST can be performed using a NDGD compatible with clinical protocols. The probabilistic approach was particularly powerful in evaluating crossing fibers when present.

A novel measure of reliability in Diffusion Tensor Imaging after data rejections due to subject motion

Diffusion Tensor Imaging (DTI) is commonly challenged by subject motion during data acquisition, which often leads to corrupted image data. Currently used procedure in DTI analysis is to correct or completely reject such data before tensor estimations, however assessing the reliability and accuracy of the estimated tensor in such situations has evaded previous studies. This work aims to define the loss of data accuracy with increasing image rejections, and to define a robust method for assessing reliability of the result at voxel level. We carried out simulations of every possible sub-scheme (N=1,073,567,387) of Jones30 gradient scheme, followed by confirming the idea with MRI data from four newborn and three adult subjects. We assessed the relative error of the most commonly used tensor estimates for DTI and tractography studies, fractional anisotropy (FA) and the major orientation vector (V1), respectively. The error was estimated using two measures, the widely used electric potential (EP) criteria as well as the rotationally variant condition number (CN). Our results show that CN and EP are comparable in situations with very few rejections, but CN becomes clearly more sensitive to depicting errors when more gradient vectors and images were rejected. The error in FA and V1 was also found depend on the actual FA level in the given voxel; low actual FA levels were related to high relative errors in the FA and V1 estimates. Finally, the results were confirmed with clinical MRI data. This showed that the errors after rejections are, indeed, inhomogeneous across brain regions. The FA and V1 errors become progressively larger when moving from the thick white matter bundles towards more superficial subcortical structures. Our findings suggest that i) CN is a useful estimator of data reliability at voxel level, and ii) DTI preprocessing with data rejections leads to major challenges when assessing brain tissue with lower FA levels, such as all newborn brain, as well as the adult superficial, subcortical areas commonly traced in precise connectivity analyses between cortical regions.

Reproducibility and variation of diffusion measures in the squirrel monkey brain, in vivo and ex vivo

PURPOSE

Animal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus. This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions.

METHODS

Data were acquired from three healthy squirrel monkeys, each imaged twice in vivo and once ex vivo. Reproducibility of fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) was assessed, and normal values were determined both in vivo and ex vivo.

RESULTS

The calculated coefficients of variation (CVs) for both intra-subject and inter-subject MD were below 10% (low variability) while FA had a wider range of CVs, 2-14% intra-subject (moderate variability), and 3-31% inter-subject (high variability). MD in ex vivo tissue was lower than in vivo (30%-50% decrease), while FA values increased in all regions (30-39% increase). The mode of angular differences between in vivo and ex vivo PEVs was 12 degrees.

CONCLUSION

This study characterizes the diffusion properties of the squirrel monkey brain and serves as the groundwork for using the squirrel monkey, both in vivo and ex vivo, as a model for diffusion MRI studies.

Diffusion Tensor Imaging of TBI: Potentials and Challenges

Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brain's neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion techniques in the setting of TBI, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging, diffusion spectrum imaging, and q-ball imaging. We discuss clinical applications of DTI and review the DTI literature as it pertains to TBI. Despite the continued advancements in DTI and related diffusion techniques over the past 20 years, DTI techniques are sensitive for TBI at the group level only and there is insufficient evidence that DTI plays a role at the individual level. We conclude by discussing future directions in DTI research in TBI including the role of machine learning in the pattern classification of TBI.

Symplectic geometry spectrum regression for prediction of noisy time series

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).