Diffusion Gradient Directions Research Articles

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

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

Brain MRI diffusion abnormalities associated with arteriolosclerosis pathology

AbstractBackgroundBrain arteriolosclerosis is one of the main pathologies of cerebral small vessel disease. It involves the thickening of the vessel wall and stenosis of arterioles causing lower cognitive and motor function, and higher odds of dementia. This work intends to explore the independent association of brain arteriolosclerosis with diffusion abnormalities in the white matter (WM) of community‐based older adults.MethodParticipants comprised community‐dwelling older adults (N = 175) from four studies of aging (Rush Memory and Aging Project, Religious Orders Study, Minority Aging Research Study, and Clinical Core of the Rush Alzheimer’s Disease Research Center) (Fig.1A). Whole brain 3D T1w MPRAGE, T2w FLAIR, and diffusion‐weighted MRI (2×2×2mm3, b = 1,000s/mm2 for 40 diffusion gradient directions, and 6 b = 0s/mm2 volumes) data were acquired in‐vivo using 3T MRI scanners. The fractional anisotropy (FA) and trace maps were transformed to the space of the IIT Human Brain Atlas v.5.0 and projected onto the WM skeleton of the IIT atlas. Following a participant’s death, the extracted brain underwent a detailed evaluation by a neuropathologist. Assessed neuropathologies included arteriolosclerosis, Alzheimer’s pathology, LATE‐NC, gross and microscopic infarcts, atherosclerosis, and cerebral amyloid angiopathy (Fig.1B). Linear regression was used in voxels of the WM skeleton to test the association of FA or trace with the severity of arteriolosclerosis controlling for all other neuropathologies, white matter hyperintensities (WMH), demographic variables (age at scan, sex, years of education), antemortem interval to imaging, and scanner. The statistical analyses were carried out using FSL’s PALM tool with 5,000 permutations and threshold‐free cluster enhancement. The significance level was set at p<0.05, after family‐wise error rate (FWER) correction for multiple testing.ResultArteriolosclerosis score was associated with lower FA (Fig.2) and higher trace (Fig.3) of the diffusion tensor throughout the brain in multiple WM regions, even in areas typically free of WMH. Other neuropathologies or the effects of WMH on diffusion characteristics had no impact on these relationships.ConclusionThe current study showed that, regardless of the impact of other neuropathologies or visible WMH, brain arteriolosclerosis in older persons is associated with diffusion abnormalities that extend throughout the WM.

Brain MRI diffusion abnormalities associated with arteriolosclerosis pathology

AbstractBackgroundBrain arteriolosclerosis is one of the main pathologies of cerebral small vessel disease. It involves the thickening of the vessel wall and stenosis of arterioles causing lower cognitive and motor function, and higher odds of dementia. This work intends to explore the independent association of brain arteriolosclerosis with diffusion abnormalities in the white matter (WM) of community‐based older adults.MethodParticipants comprised community‐dwelling older adults (N = 175) from four studies of aging (Rush Memory and Aging Project, Religious Orders Study, Minority Aging Research Study, and Clinical Core of the Rush Alzheimer’s Disease Research Center) (Fig.1A). Whole brain 3D T1w MPRAGE, T2w FLAIR, and diffusion‐weighted MRI (2×2×2mm3, b = 1,000s/mm2 for 40 diffusion gradient directions, and 6 b = 0s/mm2 volumes) data were acquired in‐vivo using 3T MRI scanners. The fractional anisotropy (FA) and trace maps were transformed to the space of the IIT Human Brain Atlas v.5.0 and projected onto the WM skeleton of the IIT atlas. Following a participant’s death, the extracted brain underwent a detailed evaluation by a neuropathologist. Assessed neuropathologies included arteriolosclerosis, Alzheimer’s pathology, LATE‐NC, gross and microscopic infarcts, atherosclerosis, and cerebral amyloid angiopathy (Fig.1B).Linear regression was used in voxels of the WM skeleton to test the association of FA or trace with the severity of arteriolosclerosis controlling for all other neuropathologies, white matter hyperintensities (WMH), demographic variables (age at scan, sex, years of education), antemortem interval to imaging, and scanner. The statistical analyses were carried out using FSL’s PALM tool with 5,000 permutations and threshold‐free cluster enhancement. The significance level was set at p<0.05, after family‐wise error rate (FWER) correction for multiple testing.ResultArteriolosclerosis score was associated with lower FA (Fig.2) and higher trace (Fig.3) of the diffusion tensor throughout the brain in multiple WM regions, even in areas typically free of WMH. Other neuropathologies or the effects of WMH on diffusion characteristics had no impact on these relationships.ConclusionThe current study showed that, regardless of the impact of other neuropathologies or visible WMH, brain arteriolosclerosis in older persons is associated with diffusion abnormalities that extend throughout the WM.

Node2Node: Self-Supervised Cardiac Diffusion Tensor Image Denoising Method

Although the existing cardiac diffusion tensor imaging (DTI) denoising methods have achieved promising results, most of them are dependent on the number of diffusion gradient directions, noise distributions, and noise levels. To address these issues, we propose a novel self-supervised cardiac DTI denoising network, Node2Node, which firstly expresses the diffusion-weighted (DW) image volumes along different directions as a graph, then the graph framelet transform (GFT) is implemented to map the DW signals into the GFT coefficients at different spectral bands, allowing us to accurately match the DW image pairs. After that, using the matched image pairs as input and target, a ResNet-like network is used to denoise in a self-supervised manner. In addition, a novel edge-aware loss based on pooling operation is proposed to retain the edge. Through comparison with several state-of-the-art methods on synthetic, ex vivo porcine, and in vivo human cardiac DTI datasets, we showed that the root mean square error (RMSE) of DW images and the average angular error (AAE) of fiber orientations obtained using Node2Node are the smallest, improved by 47.5% and 23.7%, respectively, on the synthetic dataset, demonstrating that Node2Node is not sensitive to the properties of the dataset.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions.

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

Correlation between diffusion tensor indices and fascicular morphometric parameters of peripheral nerve.

Introduction: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that measures the anisotropy of water diffusion. Clinical magnetic resonance imaging scanners enable visualization of the structural integrity of larger axonal bundles in the central nervous system and smaller structures like peripheral nerves; however, their resolution for the depiction of nerve fascicular morphology is limited. Accordingly, high-field strength MRI and strong magnetic field gradients are needed to depict the fascicular pattern. The study aimed to quantify diffusion tensor indices with high-field strength MRI within different anatomical compartments of the median nerve and determine if they correlate with nerve structure at the fascicular level. Methods: Three-dimensional pulsed gradient spin-echo (PGSE) imaging sequence in 19 different gradient directions and b value 1,150s/mm2 was performed on a 9.4T wide-bore vertical superconducting magnet. Nine-millimeter-long segments of five median nerve samples were obtained from fresh cadavers and acquired in sixteen 0.625mm thick slices. Each nerve sample had the fascicles, perineurium, and interfascicular epineurium segmented. The diffusion tensor was calculated from the region-average diffusion-weighted signals for all diffusion gradient directions. Subsequently, correlations between diffusion tensor indices of segmentations and nerve structure at the fascicular level (number of fascicles, fascicular ratio, and cross-sectional area of fascicles or nerve) were assessed. The acquired diffusion tensor imaging data was employed for display with trajectories and diffusion ellipsoids. Results: The nerve fascicles proved to be the most anisotropic nerve compartment with fractional anisotropy 0.44 ± 0.05. In the interfascicular epineurium, the diffusion was more prominent in orthogonal directions with fractional anisotropy 0.13 ± 0.02. Diffusion tensor indices within the fascicles and perineurium differed significantly between the subjects (p < 0.0001); however, there were no differences within the interfascicular epineurium (p ≥ 0.37). There were no correlations between diffusion tensor indices and nerve structure at the fascicular level (p ≥ 0.29). Conclusion: High-field strength MRI enabled the depiction of the anisotropic diffusion within the fascicles and perineurium. Diffusion tensor indices of the peripheral nerve did not correlate with nerve structure at the fascicular level. Future studies should investigate the relationship between diffusion tensor indices at the fascicular level and axon- and myelin-related parameters.

Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition.

The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data from the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 min and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm2 and six non-collinear diffusion gradient directions for each b-value. Seven different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi2 ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi2 values were found using the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a nine-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.

Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI.

Quantitative diffusion weighted MRI in the abdomen provides important markers of disease, however significant limitations exist for its accurate computation. One such limitation is the low signal-to-noise ratio, particularly at high diffusion b-values. To address this, multiple diffusion directional images can be collected at each b-value and geometrically averaged, which invariably leads to longer scan time, blurring due to motion and other artifacts. We propose a novel parameter estimation technique based on self supervised diffusion denoising probabilistic model that can effectively denoise diffusion weighted images and work on single diffusion gradient direction images. Our source code is made available at https://github.com/quin-med-harvard-edu/ssDDPM.

Registration and quantification network (RQnet) for IVIM-DKI analysis in MRI.

A deep learning method is proposed for aligning diffusion weighted images (DWIs) and estimating intravoxel incoherent motion-diffusion kurtosis imaging parameters simultaneously. We propose an unsupervised deep learning method that performs 2 tasks: registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. A common registration method in diffusion MRI is based on minimizing dissimilarity between various DWIs, which may result in registration errors due to different contrasts in different DWIs. We designed a novel unsupervised deep learning method for both accurate registration and quantification of various diffusion parameters. In order to generate motion-simulated training data and test data, 17 volunteers were scanned without moving their heads, and 4 volunteers moved their heads during the scan in a 3 Tesla MRI. In order to investigate the applicability of the proposed method to other organs, kidney images were also obtained. We compared the registration accuracy of the proposed method, statistical parametric mapping, and a deep learning method with a normalized cross-correlation loss. In the quantification part of the proposed method, a deep learning method that considered the diffusion gradient direction was used. Simulations and experimental results showed that the proposed method accurately performed registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. The registration accuracy of the proposed method was high for all b values. Furthermore, quantification performance was analyzed through simulations and in vivo experiments, where the proposed method showed the best performance among the compared methods. The proposed method aligns the DWIs and accurately quantifies the intravoxel incoherent motion-diffusion kurtosis imaging parameters.

Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence

This work focuses on obtaining a magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T1 and transverse T2⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T1 and T2⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences.

DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value.

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI) and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets and a public dataset from Human Connection Project. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.

On the down-sampling of diffusion MRI data along the angular dimension

BackgroundIt has been established that the diffusion gradient directions in diffusion MRI should be uniformly distributed in 3D spherical space, so that orientation-dependent diffusion properties (e.g., fractional anisotropy or FA) can be properly quantified. Sometimes the acquired data need to be down-sampled along the angular dimension before computing diffusion properties (e.g., to exclude data points corrupted by motion artifact; to harmonize data obtained with different protocols). It is important to quantitatively assess the impact of data down-sampling on measurement of diffusion properties. Materials and methodsHere we report 1) a numerical procedure for down-sampling diffusion MRI (e.g., for data harmonization), and 2) a spatial uniformity index of diffusion directions, aiming to predict the quality of the chosen down-sampling schemes (e.g., from data harmonization; or rejection of motion corrupted data points). We quantitatively evaluated human diffusion MRI data, which were down-sampled from 64 or 60 diffusion gradient directions to 30 directions, in terms of their 1) FA value accuracy (using fully-sampled data as the ground truth), 2) FA fitting residuals, and 3) spatial uniformity indices. ResultsOur experimental data show that the proposed spatial uniformity index is correlated with errors in FA obtained from down-sampled diffusion MRI data. The FA fitting residuals that are typically used to assess diffusion MRI quality are not correlated with either FA errors or spatial uniformity index. ConclusionsThese results suggest that the spatial uniformity index could be more valuable in assessing quality of down-sampled diffusion MRI data, as compared with FA fitting residual measures. We expect that our implemented software procedure should prove valuable for 1) guiding data harmonization for multi-site diffusion MRI studies, and 2) assessing the impact of rejecting motion corrupted data points on the accuracy of diffusion measures.

Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint

To investigate the influences of the diffusion gradient directions (angular resolution) and the strength of the diffusion gradient (b value) on diffusion tensor imaging (DTI) metrics and tractography of various connective tissues in knee joint. Two rat knee joints were scanned on a preclinical 9.4-T system using a 3-dimensional diffusion-weighted spin echo pulse sequence. One protocol with b value of 500, 1500, and 2500 s/mm2 were acquired separately using 43 diffusion gradient directions. The other protocol with b value of 1000 s/mm2 was performed using 147 diffusion gradient directions. The in-plane resolution was 45 µm isotropic. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate. The ligament showed higher FA value compared with growth plate and cartilage. The FA values were largely overestimated at the angular resolution of 6. Compared with FA, MD showed less sensitivity to the angular resolution. The fiber tracking was failed at low angular resolution (6 diffusion gradient directions) or high b value (2500 s/mm2). The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value. To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm2) and sufficient angular resolution (>14) with signal-to-noise ratio >10.

Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI

BackgroundAchieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. PurposeMagnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study typeProspective observational study. SubjectsA spherical isotropic diffusion phantom, and a single human control volunteer. Field strength/sequence3 T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. AssessmentWe compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical testsWilcoxon rank-sum test between uncorrected and corrected data. ResultsIn phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p < 0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p < 0.05). ConclusionOur method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

DTI-MRI findings in synthetic cannabinoid users

Background/aimSynthetic cannabinoids (SCs) are full agonists of both cannabinoid receptors. Conventional magnetic resonance imaging (MRI) findings of SC users are mainly defined as diffusion restriction and T2/FLAIR hyperintensity. Diffusion tensor imaging (DTI) studies examining SC users have shown contradictory results. The aim of this study was to define white matter (WM) changes of SC users using DTI.Materials and methodsThe study included 22 patients with a history of using SC for 5–37 months, and 22 healthy, age and sex-matched control subjects. A total of 41 diffusion gradient directions were used in the acquisition of diffusion imaging data. Fractional anisotropy (FA) and apparent diffusion coefficients (ADC) values were obtained. ROIs were placed on WM areas of normal appearance. ResultsIn the SC users, significantly lower FA values were determined in the left temporal lobe (216.2 ± 58.9 vs. 263 ± 27.4; P = 0.002) and right hippocampus (224.5 ± 61.5 vs. 255 ± 24.3; P = 0.040). The ADC values of the hippocampus and temporal lobe were significantly higher than those of the control group on both the left and right sides.ConclusionThe SC use causes WM microstructural changes, especially in the hippocampus and temporal lobes. DTI is a useful tool to reveal WM changes in SC addicts and can be used earlier than conventional MRI.

A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study

Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB ​= ​2) dMRI protocol.The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 19F NMR probes. The time variation of 0th, 1st and 2nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps.19F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps.In conclusion, we demonstrate that up-to-2nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.

Pattern recognition analysis of directional intravoxel incoherent motion MRI in ischemic rodent brains.

This study aimed to demonstrate a reliable automatic segmentation method for independently separating reduced diffusion and decreased perfusion areas in ischemic stroke brains using constrained nonnegative matrix factorization (cNMF) pattern recognition in directional intravoxel incoherent motion MRI (IVIM-MRI). First, the feasibility of cNMF-based segmentation of IVIM signals was investigated in both simulations and in vivo experiments. The cNMF analysis was independently performed for S0 -normalized and scaled (by the difference between the maximum and minimum) IVIM signals, respectively. Segmentations of reduced diffusion (from S0 -normalized IVIM signals) and decreased perfusion (from scaled IVIM signals) areas were performed using the corresponding cNMF pattern weight maps. Second, Monte Carlo simulations were performed for directional IVIM signals to investigate the relationship between the degree of vessel alignment and the direction of the diffusion gradient. Third, directional IVIM-MRI experiments (x, y and z diffusion-gradient directions, 20 b values at 7 T) were performed for normal (n = 4), sacrificed (n = 1, no flow) and ischemic stroke models (n = 4, locally reduced flow). The results showed that automatic segmentation of the hypoperfused lesion using cNMF analysis was more accurate than segmentation using the conventional double-exponential fitting. Consistent with the simulation, the double-exponential pattern of the IVIM signals was particularly strong in white matter and ventricle regions when the directional flows were aligned with the applied diffusion-gradient directions. cNMF analysis of directional IVIM signals allowed robust automated segmentation of white matter, ventricle, vascular and hypoperfused regions in the ischemic brain. In conclusion, directional IVIM signals were simultaneously sensitive to diffusion and aligned flow and were particularly useful for automatically segmenting ischemic lesions via cNMF-based pattern recognition.

Feasibility of postoperative 3-tesla diffusion tensor imaging in cervical spondylotic myelopathy: A comparison of single-shot EPI and multi-shot EPI

PurposeTo explore the feasibility of postoperative high-tesla DTI in CSM and optimize its acquisition parameters using both single-shot (SS) echo-planar imaging (EPI) and multi-shot (MS) EPI, and to evaluate correlation between image degradation and operative methods. MethodWe enrolled twenty-seven patients with CSM scheduled for MRI at one month after cervical operations who were divided into three groups; 11 patients in group 1; 11 in group 2; and 5 in group 3. The patient in each group underwent two sets of DTI using both SS-EPI and MS-EPI with different diffusion gradient directions. Qualitative and quantitative analysis of fractional anisotropy (FA) and color-coding maps were performed to evaluate image distortion and spinal cord visualization and were compared between SS- and MS-EPI. DTI indices, including the number of reconstructed fibers, mean apparent diffusion coefficient (ADC) values, and mean FA values, were acquired. ResultsIn the metallic segment, MS-EPI with 6 diffusion gradients showed significantly less distortion and better cord visualization than SS-EPI. Fiber tracking was also superior with MS-EPI sequences. Scores in the subjective motor improvement scale showed a moderately positive correlation correlated only with the mean ADC at 1 month postoperatively. Regarding the operation methods, DTI in patients who underwent anterior cervical discectomy and fusion showed the least image distortion and 100 % success rate of fiber tractography. ConclusionsCompared with SS-EPI, MS-EPI with motion correction significantly improves image distortions and increases the success rate of fiber tractography in CSM patients with metal implants.

Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.

Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap. A systematic review of algorithms and tract reproducibility studies. Single healthy volunteers. 3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm2 with 20, 45, and 64 diffusion gradient directions per shell, respectively. Nine international groups submitted 46 tractography algorithm entries each consisting 16 tracts per scan. The algorithms were assessed using intraclass correlation (ICC) and the Dice similarity measure. Containment analysis was performed to assess if the submitted algorithms had containment within tracts of larger volume submissions. This also serves the purpose to detect if spurious submissions had been made. The top five submissions had high ICC and Dice >0.88. Reproducibility was high within the top five submissions when assessed across sessions or across scanners: 0.87-0.97. Containment analysis shows that the top five submissions are contained within larger volume submissions. From the total of 16 tracts as an outcome relatively the number of tracts with high, moderate, and low reproducibility were 8, 4, and 4. The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices. Data and challenge infrastructure remain available for continued analysis and provide a platform for comparison. 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249.

Effects of signal averaging, gradient encoding scheme, and spatial resolution on diffusion kurtosis imaging: An empirical study using 7T MRI.

Although diffusion gradient directions and b-values have been optimized for diffusion kurtosis imaging (DKI), little is known about the effect of signal averaging on DKI reliability. To evaluate how signal averaging influences the reliability of DKI indices using two gradient encoding schemes with three spatial resolutions. Prospective. Fifteen naïve Sprague-Dawley rats. DKI was performed at 7T using two schemes (30 directions with three b-values [30d-3b] and six directions with 15 b-values [6d-15b]), three resolutions, and eight repetitions. DKI reliability was assessed using voxelwise relative error (σ) and test-retest error of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) within gray matter (GM) and white matter (WM). The number of excitations (NEX) was optimized by considering DKI reliability. The influence of the partial volume effect (PVE) was also assessed. One-way analysis of variance. The 30d-3b scheme, compared with the 6d-15b scheme, exhibited apparently smaller σFA and σMK (eg, at NEX 1, in GM, for three resolutions, σFA : 19.9-38.2% vs. 34.2-61.4%, σMK : 6.9-11.4% vs. 14.1-15.4%) and similar σMD (all differences between two schemes <1.6%). The optimal NEX was determined as 2 for enabling a reliable measurement of DKI-derived indices. The PVE at the lowest resolution apparently increased σFA for both schemes (19.9% for 30d-3b and 34.2% for 6d-15b) and σMK for the 6d-15b scheme (14.7%) in GM, and exerted lower effects on MK values for the 30d-3b scheme (P > 0.05). A higher number of diffusion directions would benefit FA and MK estimation. A higher spatial resolution helps to reduce PVE. By using the 30d-3b scheme, MK is considered a robust index to reflect microstructural changes in GM and WM. We propose a systematic approach to determine the optimal DKI protocols for appropriate preclinical settings. 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1593-1603.