White Matter Fiber Orientation Research Articles

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.

Anisotropy of the R1/T2* value dependent on white matter fiber orientation with respect to the B0 field

The R1 (1/T1) map divided by the T2* map (R1/T2* map) draws attention as a high-resolution myelin-related map. However, both R1 and R2* (1/T2*) values demonstrate anisotropy dependent on the white matter (WM) fiber orientation with respect to the static magnetic (B0) field. Therefore, this study primarily aimed to investigate the comprehensive impact of these angular-dependent anisotropies on the R1/T2* value.This study enrolled 10 healthy human volunteers (age = 25 ± 1.3) on the 3.0 T MRI system. For R1/T2* map calculation, whole brain R1 and T2* maps were repeatedly obtained in three head tilt positions by magnetization-prepared two rapid gradient echoes and multiple spoiled gradient echo sequences, respectively. Afterward, all maps were spatially normalized and registered to the Johns Hopkins University WM atlas. R1/T2*, R1, and R2* values were binned for fiber orientation related to the B0 field, which was estimated from diffusion-weighted echo-planar imaging data with 3° intervals, to investigate angular-dependent anisotropies in vivo.A larger change in the R1/T2* value in the global WM region as a function of fiber orientation with respect to the B0 field was observed compared to the R1 and R2* values alone. The minimum R1/T2* value at the near magic-angle range was 18.86% lower than the maximum value at the perpendicular angle range. Furthermore, R1/T2* values in the corpus callosum tract and the right and left cingulum cingulate gyrus tracts changed among the three head tilt positions due to fiber orientation changes. In conclusion, the R1/T2* value demonstrates distinctive and complicated angular-dependent anisotropy indicating the trends of both R1 and R2* values and may provide supplemental information for detecting slight changes in the microstructure of myelin and axons.

An image-informed Cahn–Hilliard Keller–Segel multiphase field model for tumor growth with angiogenesis

In this paper we develop a new four-phase tumor growth model with angiogenesis, derived from a diffuse-interface mixture model composed by a viable tumor component, a necrotic component, a liquid component and an angiogenetic component, coupled with two massless chemicals representing a perfectly diluted nutrient and an angiogenetic factor. This model is derived from variational principles complying with the second law of thermodynamics in isothermal situations, starting from biological constitutive assumptions on the tumor cells adhesion properties and on the infiltrative mechanics of tumor-induced vasculature in the tumor tissues, and takes the form of a coupled degenerate Cahn–Hilliard Keller–Segel system for the mixture components with reaction diffusion equations for the chemicals. The model is informed by neuroimaging data, which give informations about the patient-specific brain geometry and tissues microstructure, the distribution of the different tumor components, the white matter fiber orientations and the vasculature density. We describe specific and robust preprocessing steps to extract quantitative informations from the neuroimaging data and to construct a computational platform to solve the model on a patient-specific basis. We further introduce a finite element approximation of the model equations which preserve the qualitative properties of the continuous solutions. Finally, we show simulation results for the patient-specific tumor evolution of a patient affected by GlioBlastoma Multiforme, considering two different test cases before surgery, corresponding to situations with high or low nutrient supply inside the tumor, and a test case after surgery. We further perform a sensitivity based patient-specific parameters estimation based on longitudinal neuroimaging data for a test case with acquired data at two time points before surgery. We show that our model correctly predicts the overall extension of the tumor distribution and the intensity of the angiogenetic process, paving the way for assisting the clinicians in properly assessing the therapy outcomes and in designing optimal patient-specific therapeutic schedules.

Fiber direction estimation using constrained spherical deconvolution based on multi-model response function

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

Myelin biomarkers in the healthy adult brain: Correlation, reproducibility, and the effect of fiber orientation.

We investigated the correlation, reproducibility, and effect of white matter fiber orientation for three myelin-sensitive MRI techniques: magnetization transfer ratio (MTR), inhomogeneous magnetization transfer ratio (ihMTR), and gradient and spin echo-derived myelin water fraction (MWF). We measured the three metrics in 17 white and three deep grey matter regions in 17 healthy adults at 3 T. We found a strong correlation between ihMTR and MTR (r=0.70, p < 0.001) and ihMTR and MWF (r=0.79, p < 0.001), and a weaker correlation between MTR and MWF (r=0.54, p < 0.001). The dynamic range in white matter was greatest for MWF (2.0%-27.5%), followed by MTR (14.4%-23.2%) and then ihMTR (1.2%-5.4%). The average scan-rescan coefficient of variation for white matter regions was 0.6% MTR, 0.3% ihMTR, and 0.7% MWF in metric units; however, when adjusted by the dynamic range, these became 6.3%, 6.1% and 2.8%, respectively. All three metrics varied with fiber direction: MWF and ihMTR were lower in white matter fibers perpendicular to B0 by 6% and 1%, respectively, compared with those parallel, whereas MTR was lower by 0.5% at about 40°, with the highest values at 90°. However, separating the apparent orientation dependence by white matter region revealed large dissimilarities in the trends, suggesting that real differences in myelination between regions are confounding the apparent orientation dependence measured using this method. The strong correlation between ihMTR and MWF suggests that these techniques are measuring the same myelination; however, the larger dynamic range of MWF may provide more power to detect small differences in myelin.

Backscattering Mueller Matrix polarimetry on whole brain specimens shows promise for minimally invasive mapping of microstructural orientation features

Understanding microscale physiology and microstructural cellular features of the brain is key to understanding mechanisms of neurodegenerative diseases and injury, as well as prominent changes undergone in development and aging. Non-invasive imaging modalities sensitive to the microscale, especially diffusion magnetic resonance imaging (dMRI), are promising for mapping of cellular microstructure of brain tissues; however, there is a need for robust validation techniques to verify and improve the biological accuracy of information derived. Recent advances in dMRI have moved toward probing of the more complex grey matter architecture, challenging current validation techniques, which are largely based on ex vivo staining and microscopy focusing on white matter. Polarized light imaging (PLI) has been shown to be successful for high resolution, direct, microstructural imaging and has been applied to dMRI validation with clear advantages over staining and microscopy techniques. Conventionally, PLI is applied to thin, sectioned samples in transmission mode, but PLI has also been extended to operate in reflectance mode to bridge the gap toward in vivo measurements of the brain. In this report we investigate the use of backscattering Mueller Matrix polarimetry to characterize the microstructural content of intact ferret brain specimens. The results show that backscattering polarimetry can probe white matter fiber coherence and fiber orientation, and show promise for probing grey matter microstructure. Ultimately, this motivates further study to fully understand how best to implement backscattering polarimetry for in vivo microstructural imaging of the brain.

Regularized Asymmetric Susceptibility Tensor Imaging in the Human Brain in Vivo.

Susceptibility tensor imaging (STI) is a promising tool for studying orientation-dependent tissue magnetic susceptibility and for mapping white matter fiber orientations complementary to diffusion tensor imaging (DTI). However, the limited head rotation range within modern head coils for data acquisition makes in vivo STI reconstruction ill-conditioned. Conventional STI reconstruction method is usually vulnerable to noise and requires sufficiently large head rotations to solve this ill-conditioned inverse problem. In this study, based on the recently proposed asymmetric STI (aSTI) model, a new method termed aSTI+ was proposed to improve in vivo STI reconstruction by enforcing isotropic susceptibility tensor inside cerebrospinal fluid (CSF) and applying morphology constraint in white matter. Experimental results showed superior performance of the proposed method with reduced noise, improved tissue contrast and better fiber orientation estimation over previous methods. Thus aSTI+ may promote in vivo human brain STI studies on white matter and myelin-related brain diseases.

Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor-valued diffusion MRI.

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of high angular resolution diffusion imaging (HARDI) data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), enabling the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). Recent work on making CSD compatible with tensor-valued diffusion MRI data opens the door for methods combining CSD and DIVIDE to get both fODFs and microstructure measures. However, the impacts of such atypical data on fODF reconstruction with CSD are yet to be fully known and understood. In this work, we use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs and on the versatility of CSD in multiple realistic situations. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimized 10min protocol combining linear and spherical b-tensor encodings for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

Is Diffusion Tensor Imaging-Guided Radiotherapy the New State-of-the-Art? A Review of the Current Literature and Technical Insights

Despite the increasing precision of radiotherapy delivery, it is still frequently associated with neurological complications. This is in part due to damage to eloquent white matter (WM) tracts, which is made more likely by the fact they cannot be visualised on standard structural imaging. WM is additionally more vulnerable than grey matter to radiation damage. Primary brain malignancies also are known to spread along the WM. Diffusion tensor imaging (DTI) is the only in vivo method of delineating WM tracts. DTI is an imaging technique that models the direction of diffusion and therefore can infer the orientation of WM fibres. This review article evaluates the current evidence for using DTI to guide intracranial radiotherapy and whether it constitutes a new state-of-the-art technique. We provide a basic overview of DTI and its known applications in radiotherapy, which include using tractography to reduce the radiation dose to eloquent WM tracts and using DTI to detect or predict tumoural spread. We evaluate the evidence for DTI-guided radiotherapy in gliomas, metastatic disease, and benign conditions, finding that the strongest evidence is for its use in arteriovenous malformations. However, the evidence is weak in other conditions due to a lack of case-controlled trials.

Multi-tissue spherical deconvolution of tensor-valued diffusion MRI

Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.

Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment.

White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer's disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called “Director Field Analysis” (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto‐occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.

Geometric microstructural damage of white matter with functional compensation in post-stroke

Background and purposeSubcortical ischemic stroke usually leads to the geometric microstructural changes in the orientation of peri-infarct white matter fiber. We conducted the study to determine the microstructural changes in the white matter fiber orientation in post stroke patients with and without cognitive impairment (PSCI, NPSCI), and to investigate the impact of peri-infarct white matter damage on the morphology and functional connectivity of their projective cerebral regions. MethodsA novel mathematical framework called Director Field Analysis (DFA) was applied to study the microstructural changes in the orientation of white matter fiber in PSCI (n = 23), NPSCI (n = 17), and cognitively normal (CN, n = 29) individuals. ResultsPSCI patients had extensive abnormalities in the orientation of white matter fiber in the corpus callosum, bilateral internal capsule, external capsule, forceps major, forceps minor, and corticospinal tract in comparison with NPSCI and CN. NPSCI patients also showed significant increases in bend and twist of white matter fiber orientation in the internal capsule in comparison with CN. Seed-based functional connectivity analysis showed that peri-infarct white matter deficits indicate a significant impact on functional connectivity with related cortical regions, suggesting the coexistence of impairment and compensation in post-stroke. In addition, these peri-infarct white matter damages and abnormal functional connectivity were significantly correlated with cognitive scores. Machine learning model also indicated that these changes in white matter fiber orientation and functional connectivity can predict the cognitive status in post-stroke. ConclusionsPost-stroke patients experienced pathological damage in the orientation of peri-infarct white matter fiber. The peri-infarct white matter damage may further induce the abnormal functional connectivity in projective cerebral regions. These degenerations of peri-infarct white matter fiber and associated functional connectivity changes may mediate the cognitive impairment in post-stroke.

In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition

Multiple studies have reported a significant dependence of the effective transverse relaxation rate constant (R2*) and the phase of gradient-echo based (GRE) signal on the orientation of white matter fibres in the human brain. It has also been hypothesized that magnetic susceptibility, as obtained by single-orientation quantitative susceptibility mapping (QSM), exhibits such a dependence. In this study, we investigated this hypothesized relationship in a cohort of healthy volunteers. We show that R2* follows the predicted orientation dependence consistently across white matter regions, whereas the apparent magnetic susceptibility is related differently to fibre orientation across the brain and often in a complex non-monotonic manner. In addition, we explored the effect of fractional anisotropy measured by diffusion-weighted MRI on the strength of the orientation dependence and observed only a limited influence in many regions. However, with careful consideration of such an impact and the limitations imposed by the ill-posed nature of the dipole inversion process, it is possible to study magnetic susceptibility anisotropy in specific brain regions with a single orientation acquisition.

Early micro- and macrostructure of sensorimotor tracts and development of cerebral palsy in high risk infants.

Infants born very preterm (VPT) are at high risk of motor impairments such as cerebral palsy (CP), and diagnosis can take 2 years. Identifying in vivo determinants of CP could facilitate presymptomatic detection and targeted intervention. Our objectives were to derive micro‐ and macrostructural measures of sensorimotor white matter tract integrity from diffusion MRI at term‐equivalent age, and determine their association with early diagnosis of CP. We enrolled 263 VPT infants (≤32 weeks gestational age) as part of a large prospective cohort study. Diffusion and structural MRI were acquired at term. Following consensus guidelines, we defined early diagnosis of CP based on abnormal structural MRI at term and abnormal neuromotor exam at 3–4 months corrected age. Using Constrained Spherical Deconvolution, we derived a white matter fiber orientation distribution (fOD) for subjects, performed probabilistic whole‐brain tractography, and segmented nine sensorimotor tracts of interest. We used the recently developed fixel‐based (FB) analysis to compute fiber density (FD), fiber‐bundle cross‐section (FC), and combined fiber density and cross‐section (FDC) for each tract. Of 223 VPT infants with high‐quality diffusion MRI data, 14 (6.3%) received an early diagnosis of CP. The cohort's mean (SD) gestational age was 29.4 (2.4) weeks and postmenstrual age at MRI scan was 42.8 (1.3) weeks. FD, FC, and FDC for each sensorimotor tract were significantly associated with early CP diagnosis, with and without adjustment for confounders. Measures of sensorimotor tract integrity enhance our understanding of white matter changes that antecede and potentially contribute to the development of CP in VPT infants.

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI

Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.

Fiber orientation distribution from diffusion MRI: Effects of inaccurate response function calibration.

ABSTRACTBackground and PurposeDiffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Spherical deconvolution requires to define––or derive from the data––a response function, which is used to compute the fiber orientation distribution (FOD). Different characteristics of the response function are expected to affect the FOD computation and the subsequent fiber tracking.MethodsIn this work, we explored the effects of inaccuracies in the shape factors of the response function on the FOD characteristics.ResultsWith simulations, we show that the apparent fiber density could be doubled in the presence of underestimated shape factors in the response functions, whereas the overestimation of the shape factor will cause more spurious peaks in the FOD, especially when the signal‐to‐noise ratio is below 15. Moreover, crossing fiber populations with a separation angle smaller than 60° were more sensitive to inaccuracies in the response function than fiber populations with more orthogonal separation angles. Results with in vivo data demonstrate angular deviations in the FODs and spurious peaks as a result of modified shape factors of the response function, while the reconstruction of the main parts of fiber bundles is well preserved.ConclusionsThis work sheds light on how specific aspects of the response function shape can affect the estimated FODs, and highlights the importance of a proper calibration/definition of the response function.

Sensitivity of fiber orientation dependent to temperature and post mortem interval.

imaging of brain white matter is well known for being sensitive to the orientation of nerve fibers with respect to the B0 field of the MRI scanner. The goal of this study was to evaluate whether and to which extent fiber orientation dependent differs between in vivo and post mortem in situ examinations, and to investigate the influence of varying temperatures and post mortem intervals (PMI). Post mortem in situ and in vivo MRI scans were conducted at 3T. was acquired with a multi-echo gradient-echo sequence, and the orientation of white matter fibers was computed using diffusion tensor imaging (DTI). Fitting of the measured fiber orientation dependent was performed using three different formulations of a previously proposed model. increased with increasing fiber angle for in vivo and post mortem in situ examinations, whereby the orientation dependency was lower post mortem. The different formulations of the fiber orientation model resulted in an identical fit, but showed large variations of the estimated parameters. The higher order orientation dependent components significantly decreased with decreasing temperature, while the orientation independent components showed no significant correlation with either temperature or PMI. Although the mean diffusivity is strongly reduced post mortem, we could successfully estimate the fiber angle using DTI. Due to the strong correlation of the higher order orientation dependent components with temperature, the decreased fiber orientation dependency post mortem in situ might primarily be attributed to the lower brain temperature.

Constrained spherical deconvolution of nonspherically sampled diffusion MRI data.

Constrained spherical deconvolution (CSD) of diffusion‐weighted MRI (DW‐MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW‐MRI signal on the sphere can be represented as the spherical convolution of a single‐fiber response function (RF) and the fODF, and recovers the fODF through the inverse operation. CSD approaches typically require that the DW‐MRI data is sampled shell‐wise, and estimate the RF in a purely spherical manner using spherical basis functions, such as spherical harmonics (SH), disregarding any radial dependencies. This precludes analysis of data acquired with nonspherical sampling schemes, for example, Cartesian sampling. Additionally, nonspherical sampling can also arise due to technical issues, for example, gradient nonlinearities, resulting in a spatially dependent bias of the apparent tissue densities and connectivity information. Here, we adopt a compact model for the RFs that also describes their radial dependency. We demonstrate that the proposed model can accurately predict the tissue response for a wide range of b‐values. On shell‐wise data, our approach provides fODFs and tissue densities indistinguishable from those estimated using SH. On Cartesian data, fODF estimates and apparent tissue densities are on par with those obtained from shell‐wise data, significantly broadening the range of data sets that can be analyzed using CSD. In addition, gradient nonlinearities can be accounted for using the proposed model, resulting in much more accurate apparent tissue densities and connectivity metrics.

Myelin water imaging depends on white matter fiber orientation in the human brain

PurposeThe multi‐exponential T2 decay of the MRI signal from cerebral white matter can be separated into short T2 components related to myelin water and long T2 components related to intracellular and extracellular water. In this study, we investigated to what degree the apparent myelin water fraction (MWF) depends on the angle between white matter fibers and the main magnetic field.MethodsMaps of the apparent MWF were acquired using multi‐echo Carr‐Purcell‐Meiboom‐Gill and gradient‐echo spin‐echo sequences. The Carr‐Purcell‐Meiboom‐Gill sequence was acquired with a TR of 1073 ms, 1500 ms, and 2000 ms. The fiber orientation was mapped with DTI. By angle‐wise pooling the voxels across the brain’s white matter, orientation‐dependent apparent MWF curves were generated.ResultsWe found that the apparent MWF varied between 25% and 35% across different fiber orientations. Furthermore, the selection of the TR influences the apparent MWF.ConclusionWhite matter fiber orientation induces a strong systematic bias on the estimation of the apparent MWF. This finding has implications for future research and the interpretation of MWI results in previously published studies.

Role of diffusion tensor imaging and diffusion metrics in primary, secondary brain tumours and tumour mimics

Background and Aim: Diffusion Tensor Imaging studies provide a valuable information regarding the status of white matter tracts adjacent to the intracranial brain lesions. The quantitative nature of Diffusion Tensor Imaging will play a role in assessing the outcome of clinical trials, as an additional surrogate marker in monitoring the therapeutic response. The aim of our study were to determine the Mean Diffusivity and Fractional Anisotropy coefficients in the affected white matter tracts and thereby to compare the Fractional Anisotropy and Mean Diffusivity of the affected white matter tracts in primary, secondary brain tumours and tumour like lesions.Materials and Methods: From December 2018 to September 2019, 49 patients were included in the study who were referred to the department for conventional Magnetic Resonance Imaging. The histopathological diagnosis was obtained and confirmed in patients who have undergone surgical resection.Results: Diffusion Tensor Imaging studies provide a valuable information regarding the status of white matter tracts adjacent to the intra cranial brain lesions. Fractional Anisotropy depends on the orientation and density of white matter fibres and Mean Diffusivity depends on the degree of perilesional edema. Gliomas present with white matter fibre destruction and less vasogenic edema when compared to tumour like lesions, and a higher perilesional Apparent Diffusion Coefficient favours a non-neoplastic etiology. Thus, the Diffusion Tensor Imaging sequences may help in differentiating the brain lesions of different aetiologies.Conclusion: The quantitative nature of Diffusion Tensor Imaging will play a role in assessing the outcome of clinical trials, as an additional surrogate marker in monitoring the therapeutic response. Careful studies to validate Diffusion Tensor Imaging and its metrics will allow it to become more applicable clinically and can affect therapeutic decision-making, choosing appropriate treatment and eventually patient outcome.