Tensor Fitting Research Articles

Anatomically constrained tractography of the fetal brain

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI.

Robust frequency-dependent diffusional kurtosis computation using an efficient direction scheme, axisymmetric modelling, and spatial regularization

Abstract Frequency-dependent diffusion MRI (dMRI) using oscillating gradient encoding and diffusional kurtosis imaging (DKI) techniques have been shown to provide additional insight into tissue microstructure compared to conventional dMRI. However, a technical challenge when combining these techniques is that the generation of the large b-values (≥2000 s/mm2) required for DKI is difficult when using oscillating gradient diffusion encoding. While efficient encoding schemes can enable larger b-values by maximizing multiple gradient channels simultaneously, they do not have sufficient directions to enable the estimation of directional kurtosis parameters. Accordingly, we investigate a DKI fitting algorithm that combines axisymmetric DKI fitting, a prior that enforces the same axis of symmetry for all oscillating gradient frequencies, and spatial regularization, which together enable robust DKI fitting for a 10-direction scheme that offers double the b-value compared to traditional encoding schemes. Using data from mice (oscillating frequencies of 0, 60, and 120 Hz) and humans (0 Hz only), we first show that axisymmetric DKI fitting provides comparable or even slightly improved image quality as compared to kurtosis tensor fitting, and improved DKI map quality when using an efficient encoding scheme with averaging as compared to a traditional scheme with more encoding directions. We also demonstrate that enforcing consistent axes of symmetries across frequencies improves fitting quality, and spatial regularization during fitting preserves spatial features better than using Gaussian filtering prior to fitting, which is an oft-reported pre-processing step for DKI. Thus, the use of an efficient 10-direction scheme combined with the proposed DKI fitting algorithm provides robust maps of frequency-dependent directional kurtosis which may offer increased sensitivity to cytoarchitectural changes that occur at various cellular spatial scales over the course of healthy aging, and due to pathological alterations.

Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies

Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan.To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts.Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.

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 of ex vivo human 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 different b-values.

Test-retest reliability of diffusion tensor imaging scalars in 5-year-olds.

Diffusion tensor imaging (DTI) has provided great insights into the microstructural features of the developing brain. However, DTI images are prone to several artifacts and the reliability of DTI scalars is of paramount importance for interpreting and generalizing the findings of DTI studies, especially in the younger population. In this study, we investigated the intrascan test–retest repeatability of four DTI scalars: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in 5‐year‐old children (N = 67) with two different data preprocessing approaches: a volume censoring pipeline and an outlier replacement pipeline. We applied a region of interest (ROI) and a voxelwise analysis after careful quality control, tensor fitting and tract‐based spatial statistics. The data had three subsets and each subset included 31, 32, or 33 directions thus a total of 96 unique uniformly distributed diffusion encoding directions per subject. The repeatability of DTI scalars was evaluated with intraclass correlation coefficient (ICC(3,1)) and the variability between test and retest subsets. The results of both pipelines yielded good to excellent (ICC(3,1) > 0.75) reliability for most of the ROIs and an overall low variability (<10%). In the voxelwise analysis, FA and RD had higher ICC(3,1) values compared to AD and MD and the variability remained low (<12%) across all scalars. Our results suggest high intrascan repeatability in pediatric DTI and lend confidence to the use of the data in future cross‐sectional and longitudinal studies.

Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold.

In vivo cardiac diffusion tensor imaging (cDTI) characterizes myocardial microstructure. Despite its potential clinical impact, considerable technical challenges exist due to the inherent low signal-to-noise ratio. To reduce scan time toward one breath-hold by reconstructing diffusion tensors for in vivo cDTI with a fitting-free deep learning approach. Retrospective. A total of 197 healthy controls, 547 cardiac patients. A 3 T, diffusion-weighted stimulated echo acquisition mode single-shot echo-planar imaging sequence. A U-Net was trained to reconstruct the diffusion tensor elements of the reference results from reduced datasets that could be acquired in 5, 3 or 1 breath-hold(s) (BH) per slice. Fractional anisotropy (FA), mean diffusivity (MD), helix angle (HA), and sheetlet angle (E2A) were calculated and compared to the same measures when using a conventional linear-least-square (LLS) tensor fit with the same reduced datasets. A conventional LLS tensor fit with all available data (12 ± 2.0 [mean ± sd] breath-holds) was used as the reference baseline. Wilcoxon signed rank/rank sum and Kruskal-Wallis tests. Statistical significance threshold was set at P=0.05. Intersubject measures are quoted as median [interquartile range]. For global mean or median results, both the LLS and U-Net methods with reduced datasets present a bias for some of the results. For both LLS and U-Net, there is a small but significant difference from the reference results except for LLS: MD 5BH (P=0.38) and MD 3BH (P=0.09). When considering direct pixel-wise errors the U-Net model outperformed significantly the LLS tensor fit for reduced datasets that can be acquired in three or just one breath-hold for all parameters. Diffusion tensor prediction with a trained U-Net is a promising approach to minimize the number of breath-holds needed in clinical cDTI studies. 4 TECHNICAL EFFICACY: Stage 1.

An optimal statistical and computational framework for generalized tensor estimation

This paper describes a flexible framework for generalized low-rank tensor estimation problems that includes many important instances arising from applications in computational imaging, genomics, and network analysis. The proposed estimator consists of finding a low-rank tensor fit to the data under generalized parametric models. To overcome the difficulty of nonconvexity in these problems, we introduce a unified approach of projected gradient descent that adapts to the underlying low-rank structure. Under mild conditions on the loss function, we establish both an upper bound on statistical error and the linear rate of computational convergence through a general deterministic analysis. Then we further consider a suite of generalized tensor estimation problems, including sub-Gaussian tensor PCA, tensor regression, and Poisson and binomial tensor PCA. We prove that the proposed algorithm achieves the minimax optimal rate of convergence in estimation error. Finally, we demonstrate the superiority of the proposed framework via extensive experiments on both simulated and real data.

Tracing individual trajectory of longitudinal tau spreading in deep latent space

AbstractBackgroundAlzheimer’s disease (AD) usually appears in mid‐60s (Late‐Onset AD, LOAD), but also rarely appears in patients younger than 65 (Early‐Onset AD, EOAD), while showing distinct features. Tau shows high correlation with AD prognosis and can be detected through positron emission tomography (PET) scan. Tau data mapped into a low dimensional latent space will help detect longitudinal change pattern along AD spectrum. Furthermore, fitting tensors using individual line segments will enable us to track individual tau trajectory in the latent space.MethodWe recruited 74 healthy controls, 75 mild cognitive impairment patients, and 38 AD patients with two PET scans ([18F]Flortaucipir for tau and [18F]Florbetaben for Aβ) with two‐year follow‐up. We used standardized uptake value ratios (SUVRs) for tau PET data. All patients’ tau data went through autoencoder and principal component analysis, pretrained using baseline (BL) tau data. Using only amyloid positive AD patients’ BL – follow‐up (FU) line segments, we fit tensors and tracked individual trajectories in the latent space using a tensor tracing algorithm.ResultIn the 2‐dimensional latent space the points dispersed toward the upper‐ and right‐side as the disease deteriorated. Reconstructed SUVR maps showed tau accumulation pattern of EOAD through x‐axis, such as tau spreading in temporal, prefrontal, and parietal lobes. Reconstructed map of 1st quartile group in y‐axis showed tau spreading dominance in parietal lobe and 4th quartile group showed dominance in prefrontal, temporal lobe. Each patient’s trajectory, predicted based on the latent space, showed reconstructed map following individual tau accumulation pattern. In addition, EOAD tau spreading trajectory tend to precede LOAD tau spreading trajectory.ConclusionWe decomposed high dimensional tau accumulation pattern into 2 independent components through deep‐learning methods. X‐axis showed EOAD tau accumulation pattern and y‐axis was divided into LOAD tau accumulation pattern and non‐prefrontal dominant pattern. In addition, tensor fitting of the longitudinal vector field enables us to find individual tau spreading trajectories which can be used to predict future and past spreading. Even though this study was done using only one factor, tau, it has the potential to be expanded to other factors such as amyloid or neurodegeneration data.

Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters.

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

SuperDTI: Ultrafast DTI and fiber tractography with deep learning.

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

Origin and Properties of Hydrothermal Tremor at Lone Star Geyser, Yellowstone National Park, USA

AbstractGeysers are rare geologic features that intermittently discharge liquid water and steam driven by heating and decompression boiling. The cause of variability in eruptive styles and the associated seismic signals are not well understood. Data collected from five broadband seismometers at Lone Star Geyser, Yellowstone National Park are used to determine the properties, location, and temporal patterns of hydrothermal tremor. The tremor is harmonic at some stages of the eruption cycle and is caused by near‐periodic repetition of discrete seismic events. Using the polarization of ground motion, we identify the location of tremor sources throughout several eruption cycles. During preplay episodes (smaller eruptions preceding the more vigorous major eruption), tremor occurs at depths of 7–10 m and is laterally offset from the geyser's cone by ~5 m. At the onset of the main eruption, tremor sources migrate laterally and become shallower. As the eruption progresses, tremor sources migrate along the same path but in the opposite direction, ending where preplay tremor originates. The upward and then downward migration of tremor sources during eruptions are consistent with warming of the conduit followed by evacuation of water during the main eruption. We identify systematic relations among the two types of preplays, discharge, and the main eruption. A point‐source moment tensor fit to low‐frequency waveforms of an individual tremor event using half‐space velocity models indicates average VS ≳ 0.8 km/s, source depths ~4–20 m, and moment tensors with primarily positive isotropic and compensated linear vector dipole moments.

Associations Between Physical Fitness and Brain Structure in Young Adulthood.

A comprehensive analysis of associations between physical fitness and brain structure in young adulthood is lacking, and further, it is unclear the degree to which associations between physical fitness and brain health can be attributed to a common genetic pathway or to environmental factors that jointly influences physical fitness and brain health. This study examined genotype-confirmed monozygotic and dizygotic twins, along with non-twin full-siblings to estimate the contribution of genetic and environmental factors to variation within, and covariation between, physical fitness and brain structure. Participants were 1,065 young adults between the ages of 22 and 36 from open-access Young Adult Human Connectome Project (YA-HCP). Physical fitness was assessed by submaximal endurance (2-min walk test), grip strength, and body mass index. Brain structure was assessed using magnetic resonance imaging on a Siemens 3T customized ‘Connectome Skyra’ at Washington University in St. Louis, using a 32-channel Siemens head coil. Acquired T1-weighted images provided measures of cortical surface area and thickness, and subcortical volume following processing by the YA-HCP structural FreeSurfer pipeline. Diffusion weighted imaging was acquired to assess white matter tract integrity, as measured by fractional anisotropy, following processing by the YA-HCP diffusion pipeline and tensor fit. Following correction for multiple testing, body mass index was negatively associated with fractional anisotropy in various white matter regions of interest (all | z| statistics > 3.9) and positively associated with cortical thickness within the right superior parietal lobe (z statistic = 4.6). Performance-based measures of fitness (i.e., endurance and grip strength) were not associated with any structural neuroimaging markers. Behavioral genetic analysis suggested that heritability of white matter integrity varied by region, but consistently explained >50% of the phenotypic variation. Heritability of right superior parietal thickness was large (∼75% variation). Heritability of body mass index was also fairly large (∼60% variation). Generally, to of the correlation between brain structure and body mass index could be attributed to heritability effects. Overall, this study suggests that greater body mass index is associated with lower white matter integrity, which may be due to common genetic effects that impact body composition and white matter integrity.

Investigation and validation of magnetic resonance white matter atlas for 0 to 2 years old infants

Objective To construct and verify a standard template of white matter based on Chinese normal 0 to 2 years old infants by using nonlinear high registration accuracy of non-rigid diffeomorphism paradigm (large deformation diffeomorphic metric mapping, LDDMM). Methods Full-term spontaneous labor children without maternal pregnancy disease (hypertension, diabetes, etc.), intrauterine hypoxia and ischemia, head trauma, intracranial infection, intracranial surgery history, family history of mental disorders were selected. Diffusion tensor imaging (DTI) data from the 120 normal Chinese infants under 2 years old were acquired after excluding the existence of neurological diseases revealed by neurologists, radiologists and Gesell Developmental Scale. All the data were divided into six groups including group A: 1 day to 1.5 months, group B: 1.5 to 4.5 months, group C: 4.5 to 9.0 months, group D: 9 to 15 months, group E: 15 to 21 months, and group F: 21 to 24 months. Data pre-processing, normalizing, tensor fitting and calculation of all the images were performed by using MRlcron, DtiStudio, DiffeoMap and SPM software package combined with LDDMM image registration method based on the selected single template of each group. And the average templates of each group were constructed by MATLAB software platform. The set of templates included fractional anisotropy figure (FA), color map, T1 weighted image, b0 image and the mean of all DWfs figures. Results The templates of FA, T1, b0, DWfs and color map for the normal brain magnetic resonance white matter development of the Chinese infants aged 0 to 2 years were successfully established with the subjective scores exceeding 2 points. The objective evaluation root mean squared error was controlled below 0.19, and the cubic chart of brain alternation trend for the children aged 0 to 2 years was consistent with previous literature. Conclusion Constructing a standard template of white matter based on Chinese normal infants, by using nonlinear high registration accuracy of non-rigid diffeomorphism paradigm provides not only a foundation of further research on brain development, mechanism and treatment of pediatric diseases associated with brain, but also objective and fair imaging information for medical education and research.

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.

Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases.

Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.

Brain structure and neurological and behavioural functioning in infants born preterm.

To examine: (1) relationships between brain structure, and concurrently assessed neurological and behavioural functioning, in infants born preterm at term-equivalent age (TEA; approximately 38-44wks); and (2) whether brain structure-function relationships differ between infants born very (24-29wks) and moderate-late (32-36wks) preterm. A total of 257 infants (91 very preterm, 166 moderate-late preterm; 120 males, 137 females) had structural magnetic resonance imaging (MRI) and neurological and behavioural assessments (Prechtl's general movements assessment, Neonatal Intensive Care Unit Network Neurobehavioral Scale [NNNS] and Hammersmith Neonatal Neurological Examination [HNNE]). Two hundred and sixty-three infants (90 very preterm, 173 moderate-late preterm; 131 males, 132 females) had diffusion MRI and assessments. Associations were investigated between assessment scores and global brain volumes using linear regressions, regional brain volumes using Voxel-Based Morphometry, and white matter microstructure using Tract-Based Spatial Statistics. Suboptimal scores on some assessments were associated with lower fractional anisotropy and/or higher axial, radial, and mean diffusivities in some tracts: NNNS attention and reflexes, and HNNE total score and tone, were associated with the corpus callosum and optic radiation; NNNS quality of movement with the corona radiata; HNNE abnormal signs with several major tracts. Brain structure-function associations generally did not differ between the very and moderate-late preterm groups. White matter microstructural alterations may be associated with suboptimal neurological and behavioural performance in some domains at TEA in infants born preterm. Brain structure-function relationships are similar for infants born very preterm and moderate-late preterm. Brain volume is not related to neurological/behavioural function in infants born preterm at term. White matter microstructure is related to some neurological/behavioural domains at term. Brain-behaviour relationships are generally similar for infants born very preterm and moderate-late preterm.

Statistical differences in the white matter tracts in subjects with depression by using different skeletonized voxel-wise analysis approaches and DTI fitting procedures

Major depressive disorder (MDD) is one of the most significant contributors to the global burden of illness. Diffusion tensor imaging (DTI) is a procedure that has been used in several studies to characterize abnormalities in white matter (WM) microstructural integrity in MDD. These studies, however, have provided divergent findings, potentially due to the large variety of methodological alternatives available in conducting DTI research. In order to determine the importance of different approaches to coregistration of DTI-derived metrics to a standard space, we compared results from two different skeletonized voxel-wise analysis approaches: the standard TBBS pipeline and the Advanced Normalization Tools (ANTs) approach incorporating a symmetric image normalization (SyN) algorithm and a group-wise template (ANTs TBSS). We also assessed effects of applying twelve different fitting procedures for the diffusion tensor. For our dataset, lower fractional anisotropy (FA) and axial diffusivity (AD) in depressed subjects compared with healthy controls were found for both methods and for all fitting procedures. No group differences were found for radial and mean diffusivity indices. Importantly, for the AD metric, the normalization methods and fitting procedures showed reliable differences, both in the volume and in the number of significant between-groups difference clusters detected. Additionally, a significant voxel-based correlation, in the left inferior fronto-occipital fasciculus, between AD and self-reported stress was found only for one of the normalization procedure (ANTs TBSS). In conclusion, the sensitivity to detect group-level effects on DTI metrics might depend on the DTI normalization and/or tensor fitting procedures used.

Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults.

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals.

Analysis of the effects of noise, DWI sampling, and value of assumed parameters in diffusion MRI models.

PurposeThis study was a systematic evaluation across different and prominent diffusion MRI models to better understand the ways in which scalar metrics are influenced by experimental factors, including experimental design (diffusion‐weighted imaging [DWI] sampling) and noise.MethodsFour diffusion MRI models—diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator MRI (MAP‐MRI), and neurite orientation dispersion and density imaging (NODDI)—were evaluated by comparing maps and histogram values of the scalar metrics generated using DWI datasets obtained in fixed mouse brain with different noise levels and DWI sampling complexity. Additionally, models were fit with different input parameters or constraints to examine the consequences of model fitting procedures.ResultsExperimental factors affected all models and metrics to varying degrees. Model complexity influenced sensitivity to DWI sampling and noise, especially for metrics reporting non‐Gaussian information. DKI metrics were highly susceptible to noise and experimental design. The influence of fixed parameter selection for the NODDI model was found to be considerable, as was the impact of initial tensor fitting in the MAP‐MRI model.ConclusionAcross DTI, DKI, MAP‐MRI, and NODDI, a wide range of dependence on experimental factors was observed that elucidate principles and practical implications for advanced diffusion MRI. Magn Reson Med 78:1767–1780, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

MO-FG-CAMPUS-IeP3-04: Evaluation of Reproducibility of Diffusion Tensor Imaging Metrics Using Automatically Generated Regions of Interest

Purpose: To establish diffusion tensor imaging (DTI) as a valid diagnostic tool, it is essential to first establish the reproducibility of DTI metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). In this study, we developed two fully automated pipelines for obtaining FA and MD in multiple ROIs, and used them to evaluate intra- and inter-session reproducibility. Methods: Twenty subjects were scanned at 3.0T in two identical sessions separated by 1 week. During each session, the subjects were scanned twice with identical protocols, which comprised a 3D T1 anatomical scan and a DTI scan of 7 minutes. Two pipelines were developed for image analysis. In the first approach, DTI images were linearly registered to anatomical images, which were then non-linearly registered to the MNI152 brain template. And DTI tensor fitting was performed in the MNI152 space. In the second pipeline, the tensor fitting was performed in the native space and the resulting FA maps were non-linearly registered to the FMRIB58 FA template. The reproducibility of FA and MD were evaluated for 48 white matter ROIs using coefficients of variation (CV) and intraclass correlation coefficients (ICC). Results: The second pipeline was found to have higher reproducibility, with 85% of ROIs having inter-session CV =0.82 for FA and >=0.79 for MD. Large variations were only found in small or narrow ROIs which are more prone to partial volume effect and misalignment. Most ROIs had higher inter-session CV than intra-session CV, though the difference was not statistically significant. Conclusion: Our results indicate that current DTI technology can achieve high enough reproducibility for many clinical applications, and that a fully automated image registration pipeline can be a valid and efficient approach for extracting FA and MD information from a large number of ROIs.