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Journal of Biomechanical Engineering Nov 2022Due to lack of full vascularization, the meniscus relies on diffusion through the extracellular matrix to deliver small (e.g., nutrients) and large (e.g., proteins) to...
Due to lack of full vascularization, the meniscus relies on diffusion through the extracellular matrix to deliver small (e.g., nutrients) and large (e.g., proteins) to resident cells. Under normal physiological conditions, the meniscus undergoes up to 20% compressive strains. While previous studies characterized solute diffusivity in the uncompressed meniscus, to date, little is known about the diffusive transport under physiological strain levels. This information is crucial to fully understand the pathophysiology of the meniscus. The objective of this study was to investigate strain-dependent diffusive properties of the meniscus fibrocartilage. Tissue samples were harvested from the central portion of porcine medial menisci and tested via fluorescence recovery after photobleaching to measure diffusivity of fluorescein (332 Da) and 40 K Da dextran (D40K) under 0%, 10%, and 20% compressive strain. Specifically, average diffusion coefficient and anisotropic ratio, defined as the ratio of the diffusion coefficient in the direction of the tissue collagen fibers to that orthogonal, were determined. For all the experimental conditions investigated, fluorescein diffusivity was statistically faster than that of D40K. Also, for both molecules, diffusion coefficients significantly decreased, up to ∼45%, as the strain increased. In contrast, the anisotropic ratios of both molecules were similar and not affected by the strain applied to the tissue. This suggests that compressive strains used in this study did not alter the diffusive pathways in the meniscus. Our findings provide new knowledge on the transport properties of the meniscus fibrocartilage that can be leveraged to further understand tissue pathophysiology and approaches to tissue restoration.
Topics: Animals; Anisotropy; Diffusion; Fibrocartilage; Fluoresceins; Meniscus; Swine
PubMed: 35789377
DOI: 10.1115/1.4054931 -
Magnetic Resonance in Medicine Dec 2022Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion...
PURPOSE
Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, in mm s and a fractional exponent, , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized.
METHODS
Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between and 5000 s mm . The effects of varying maximum b-value ( ), number of b-value shells, and the effects of Rician noise were investigated.
RESULTS
QDTI measures showed dependence, most significantly for in white matter, which monotonically decreased with higher leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of and underestimation of in white matter, and overestimation of and anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at s mm , and 4 b-value shells at s mm , providing minimal bias in and compared to the MbR.
CONCLUSION
A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.
Topics: Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Reproducibility of Results; White Matter
PubMed: 36054778
DOI: 10.1002/mrm.29420 -
Brain Imaging and Behavior Oct 2019Both white and grey matter atrophy with age, but it is still unclear how decline in white matter relates to decline in grey matter, and how this relationship varies with...
Both white and grey matter atrophy with age, but it is still unclear how decline in white matter relates to decline in grey matter, and how this relationship varies with age. In a group of healthy adults from 20 to 80 years old, divided into three age groups by tertiles, we cross-sectionally examined the white-to-grey matter associations in the fornix and the hippocampus, and tested if and how the fornix-to-hippocampus relationship differs across the age groups. Both structures were also tested as predictors for performance on a memory test, the Selective Reminding Task (SRT). Participants were imaged with T1-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI), from which the hippocampal volume, fractional anisotropy (FA), and mean diffusivity (MD) for the bilateral crus and body of the fornix were calculated. Our data showed that even after accounting for age, sex, and motion parameters, fornix integrity predicted hippocampal volume in the two older age groups (middle and old age) for the crus of the fornix, and only in the oldest age group for the body of the fornix. Furthermore, fornix integrity significantly predicted SRT performance, whereas hippocampal volume did not; this relationship was also observed only in the oldest age group, and absent in the two younger age groups. The age specificity of the relationships suggests that the fornix-to-hippocampus relationship only manifests once brain structures begin to atrophy in old age, and that fornix integrity is a more sensitive measure for episodic memory than is hippocampal volume.
Topics: Adult; Aged; Aged, 80 and over; Aging; Anisotropy; Diffusion Tensor Imaging; Female; Fornix, Brain; Hippocampus; Humans; Magnetic Resonance Imaging; Male; Memory, Episodic; Middle Aged; Neuropsychological Tests; White Matter; Young Adult
PubMed: 30187206
DOI: 10.1007/s11682-018-9958-1 -
Magnetic Resonance Imaging Jan 2023To describe an optimized fiber orientation density function (fODF) rectification procedure that removes negative values and absorbs all features below a specified...
PURPOSE
To describe an optimized fiber orientation density function (fODF) rectification procedure that removes negative values and absorbs all features below a specified threshold into a constant background.
THEORY AND METHODS
The fODF for a white matter imaging voxel describes the angular density of axons. Because of signal noise and Gibbs ringing, fODFs estimated with diffusion MRI may take on unphysical negative values in some directions and contain spurious peaks. In order to suppress such artifacts, an fODF rectification procedure is proposed that both eliminates all negative values and incorporates all features below a specified threshold, η, into a constant background while at the same time minimizing the mean square deviation from the original, unrectified fODF. Calculating this fODF is straightforward, and the directions and shapes of peaks not absorbed into the background are preserved. The rectification method is illustrated for an analytic fODF model and for experimental diffusion MRI data obtained in healthy human brain, with the original fODFs being obtained from fiber ball imaging.
RESULTS
Examples of optimal rectified fODFs are given for three choices of the background threshold referred to as minimal rectification (η = 0), average-level rectification (η ≈ 0.08), and fractional-anisotropy-axonal-based rectification (η ≈ 0.1). As η is increased, artifacts and other small features are more strongly suppressed, but the major fODF peaks are largely unaffected for the range of η values illustrated by these three alternatives.
CONCLUSION
Artifactual features of fODFs estimated with diffusion MRI can be effectively suppressed by applying the proposed optimized rectification procedure. Since it minimizes fODF distortion in the mean square sense, it may be useful in the study of how fODF fine structure is affected by aging and disease.
Topics: Humans; Image Processing, Computer-Assisted; Diffusion Magnetic Resonance Imaging; White Matter; Brain; Anisotropy
PubMed: 36368495
DOI: 10.1016/j.mri.2022.11.001 -
Journal of Neuroimaging : Official... Jan 2022This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy...
BACKGROUND AND PURPOSE
This study aims todetermine the sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC).
METHODS
Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29 early MS patients and 31 age- and sex-matched HC. Thickness of 68 cortical regions and resting-state functional-connectivity (RSFC) among them were calculated. The distribution of structural and functional metrics between groups were compared using Wilcoxon rank-sum test. Utilizing a machine learning method (adaptive boosting), 6 models were built based on: 1-SWM, 2-DWM, 3-SWM and DWM, 4-cortical thickness, or 5-RSFC measures. In model 6, all features from previous models were incorporated. The models were trained with nested 5-folds cross-validation. Area under the receiver operating characteristic curve (AUC ) values were calculated to evaluate classification performance of each model. Permutation tests were used to compare the AUC values.
RESULTS
Patients had higher MD in SWM bundles including insula, inferior frontal, orbitofrontal, superior and medial temporal, and pre- and post-central cortices (p < .05). No group differences were found for any other MRI metric. The model incorporating SWM and DWM features provided the best classification (AUC = 0.75). The SWM model provided higher AUC (0.74), compared to DWM (0.63), cortical thickness (0.67), RSFC (0.63), and all-features (0.68) models (p < .001 for all).
CONCLUSION
Our results reveal a non-random pattern of SWM abnormalities at early stages of MS even before pronounced structural and functional alterations emerge.
Topics: Anisotropy; Diffusion Tensor Imaging; Humans; Machine Learning; Magnetic Resonance Imaging; Multiple Sclerosis; White Matter
PubMed: 34532924
DOI: 10.1111/jon.12934 -
Medical Image Analysis Apr 2022AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in...
AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain.
Topics: Brain; Diffusion; Diffusion Magnetic Resonance Imaging; Humans; Image Processing, Computer-Assisted
PubMed: 35074665
DOI: 10.1016/j.media.2022.102356 -
AJNR. American Journal of Neuroradiology May 2022Ischemic stroke is a worldwide problem, with 15 million people experiencing a stroke annually. MR imaging is a valuable tool for understanding and assessing brain... (Review)
Review
Ischemic stroke is a worldwide problem, with 15 million people experiencing a stroke annually. MR imaging is a valuable tool for understanding and assessing brain changes after stroke and predicting recovery. Of particular interest is the use of diffusion MR imaging in the nonacute stage 1-30 days poststroke. Thousands of articles have been published on the use of diffusion MR imaging in stroke, including several recent articles reviewing the use of DTI for stroke. The goal of this work was to survey and put into context the recent use of diffusion MR imaging methods beyond DTI, including diffusional kurtosis, generalized fractional anisotropy, spherical harmonics methods, and neurite orientation and dispersion models, in patients poststroke. Early studies report that these types of beyond-DTI methods outperform DTI metrics either in being more sensitive to poststroke changes or by better predicting outcome motor scores. More and larger studies are needed to confirm the improved prediction of stroke recovery with the beyond-DTI methods.
Topics: Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; Ischemic Stroke; Stroke
PubMed: 35272983
DOI: 10.3174/ajnr.A7414 -
Magnetic Resonance in Medicine Dec 2021To use diffusion measurements to map the spatial dependence of the magnetic field produced by the gradient coils of an MRI scanner with sufficient accuracy to correct...
PURPOSE
To use diffusion measurements to map the spatial dependence of the magnetic field produced by the gradient coils of an MRI scanner with sufficient accuracy to correct errors in quantitative diffusion MRI (DMRI) caused by gradient nonlinearity and gradient amplifier miscalibration.
THEORY AND METHODS
The field produced by the gradient coils is expanded in regular solid harmonics. The expansion coefficients are found by fitting a model to a minimum set of diffusion-weighted images of an isotropic diffusion phantom. The accuracy of the resulting gradient coil field maps is evaluated by using them to compute corrected b-matrices that are then used to process a multi-shell diffusion tensor imaging (DTI) dataset with 32 diffusion directions per shell.
RESULTS
The method substantially reduces both the spatial inhomogeneity of the computed mean diffusivities (MD) and the computed values of the fractional anisotropy (FA), as well as virtually eliminating any artifactual directional bias in the tensor field secondary to gradient nonlinearity. When a small scaling miscalibration was purposely introduced in the x, y, and z, the method accurately detected the amount of miscalibration on each gradient axis.
CONCLUSION
The method presented detects and corrects the effects of gradient nonlinearity and gradient gain miscalibration using a simple isotropic diffusion phantom. The correction would improve the accuracy of DMRI measurements in the brain and other organs for both DTI and higher order diffusion analysis. In particular, it would allow calibration of MRI systems, improving data harmony in multicenter studies.
Topics: Anisotropy; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Magnetic Resonance Imaging; Phantoms, Imaging
PubMed: 34351007
DOI: 10.1002/mrm.28890 -
Frontiers in Endocrinology 2022To noninvasively evaluate the three-dimensional collagen fiber architecture of porcine meniscus using diffusion MRI, meniscal specimens were scanned using a 3D...
To noninvasively evaluate the three-dimensional collagen fiber architecture of porcine meniscus using diffusion MRI, meniscal specimens were scanned using a 3D diffusion-weighted spin-echo pulse sequence at 7.0 T. The collagen fiber alignment was revealed in each voxel and the complex 3D collagen network was visualized for the entire meniscus using tractography. The proposed automatic segmentation methods divided the whole meniscus to different zones (Red-Red, Red-White, and White-White) and different parts (anterior, body, and posterior). The diffusion tensor imaging (DTI) metrics were quantified based on the segmentation results. The heatmap was generated to investigate the connections among different regions of meniscus. Strong zonal-dependent diffusion properties were demonstrated by DTI metrics. The fractional anisotropy (FA) value increased from 0.13 (White-White zone) to 0.26 (Red-Red zone) and the radial diffusivity (RD) value changed from 1.0 × 10 mm/s (White-White zone) to 0.7 × 10 mm/s (Red-Red zone). Coexistence of both radial and circumferential collagen fibers in the meniscus was evident by diffusion tractography. Weak connections were found between White-White zone and Red-Red zone in each part of the meniscus. The anterior part and posterior part were less connected, while the body part showed high connections to both anterior part and posterior part. The tractography based on diffusion MRI may provide a complementary method to study the integrity of meniscus and nondestructively visualize the 3D collagen fiber architecture.
Topics: Animals; Anisotropy; Collagen; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Meniscus; Swine
PubMed: 35620393
DOI: 10.3389/fendo.2022.876784 -
AJNR. American Journal of Neuroradiology Jul 2022Verbal declarative memory performance relies on frontotemporal connectivity. The uncinate fasciculus is a major association tract connecting the frontal and temporal...
BACKGROUND AND PURPOSE
Verbal declarative memory performance relies on frontotemporal connectivity. The uncinate fasciculus is a major association tract connecting the frontal and temporal lobes. Hemispheric asymmetries contribute to various cognitive and neurobehavioral abilities. Here we investigated microstructural alterations and hemispheric asymmetry of the uncinate fasciculus and their possible correlation to memory performance of children with learning disorders attributed to verbal memory deficits.
MATERIALS AND METHODS
Two groups of right-handed children with learning disorders attributed to verbal memory deficits and typically developing children ( = 20 and 22, respectively) underwent DTI on a 1.5T scanner. Tractography of the uncinate fasciculus in both hemispheres was performed, and fractional anisotropy and diffusivity indices (radial diffusivity, axial diffusivity, and trace) were obtained. The asymmetry index was calculated. Verbal memory was assessed using subsets of the Stanford Binet Intelligence Scale, 4th edition, a dyslexia assessment test, and the Illinois test of Psycholinguistic Abilities. Correlation between diffusion metrics and verbal memory performance was investigated in the learning disorders group. Also, hemispheric differences in each group were tested, and between-group comparisons were performed.
RESULTS
Children with learning disorders showed absence of the normal left-greater-than-right asymmetry of fractional anisotropy and the normal right-greater-than-left asymmetry of radial diffusivity seen in typically developing children. Correlation with verbal memory subsets revealed that the higher the fractional anisotropy and asymmetry index, the better the rapid naming performance ( <.05) was.
CONCLUSIONS
These findings demonstrated microstructural aberrations with reduction of hemispheric asymmetry of the uncinate fasciculus, which could disrupt the normal frontotemporal connectivity in children with learning disorders attributed to verbal memory deficits. This outcome gives more understanding of pathologic mechanisms underlying this disorder.
Topics: Anisotropy; Child; Diffusion Tensor Imaging; Humans; Learning Disabilities; Memory Disorders; Uncinate Fasciculus; White Matter
PubMed: 35680160
DOI: 10.3174/ajnr.A7535