Multidimensional MRI Core Publications

Lampinen B, Lätt J, Wasselius J, van Westen D, Nilsson M. Time Dependence in Diffusion MRI Predicts Tissue Outcome in Ischemic Stroke Patients. Magn Reson Med. 2021;86 (2) :754-64.Abstract
PURPOSE: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke. METHODS: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of "rate of kurtosis change" (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable. RESULTS: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values. CONCLUSIONS: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.
Ning L, Szczepankiewicz F, Nilsson M, Rathi Y, Westin C-F. Probing Tissue Microstructure by Diffusion Skewness Tensor Imaging. Sci Rep. 2021;11 (1) :135.Abstract
Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.
Szczepankiewicz F, Sjölund J, Dall'Armellina E, Plein S, Schneider JE, Teh I, Westin C-F. Motion-Compensated Gradient Waveforms for Tensor-Valued Diffusion Encoding by Constrained Numerical Optimization. Magn Reson Med. 2021;85 (4) :2117-26.Abstract
PURPOSE: Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime. METHODS: Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimization framework, along with existing constraints that account for b-tensor shape, hardware restrictions, and concomitant field gradients. We evaluated the efficacy of encoding and motion compensation in simulations, and we demonstrated the approach by linear and planar b-tensor encoding in a healthy heart in vivo. RESULTS: The optimization framework produced asymmetric motion-compensated waveforms that yielded b-tensors of arbitrary shape with improved efficiency compared with previous designs for tensor-valued encoding, and equivalent efficiency to previous designs for linear (conventional) encoding. Technical feasibility was demonstrated in the heart in vivo, showing vastly improved data quality when using motion compensation. The optimization framework is available online in open source. CONCLUSION: Our gradient waveform design is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues in which motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single refocusing pulse or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.
Guder S, Pasternak O, Gerloff C, Schulz R. Strengthened Structure-Function Relationships of the Corticospinal Tract by Free Water Correction After Stroke. Brain Commun. 2021;3 (2) :fcab034.Abstract
The corticospinal tract is the most intensively investigated tract of the human motor system in stroke rehabilitative research. Diffusion-tensor-imaging gives insights into its microstructure, and transcranial magnetic stimulation assesses its excitability. Previous data on the interrelationship between both measures are contradictory. Correlative or predictive models which associate them with motor outcome are incomplete. Free water correction has been developed to enhance diffusion-tensor-imaging by eliminating partial volume with extracellular water, which could improve capturing stroke-related microstructural alterations, thereby also improving structure-function relationships in clinical cohorts. In the present cross-sectional study, data of 18 chronic stroke patients and 17 healthy controls, taken from a previous study on cortico-cerebellar motor tracts, were re-analysed: The data included diffusion-tensor-imaging data quantifying corticospinal tract microstructure with and without free water correction, transcranial magnetic stimulation data assessing recruitment curve properties of motor evoked potentials and detailed clinical data. Linear regression modelling was used to interrelate corticospinal tract microstructure, recruitment curves properties and clinical scores. The main finding of the present study was that free water correction substantially strengthens structure-function associations in stroke patients: Specifically, our data evidenced a significant association between fractional anisotropy of the ipsilesional corticospinal tract and its excitability ( = 0.001, adj.  = 0.54), with free water correction explaining additional 20% in recruitment curve variability. For clinical scores, only free water correction leads to the reliable detection of significant correlations between ipsilesional corticospinal tract fractional anisotropy and residual grip ( = 0.001, adj.  = 0.70) and pinch force ( < 0.001, adj.  = 0.72). Finally, multimodal models can be improved by free water correction as well. This study evidences that corticospinal tract microstructure directly relates to its excitability in stroke patients. It also shows that unexplained variance in motor outcome is considerably reduced by free water correction arguing that it might serve as a powerful tool to improve existing models of structure-function associations and potentially also outcome prediction after stroke.
He J, Zhang F, Xie G, Yao S, Feng Y, Bastos DCA, Rathi Y, Makris N, Kikinis R, Golby AJ, et al. Comparison of Multiple Tractography Methods for Reconstruction of the Retinogeniculate Visual Pathway Using Diffusion MRI. Hum Brain Mapp. 2021;42 (12) :3887-904.Abstract
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
Maziero MP, Seitz-Holland J, Cho KIK, Goldenberg JE, Tanamatis TW, Diniz JB, Cappi C, Alice de Mathis M, Otaduy MCG, da Graça Morais Martin M, et al. Cellular and Extracellular White Matter Abnormalities in Obsessive-Compulsive Disorder: A Diffusion MRI Study. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021.Abstract
BACKGROUND: While previous studies have implicated white matter (WM) as a core pathology of Obsessive-Compulsive Disorder (OCD), the underlying neurobiological processes remain elusive. This study utilizes free-water imaging derived from diffusion MRI to identify cellular and extracellular WM abnormalities in patients with OCD compared to controls (Cs). Next, we investigate the association between diffusion measures, and clinical variables in patients. METHODS: We collected diffusion-weighted MRI and clinical data from eighty-three patients with OCD (56 females/27 males, age=37.7 ± 10.6) and 52 Cs (27 females/25 males, age=32.8 ± 11.5). Fractional anisotropy (FA), fractional anisotropy of cellular tissue (FAT), and extracellular free-water (FW) maps were extracted and compared between patients and Cs using tract-based spatial statistics, and voxel-wise comparison in FSL's Randomise. Next, we correlated these WM measures with clinical variables (age-of-onset and symptom severity) and compared them between patients with and without comorbidities and patients with and without psychiatric medication. RESULTS: Patients with OCD demonstrated lower FA (43.4% of the WM skeleton), FAт (31% of the WM skeleton), and higher FW (22.5% of the WM skeleton) compared to Cs. We did not observe significant correlations between diffusion measures and clinical variables. Comorbidities and medication status did not influence diffusion measures. CONCLUSIONS: Our findings of widespread FA, FAт, and FW abnormalities suggest that OCD is associated with both microstructural cellular and extracellular abnormalities beyond the cortico-striato-thalamo-cortical circuits. Future multimodal longitudinal studies are needed to understand better the influence of essential clinical variables across the illness trajectory.
Szczepankiewicz F, Westin C-F, Nilsson M. Gradient Waveform Design for Tensor-valued Encoding in Diffusion MRI. J Neurosci Methods. 2021;348 :109007.Abstract
Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.
Zhang F, Breger A, Cho KIK, Ning L, Westin C-F, O'Donnell LJ, Pasternak O. Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI. Neuroimage. 2021;233 :117934.Abstract
Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.
Ramos-Llordén G, Vegas-Sánchez-Ferrero G, Liao C, Westin C-F, Setsompop K, Rathi Y. SNR-Enhanced Diffusion MRI With Structure-Preserving Low-Rank Denoising in Reproducing Kernel Hilbert Spaces. Magn Reson Med. 2021;86 (3) :1614-32.Abstract
PURPOSE: To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages nonlinear redundancy in the data to boost the SNR while preserving signal information. METHODS: We exploit nonlinear redundancy of the dMRI data by means of kernel principal component analysis (KPCA), a nonlinear generalization of PCA to reproducing kernel Hilbert spaces. By mapping the signal to a high-dimensional space, a higher level of redundant information is exploited, thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with parameters automatically selected from knowledge of the noise statistics, and validate it on realistic Monte Carlo simulations as well as with in vivo human brain submillimeter and low-resolution dMRI data. We also demonstrate KPCA denoising on multi-coil dMRI data. RESULTS: SNR improvements up to 2.7 were obtained in real in vivo datasets denoised with KPCA, in comparison to SNR gains of up to 1.8 using a linear PCA denoising technique called Marchenko-Pastur PCA (MPPCA). Compared to gold-standard dataset references created from averaged data, we showed that lower normalized root mean squared error was achieved with KPCA compared to MPPCA. Statistical analysis of residuals shows that anatomical information is preserved and only noise is removed. Improvements in the estimation of diffusion model parameters such as fractional anisotropy, mean diffusivity, and fiber orientation distribution functions were also demonstrated. CONCLUSION: Nonlinear redundancy of the dMRI signal can be exploited with KPCA, which allows superior noise reduction/SNR improvements than the MPPCA method, without loss of signal information.
Zhang F, Cho KIK, Tang Y, Zhang T, Kelly S, Biase MD, Xu L, Li H, Matcheri K, Whitfield-Gabrieli S, et al. MK-Curve Improves Sensitivity to Identify White Matter Alterations in Clinical High Risk for Psychosis. Neuroimage. 2021;226 :117564.Abstract
Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.
Gullett JM, O'Shea A, Lamb DG, Porges EC, O'Shea DM, Pasternak O, Cohen RA, Woods AJ. The Association of White Matter Free Water With Cognition in Older Adults. Neuroimage. 2020;219 :117040.Abstract
BACKGROUND: Extracellular free water within cerebral white matter tissue has been shown to increase with age and pathology, yet the cognitive consequences of free water in typical aging prior to the development of neurodegenerative disease remains unclear. Understanding the contribution of free water to cognitive function in older adults may provide important insight into the neural mechanisms of the cognitive aging process. METHODS: A diffusion-weighted MRI measure of extracellular free water as well as a commonly used diffusion MRI metric (fractional anisotropy) along nine bilateral white matter pathways were examined for their relationship with cognitive function assessed by the NIH Toolbox Cognitive Battery in 47 older adults (mean age ​= ​74.4 years, SD ​= ​5.4 years, range ​= ​65-85 years). Probabilistic tractography at the 99th percentile level of probability (Tracts Constrained by Underlying Anatomy; TRACULA) was utilized to produce the pathways on which microstructural characteristics were overlaid and examined for their contribution to cognitive function independent of age, education, and gender. RESULTS: When examining the 99th percentile probability core white matter pathway derived from TRACULA, poorer fluid cognitive ability was related to higher mean free water values across the angular and cingulum bundles of the cingulate gyrus, as well as the corticospinal tract and the superior longitudinal fasciculus. There was no relationship between cognition and mean FA or free water-adjusted FA across the 99th percentile core white matter pathway. Crystallized cognitive ability was not associated with any of the diffusion measures. When examining cognitive domains comprising the NIH Toolbox Fluid Cognition index relationships with these white matter pathways, mean free water demonstrated strong hemispheric and functional specificity for cognitive performance, whereas mean FA was not related to age or cognition across the 99th percentile pathway. CONCLUSIONS: Extracellular free water within white matter appears to increase with normal aging, and higher values are associated with significantly lower fluid but not crystallized cognitive functions. When using TRACULA to estimate the core of a white matter pathway, a higher degree of free water appears to be highly specific to the pathways associated with memory, working memory, and speeded decision-making performance, whereas no such relationship existed with FA. These data suggest that free water may play an important role in the cognitive aging process, and may serve as a stronger and more specific indicator of early cognitive decline than traditional diffusion MRI measures, such as FA.
Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Hansson O, Westin C-F, Nilsson M. Towards Unconstrained Compartment Modeling in White Matter Using Diffusion-Relaxation MRI with Tensor-Valued Diffusion Encoding. Magn Reson Med. 2020;84 (3) :1605-23.Abstract
PURPOSE: To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS: Sampling protocols featuring different b-values (b), b-tensor shapes (b ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm /ms) but higher T values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm /ms) and T values (150 ms). CONCLUSIONS: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.
Dalamagkas K, Tsintou M, Rathi Y, O'Donnell LJ, Pasternak O, Gong X, Zhu A, Savadjiev P, Papadimitriou GM, Kubicki M, et al. Individual Variations of the Human Corticospinal Tract and Its Hand-Related Motor Fibers Using Diffusion MRI Tractography. Brain Imaging Behav. 2020;14 (3) :696-714.Abstract
The corticospinal tract (CST) is one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we propose a novel method, using manually drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heat maps from 37 subjects for both the aforementioned tracts, which could be utilized as a reference for future studies with clinical focus to explore neuropathology in both trauma and disease states.
Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, et al. Creation of a Novel Trigeminal Tractography Atlas for Automated Trigeminal Nerve Identification. Neuroimage. 2020;220 :117063.Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
Haehn D, Franke L, Zhang F, Cetin-Karayumak S, Pieper S, O'Donnell LJ, Rathi Y. TRAKO: Efficient Transmission of Tractography Data for Visualization. Med Image Comput Comput Assist Interv. 2020;12267 :322-32.Abstract
Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines. Such vast amounts of data require formats that allow for efficient storage, transfer, and visualization. We present TRAKO, a new data format based on the Graphics Layer Transmission Format (glTF) that enables immediate graphical and hardware-accelerated processing. We integrate a state-of-the-art compression technique for vertices, streamlines, and attached scalar and property data. We then compare TRAKO to existing tractography storage methods and provide a detailed evaluation on eight datasets. TRAKO can achieve data reductions of over 28x without loss of statistical significance when used to replicate analysis from previously published studies.

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