More accurate estimation of diffusion tensor parameters using diffusion Kurtosis imaging

Linear rotationally invariant kurtosis measures from double diffusion encoding MRI

PURPOSE

To characterize the complete set of linear rotationally invariant kurtosis measures provided by double diffusion encoding (DDE) MRI, show their utility in distinguishing different types of multiple Gaussian compartment (MGC) models, and demonstrate simplified acquisition and analysis schemes for their estimation.

THEORY AND METHODS

The lowest order novel information obtainable with DDE MRI can be encapsulated in a six-dimensional kurtosis tensor. The most basic DDE MRI kurtosis measures are rotational invariants that are linear in this tensor while depending on no other physical quantities. We identify four such invariants and show that any others must be linear combinations of these. The invariants are applied to classify MGC models according to whether they include microscopic anisotropy or intercompartmental water exchange. In addition, they are used to investigate the effect of exchange on estimates of the microscopic fractional anisotropy (μFA). Simplified acquisition and analysis schemes for the invariants are proposed and demonstrated with human brain data obtained at 3 T.

RESULTS

For the considered brain regions, the kurtosis invariants are found to be largely consistent with MGC models having microscopic anisotropy. They also indicate that water exchange in gray matter may affect estimates of μFA.

CONCLUSION

The kurtosis measures can classify MGC models according to whether they have microscopic anisotropy or water exchange, and they can be estimated with simple acquisition and analysis schemes. Measurements of the invariants in brain support the validity of MGC models with microscopic anisotropy and the importance of water exchange for modeling diffusion in gray matter.

Potential mechanism of impaired perceptual reasoning in children with obstructive sleep apnea syndrome: topological analysis of brain white matter network employing graph theory

Childhood obstructive sleep apnea syndrome (OSAS) disrupts normal ventilation and sleep structure and affects cognitive functions. However, the neurophysiological mechanisms underlying cognitive impairment are unclear. This study investigates the topological connectivity of white matter networks in children with moderate to severe OSAS and explores the underlying mechanisms of cognitive impairment. We collected clinical data of patients with moderate to severe OSAS (n = 43) and non-OSAS (n = 30). Intelligence testing was conducted using the China Wechsler Intelligence Scale for Children-Fourth Edition (C-WISC IV), including Processing speed, Working memory, Verbal comprehension, Perceptual reasoning, and Full-scale intelligence quotient (FSIQ). DTI data were collected using 3.0T MRI scanner (Ingenia, Philips, Netherlands). White matter network topology connections were analyzed using FSL and DSI Studio and inter group differences were statistically assessed. The difference of clinical and intelligence test was calculated by two sample t-test. Pearson correlation analysis was employed to examine the correlation between the abnormal white matter network metrics and cognitive function in OSAS patients. Clustering coefficient (Cp) and global efficiency (Eg), nodal degree (Dc), and nodal efficiency (Ne) were lower in the OSAS group (p < 0.05). Correlations between white matter network metrics and cognitive function: The Cp and Eg were positively correlated with Perceptual reasoning, and the shortest path length (Lp) was negatively correlated with Perceptual reasoning. The results indicate that there was impairment of cognitive function and abnormality of topological structural connectivity in white matter networks for children with OSAS. The Cp, Eg, and Lp correlate with Perceptual reasoning, indicating that abnormal topological structural connectivity of the white matter network might be neurofunctional basis for impaired perceptual reasoning.

Romer-EPTI: Rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale diffusion MRI and microstructure imaging

PURPOSE

To overcome the major challenges in diffusion MRI (dMRI) acquisition, including limited SNR, distortion/blurring, and susceptibility to motion artifacts.

THEORY AND METHODS

A novel Romer-EPTI technique is developed to achieve SNR-efficient acquisition while providing distortion-free imaging, minimal spatial blurring, high motion robustness, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free Echo Planar Time-resolved Imaging (EPTI) readout. Romer enhances SNR through simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness via a high-fidelity, motion-aware super-resolution reconstruction. Instead of EPI, the in-plane encoding is performed using EPTI readout to prevent geometric distortion, T2/T2*-blurring, and importantly, dynamic distortions that could introduce additional blurring/artifacts after super-resolution reconstruction due to combining volumes with inconsistent geometries. This further improves effective spatial resolution and motion robustness. Additional developments include strategies to address slab-boundary artifacts, achieve minimized TE and optimized readout for additional SNR gain, and increase robustness to strong phase variations at high b-values.

RESULTS

Using Romer-EPTI, we demonstrated distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm isotropic [iso] resolution) and 7T (485-μm iso resolution) for the first time. Motion experiments demonstrated the technique's motion robustness and its ability to obtain high-resolution diffusion images in the presence of subject motion. Romer-EPTI also demonstrated high SNR gain and robustness in high b-value (b = 5000 s/mm2) and time-dependent dMRI.

CONCLUSION

The high SNR efficiency, improved image quality, and motion robustness of Romer-EPTI make it a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Improving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field - Sequence development and in-vitro validation

PURPOSE

To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.

METHODS

We developed a Cartesian phase contrast sequence with FAST velocity encoding and two separately measured partial echoes with opposite readout directions. This design aims to reduce the high-order gradient moments that are responsible for the described measurement error. Velocity encoding directions follow the ICOSA6 scheme to capture the full RST. Turbulence data is reconstructed using the intra-voxel phase dispersion (IVPD) technique. We validated this sequence in vitro using a periodic hill flow benchmark with highly anisotropic turbulence. MRI data underwent extensive averaging, with multiple velocity encoding values employed to reduce noise and isolate systematic effects.

RESULTS

The RST data obtained from the new sequence agree well with the ground truth. Compared to a state-of-the-art sequence, the maximum errors were reduced by factor five.

CONCLUSION

Simple adjustments to current MRI protocols can greatly enhance turbulence measurement accuracy through the reduction of high-order gradient moments. The proposed measures include applying FAST velocity encoding, high readout bandwidth, and a highly asymmetric readout. Ringing artifacts due to the asymmetric readout can be removed via a second, inverted readout.

Robust, fast and accurate mapping of diffusional mean kurtosis

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Impact of truncating diffusion MRI scans on diffusional kurtosis imaging

OBJECTIVE

Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (OptEEM); 2) spherical codes (OptSC); 3) random (RandomTRUNC).

MATERIALS AND METHODS

Pre-acquired diffusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing different amounts of the full dataset were generated. The subsampling effects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at different SNRs and the influence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively.

RESULTS

Simulations showed that subsampling had different effects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). RandomTRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least affected (OptEEM: up to 5% error; OptSC: up to 7% error) and peak height (OptEEM: up to 8% error; OptSC: up to 11% error) the most affected.

CONCLUSION

The impact of truncation depends on specific histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.

Ultrahigh field diffusion magnetic resonance imaging uncovers intriguing microstructural changes in the adult zebrafish brain caused by Toll-like receptor 2 genomic deletion

Toll-like receptor 2 (TLR2) belongs to the TLR protein family that plays an important role in the immune and inflammation response system. While TLR2 is predominantly expressed in immune cells, its expression has also been detected in the brain, specifically in microglia and astrocytes. Recent studies indicate that genomic deletion of TLR2 can result in impaired neurobehavioural function. It is currently not clear if the genomic deletion of TLR2 leads to any alterations in the microstructural features of the brain. In the current study, we noninvasively assess microstructural changes in the brain of TLR2-deficient (tlr2-/-) zebrafish using state-of-the art magnetic resonance imaging (MRI) methods at ultrahigh magnetic field strength (17.6 T). A significant increase in cortical thickness and an overall trend towards increased brain volumes were observed in young tlr2-/- zebrafish. An elevated T2 relaxation time and significantly reduced apparent diffusion coefficient (ADC) unveil brain-wide microstructural alterations, potentially indicative of cytotoxic oedema and astrogliosis in the tlr2-/- zebrafish. Multicomponent analysis of the ADC diffusivity signal by the phasor approach shows an increase in the slow ADC component associated with restricted diffusion. Diffusion tensor imaging and diffusion kurtosis imaging analysis revealed diminished diffusivity and enhanced kurtosis in various white matter tracks in tlr2-/- compared with control zebrafish, identifying the microstructural underpinnings associated with compromised white matter integrity and axonal degeneration. Taken together, our findings demonstrate that the genomic deletion of TLR2 results in severe alterations to the microstructural features of the zebrafish brain. This study also highlights the potential of ultrahigh field diffusion MRI techniques in discerning exceptionally fine microstructural details within the small zebrafish brain, offering potential for investigating microstructural changes in zebrafish models of various brain diseases.

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Compartmental anisotropy of filtered exchange imaging (FEXI) in human white matter: What is happening in FEXI?

PURPOSE

To investigate the effects of compartmental anisotropy on filtered exchange imaging (FEXI) in white matter (WM).

THEORY AND METHODS

FEXI signals were measured using multiple combinations of diffusion filter and detection directions in five healthy volunteers. Additional filters, including a trace-weighted diffusion filter with trapezoidal gradients, a spherical b-tensor encoded diffusion filter, and a T2 filter, were tested with trace-weighted diffusion detection.

RESULTS

A large range of apparent exchange rates (AXR) and both positive and negative filter efficiencies (σ) were found depending on the mutual orientation of the filter and detection gradients relative to WM fiber orientation. The data demonstrated that the fast-diffusion compartment suppressed by diffusional filtering is not exclusively extra-cellular, but also intra-cellular. While not comprehensive, a simple two-compartment diffusion tensor model with water exchange was able to account qualitatively for the trends in positive and negative filtering efficiencies, while standard model imaging (SMI) without exchange could not. This two-compartment diffusion tensor model also demonstrated smaller AXR variances across subjects. When employing trace-weighted diffusion detection, AXR values were on the order of the R1 (=1/T1) of water at 3T for crossing fibers, while being less than R1 for parallel fibers.

CONCLUSION

Orientation-dependent AXR and σ values were observed when using multi-orientation filter and detection gradients in FEXI, indicating that WM FEXI models need to account for compartmental anisotropy. When using trace-weighted detection, AXR values were on the order of or less than R1, complicating the interpretation of FEXI results in WM in terms of biological exchange properties. These findings may contribute toward better understanding of FEXI results in WM.

Widespread White Matter Abnormalities in Concussed Athletes Detected by 7T Diffusion Magnetic Resonance Imaging

Sports-related concussions may cause white matter injuries and persistent post-concussive symptoms (PPCS). We hypothesized that athletes with PPCS would have neurocognitive impairments and white matter abnormalities that could be revealed by advanced neuroimaging using ultra-high field strength diffusion tensor (DTI) and diffusion kurtosis (DKI) imaging metrics and cerebrospinal fluid (CSF) biomarkers. A cohort of athletes with PPCS severity limiting the ability to work/study and participate in sport school and/or social activities for ≥6 months completed 7T magnetic resonance imaging (MRI) (morphological T1-weighed volumetry, DTI and DKI), extensive neuropsychological testing, symptom rating, and CSF biomarker sampling. Twenty-two athletes with PPCS and 22 controls were included. Concussed athletes performed below norms and significantly lower than controls on all but one of the psychometric neuropsychology tests. Supratentorial white and gray matter, as well as hippocampal volumes did not differ between concussed athletes and controls. However, of the 72 examined white matter tracts, 16% of DTI and 35% of DKI metrics (in total 28%) were significantly different between concussed athletes and controls. DKI fractional anisotropy and axial kurtosis were increased, and DKI radial diffusivity and radial kurtosis decreased in concussed athletes when compared with controls. CSF neurofilament light (NfL; an axonal injury marker), although not glial fibrillary acidic protein, correlated with several diffusion metrics. In this first 7T DTI and DKI study investigating PPCS, widespread microstructural alterations were observed in the white matter, correlating with CSF markers of axonal injury. More white matter changes were observed using DKI than using DTI. These white matter alterations may indicate persistent pathophysiological processes following concussion in sport.

Anesthesia-related brain microstructure modulations detected by diffusion magnetic resonance imaging

Recent studies have shown significant changes to brain microstructure during sleep and anesthesia. In vivo optical microscopy and magnetic resonance imaging (MRI) studies have attributed these changes to anesthesia and sleep-related modulation of the brain's extracellular space (ECS). Isoflurane anesthesia is widely used in preclinical diffusion MRI (dMRI) and it is therefore important to investigate if the brain's microstructure is affected by anesthesia to an extent detectable with dMRI. Here, we employ diffusion kurtosis imaging (DKI) to assess brain microstructure in the awake and anesthetized mouse brain (n = 22). We find both mean diffusivity (MD) and mean kurtosis (MK) to be significantly decreased in the anesthetized mouse brain compared with the awake state (p < 0.001 for both). This effect is observed in both gray matter and white matter. To further investigate the time course of these changes we introduce a method for time-resolved fast DKI. With this, we show the time course of the microstructural alterations in mice (n = 5) as they transition between states in an awake-anesthesia-awake paradigm. We find that the decrease in MD and MK occurs rapidly after delivery of gas isoflurane anesthesia and that values normalize only slowly when the animals return to the awake state. Finally, time-resolved fast DKI is employed in an experimental mouse model of brain edema (n = 4), where cell swelling causes the ECS volume to decrease. Our results show that isoflurane affects DKI parameters and metrics of brain microstructure and point to isoflurane causing a reduction in the ECS volume. The demonstrated DKI methods are suitable for in-bore perturbation studies, for example, for investigating microstructural modulations related to sleep/wake-dependent functions of the glymphatic system. Importantly, our study shows an effect of isoflurane anesthesia on rodent brain microstructure that has broad relevance to preclinical dMRI.

Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps

Background

Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT.

Methods

Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done.

Results

Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our "one-minute FA" did not suffer from the same problem.

Conclusion

When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed.

Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging

Purpose

To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability.

Methods

A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values.

Results

Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI.

Conclusion

Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Detection of recurrent high-grade glioma using microstructure characteristics of distinct metabolic compartments in a multimodal and integrative 18F-FET PET/fast-DKI approach

OBJECTIVES

Differentiation between high-grade glioma (HGG) and post-treatment-related effects (PTRE) is challenging, but advanced imaging techniques were shown to provide benefit. We aim to investigate microstructure characteristics of metabolic compartments identified from amino acid PET and to evaluate the diagnostic potential of this multimodal and integrative O-(2-18F-fluoroethyl)-L-tyrosine-(FET)-PET and fast diffusion kurtosis imaging (DKI) approach for the detection of recurrence and IDH genotyping.

METHODS

Fifty-nine participants with neuropathologically confirmed recurrent HGG (n = 39) or PTRE (n = 20) were investigated using static 18F-FET PET and a fast-DKI variant. PET and advanced diffusion metrics of metabolically defined (80-100% and 60-75% areas of 18F-FET uptake) compartments were assessed. Comparative analysis was performed using Mann-Whitney U tests with Holm-Šídák multiple-comparison test and Wilcoxon signed-rank test. Receiver operating characteristic (ROC) curves, regression, and Spearman's correlation analysis were used for statistical evaluations.

RESULTS

Compared to PTRE, recurrent HGG presented increased 18F-FET uptake and diffusivity (MD60), but lower (relative) mean kurtosis tensor (rMKT60) and fractional anisotropy (FA60) (respectively p < .05). Diffusion metrics determined from the metabolic periphery showed improved diagnostic performance - most pronounced for FA60 (AUC = 0.86, p < .001), which presented similar benefit to 18F-FET PET (AUC = 0.86, p < .001) and was negatively correlated with amino acid uptake (rs =  - 0.46, p < .001). When PET and DKI metrics were evaluated in a multimodal biparametric approach, TBRmax + FA60 showed highest diagnostic accuracy (AUC = 0.93, p < .001), which improved the detection of relapse compared to PET alone (difference in AUC = 0.069, p = .04). FA60 and MD60 distinguished the IDH genotype in the post-treatment setting.

CONCLUSION

Detection of glioma recurrence benefits from a multimodal and integrative PET/DKI approach, which presented significant diagnostic advantage to the assessment based on PET alone.

CLINICAL RELEVANCE STATEMENT

A multimodal and integrative 18F-FET PET/fast-DKI approach for the non-invasive microstructural characterization of metabolic compartments provided improved diagnostic capability for differentiation between recurrent glioma and post-treatment-related changes, suggesting a role for the diagnostic workup of patients in post-treatment settings.

KEY POINTS

• Multimodal PET/MRI with integrative analysis of 18F-FET PET and fast-DKI presents clinical benefit for the assessment of CNS cancer, particularly for the detection of recurrent high-grade glioma. • Microstructure markers of the metabolic periphery yielded biologically pertinent estimates characterising the tumour microenvironment, and, thereby, presented improved diagnostic accuracy with similar accuracy to amino acid PET. • Combined 18F-FET PET/fast-DKI achieved the best diagnostic performance for detection of high-grade glioma relapse with significant benefit to the assessment based on PET alone.

Macro- and micro-structural insights into primary dystonia: a UK Biobank study

BACKGROUND

Dystonia is a hyperkinetic movement disorder with key motor network dysfunction implicated in pathophysiology. The UK Biobank encompasses > 500,000 participants, of whom 42,565 underwent brain MRI scanning. This study applied an optimized pre-processing pipeline, aimed at better accounting for artifact and improving data reliability, to assess for grey and white matter structural MRI changes between individuals diagnosed with primary dystonia and an unaffected control cohort.

METHODS

Individuals with dystonia (n = 76) were identified from the UK Biobank using published algorithms, alongside an age- and sex-matched unaffected control cohort (n = 311). Grey matter morphometric and diffusion measures were assessed, together with white matter diffusion tensor and diffusion kurtosis metrics using tractography and tractometry. Post-hoc Neurite Orientation and Density Distribution Imaging (NODDI) was also undertaken for tracts in which significant differences were observed.

RESULTS

Grey matter tremor-specific striatal differences were observed, with higher radial kurtosis. Tractography identified no white matter differences, however segmental tractometry identified localised differences, particularly in the superior cerebellar peduncles and anterior thalamic radiations, including higher fractional anisotropy and lower orientation distribution index in dystonia, compared to controls. Additional tremor-specific changes included lower neurite density index in the anterior thalamic radiations.

CONCLUSIONS

Analysis of imaging data from one of the largest dystonia cohorts to date demonstrates microstructural differences in cerebellar and thalamic white matter connections, with architectural differences such as less orientation dispersion potentially being a component of the morphological structural changes implicated in dystonia. Distinct tremor-related imaging features are also implicated in both grey and white matter.

Analysis of white matter tract integrity using diffusion kurtosis imaging reveals the correlation of white matter microstructural abnormalities with cognitive impairment in type 2 diabetes mellitus

Background

This study aimed to identify disruptions in white matter integrity in type 2 diabetes mellitus (T2DM) patients by utilizing the white matter tract integrity (WMTI) model, which describes compartment-specific diffusivities in the intra- and extra-axonal spaces, and to investigate the relationship between WMTI metrics and clinical and cognitive measurements.

Methods

A total of 73 patients with T2DM and 57 healthy controls (HCs) matched for age, sex, and education level were enrolled and underwent diffusional kurtosis imaging and cognitive assessments. Tract-based spatial statistics (TBSS) and atlas-based region of interest (ROI) analysis were performed to compare group differences in diffusional metrics, including fractional anisotropy (FA), mean diffusivity (MD), axonal water fraction (AWF), intra-axonal diffusivity (Daxon), axial extra-axonal space diffusivity (De,//), and radial extra-axonal space diffusivity (De,⊥) in multiple white matter (WM) regions. Relationships between diffusional metrics and clinical and cognitive functions were characterized.

Results

In the TBSS analysis, the T2DM group exhibited decreased FA and AWF and increased MD, De,∥, and De,⊥ in widespread WM regions in comparison with the HC group, which involved 56.28%, 32.07%, 73.77%, 50.47%, and 75.96% of the mean WM skeleton, respectively (P < 0.05, TFCE-corrected). De,⊥ detected most of the WM changes, which were mainly located in the corpus callosum, internal capsule, external capsule, corona radiata, posterior thalamic radiations, sagittal stratum, cingulum (cingulate gyrus), fornix (stria terminalis), superior longitudinal fasciculus, and uniform fasciculus. Additionally, De,⊥ in the genu of the corpus callosum was significantly correlated with worse performance in TMT-A (β = 0.433, P < 0.001) and a longer disease duration (β = 0.438, P < 0.001).

Conclusions

WMTI is more sensitive than diffusion tensor imaging in detecting T2DM-related WM microstructure abnormalities and can provide novel insights into the possible pathological changes underlying WM degeneration in T2DM. De,⊥ could be a potential imaging marker in monitoring disease progression in the brain and early intervention treatment for the cognitive impairment in T2DM.

Quantitative microglia morphological features correlate with diffusion MRI in 2-month-old 3xTg-AD mice

Microglia (MØ) morphologies are closely related to their functional state and have a central role in the maintenance of brain homeostasis. It is well known that inflammation contributes to neurodegeneration at later stages of Alzheimer's Disease, but it is not clear which role MØ-mediated inflammation may play earlier in the disease pathogenesis. We have previously reported that diffusion MRI (dMRI) is able to detect early myelin abnormalities present in 2-month-old 3xTg-AD (TG) mice; since MØ actively participate in regulating myelination, the goal of this study was to assess quantitatively MØ morphological characteristics and its association with dMRI metrics patterns in 2-month-old 3xTg-AD mice. Our results show that, even at this young age (2-month-old), TG mice have statistically significantly more MØ cells, which are overall smaller and more complex, compared with age-matched normal control mice (NC). Our results also confirm that myelin basic protein is reduced in TG mice, particularly in fimbria (Fi) and cortex. Additionally, MØ morphological characteristics, in both groups, correlate with several dMRI metrics, depending on the brain region examined. For example, the increase in MØ number correlated with higher radial diffusivity (r = 0.59, p = 0.008), lower fractional anisotropy (FA) (r = -0.47, p = 0.03), and lower kurtosis fractional anisotropy (KFA) (r = -0.55, p = 0.01) in the CC. Furthermore, smaller MØ cells correlate with higher axial diffusivity) in the HV (r = 0.49, p = 0.03) and Sub (r = 0.57, p = 0.01). Our findings demonstrate, for the first time, that MØ proliferation/activation are a common and widespread feature in 2-month-old 3xTg-AD mice and suggest that dMRI measures are sensitive to these MØ alterations, which are associated in this model with myelin dysfunction and microstructural integrity abnormalities.

Frequency-dependent diffusion kurtosis imaging in the human brain using an oscillating gradient spin echo sequence and a high-performance head-only gradient

Measuring the time/frequency dependence of diffusion MRI is a promising approach to distinguish between the effects of different tissue microenvironments, such as membrane restriction, tissue heterogeneity, and compartmental water exchange. In this study, we measure the frequency dependence of diffusivity (D) and kurtosis (K) with oscillating gradient diffusion encoding waveforms and a diffusion kurtosis imaging (DKI) model in human brains using a high-performance, head-only MAGNUS gradient system, with a combination of b-values, oscillating frequencies (f), and echo time that has not been achieved in human studies before. Frequency dependence of diffusivity and kurtosis are observed in both global and local white matter (WM) and gray matter (GM) regions and characterized with a power-law model ∼Λ*fθ. The frequency dependences of diffusivity and kurtosis (including changes between fmin and fmax, Λ, and θ) vary over different WM and GM regions, indicating potential microstructural differences between regions. A trend of decreasing kurtosis over frequency in the short-time limit is successfully captured for in vivo human brains. The effects of gradient nonlinearity (GNL) on frequency-dependent diffusivity and kurtosis measurements are investigated and corrected. Our results show that the GNL has prominent scaling effects on the measured diffusivity values (3.5∼5.5% difference in the global WM and 6∼8% difference in the global cortex) and subsequently affects the corresponding power-law parameters (Λ, θ) while having a marginal influence on the measured kurtosis values (<0.05% difference) and power-law parameters (Λ, θ). This study expands previous OGSE studies and further demonstrates the translatability of frequency-dependent diffusivity and kurtosis measurements to human brains, which may provide new opportunities to probe human brain microstructure in health and disease.

MR imaging and outcome in neonatal HIBD models are correlated with sex: the value of diffusion tensor MR imaging and diffusion kurtosis MR imaging

Objective

Hypoxic-ischemic encephalopathy can lead to lifelong morbidity and premature death in full-term newborns. Here, we aimed to determine the efficacy of diffusion kurtosis (DK) [mean kurtosis (MK)] and diffusion tensor (DT) [fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion (RD)] parameters for the early diagnosis of early brain histopathological changes and the prediction of neurodegenerative events in a full-term neonatal hypoxic-ischemic brain injury (HIBD) rat model.

Methods

The HIBD model was generated in postnatal day 7 Sprague-Dawley rats to assess the changes in DK and DT parameters in 10 specific brain structural regions involving the gray matter, white matter, and limbic system during acute (12 h) and subacute (3 d and 5 d) phases after hypoxic ischemia (HI), which were validated against histology. Sensory and cognitive parameters were assessed by the open field, novel object recognition, elevated plus maze, and CatWalk tests.

Results

Repeated-measures ANOVA revealed that specific brain structures showed similar trends to the lesion, and the temporal pattern of MK was substantially more varied than DT parameters, particularly in the deep gray matter. The change rate of MK in the acute phase (12 h) was significantly higher than that of DT parameters. We noted a delayed pseudo-normalization for MK. Additionally, MD, AD, and RD showed more pronounced differences between males and females after HI compared to MK, which was confirmed in behavioral tests. HI females exhibited anxiolytic hyperactivity-like baseline behavior, while the memory ability of HI males was affected in the novel object recognition test. CatWalk assessments revealed chronic deficits in limb gait parameters, particularly the left front paw and right hind paw, as well as poorer performance in HI males than HI females.

Conclusions

Our results suggested that DK and DT parameters were complementary in the immature brain and provided great value in assessing early tissue microstructural changes and predicting long-term neurobehavioral deficits, highlighting their ability to detect both acute and long-term changes. Thus, the various diffusion coefficient parameters estimated by the DKI model are powerful tools for early HIBD diagnosis and prognosis assessment, thus providing an experimental and theoretical basis for clinical treatment.

Disrupted topological organization of white matter structural networks in high myopia patients revealed by diffusion kurtosis imaging and tractography

Introduction

High myopia (HM) is a public health issue that can lead to severe visual impairment. Previous studies have exhibited widespread white matter (WM) integrity damage in HM patients. However, how these WM damages are topologically related, and the network-level structural disruptions underlying HM has not been fully defined. We aimed to assess the alterations of brain WM structural networks in HM patients using diffusion kurtosis imaging (DKI) and tractography in the present study.

Methods

Individual whole-brain and ROI-level WM networks were constructed using DKI tractography in 30 HM patients and 33 healthy controls. Graph theory analysis was then applied to explore the altered global and regional network topological properties. Pearson correlations between regional properties and disease duration in the HM group were also assessed.

Results

For global topology, although both groups showed a small-world network organization, HM patients exhibited significant decreased local efficiency and clustering coefficient compared with controls. For regional topology, HM patients and controls showed highly similar hub distributions, except for three additional hub regions in HM patients including left insula, anterior cingulate and paracingulate gyri (ACG), and median cingulate and paracingulate gyri (DCG). In addition, HM patients showed significantly altered nodal betweenness centrality (BC) mainly in the bilateral inferior occipital gyrus (IOG), left superior occipital gyrus (SOG), caudate nucleus, rolandic operculum and right putamen, pallidum, and gyrus rectus compared with controls. Intriguingly, the nodal BC of left IOG was negatively correlated with disease duration in HM patients.

Discussion

Our findings suggest that HM exhibited alterations in WM structural networks as indicated by decreased local specialization. This study may advance the current understanding of the pathophysiological mechanisms underlying HM.

White matter microstructure in face and body networks predicts facial expression and body posture perception across development

Facial expression and body posture recognition have protracted developmental trajectories. Interactions between face and body perception, such as the influence of body posture on facial expression perception, also change with development. While the brain regions underpinning face and body processing are well-defined, little is known about how white-matter tracts linking these regions relate to perceptual development. Here, we obtained complementary diffusion magnetic resonance imaging (MRI) measures (fractional anisotropy [FA], spherical mean Ṧμ ), and a quantitative MRI myelin-proxy measure (R1), within white-matter tracts of face- and body-selective networks in children and adolescents and related these to perceptual development. In tracts linking occipital and fusiform face areas, facial expression perception was predicted by age-related maturation, as measured by Ṧμ and R1, as well as age-independent individual differences in microstructure, captured by FA and R1. Tract microstructure measures linking posterior superior temporal sulcus body region with anterior temporal lobe (ATL) were related to the influence of body on facial expression perception, supporting ATL as a site of face and body network convergence. Overall, our results highlight age-dependent and age-independent constraints that white-matter microstructure poses on perceptual abilities during development and the importance of complementary microstructural measures in linking brain structure and behaviour.

Negative emotion differentiation and white matter microstructure

BACKGROUND

Deficits in the differentiation of negative emotions - the ability to specifically identify one's negative emotions - are associated with poorer mental health outcomes. However, the processes that lead to individual differences in negative emotion differentiation are not well understood, hampering our understanding of why this process is related to poor mental health outcomes. Given that disruptions in some affective processes are associated with white matter microstructure, identifying the circuitry associated with different affective processes can inform our understanding of how disturbances in these networks may lead to psychopathology. Thus, examination of how white matter microstructure relates to individual differences in negative emotion differentiation (NED) may provide insights into (i) its component processes and (ii) its relationship to brain structure.

METHOD

The relationship between white matter microstructure and NED was examined.

RESULTS

NED was related to white matter microstructure in right anterior thalamic radiation and inferior fronto-occipital fasciculus and left peri-genual cingulum.

LIMITATIONS

Although participants self-reported psychiatric diagnoses and previous psychological treatment, psychopathology was not directly targeted, and thus, the extent to which microstructure related to NED could be examined in relation to maladaptive outcomes is limited.

CONCLUSIONS

Results indicate that NED is related to white matter microstructure and suggest that pathways subserving processes that facilitate memory, semantics, and affective experience are important for NED. Our findings provide insights into the mechanisms by which individual differences in NED arise, suggesting intervention targets that may disrupt the relationship between poor differentiation and psychopathology.

Measuring water exchange on a preclinical MRI system using filter exchange and diffusion time dependent kurtosis imaging

PURPOSE

Filter exchange imaging (FEXI) and diffusion time (t)-dependent diffusion kurtosis imaging (DKI(t)) are both sensitive to water exchange between tissue compartments. The restrictive effects of tissue microstructure, however, introduce bias to the exchange rate obtained by these two methods, as their interpretation conventionally rely on the Kärger model of barrier limited exchange between Gaussian compartments. Here, we investigated whether FEXI and DKI(t) can provide comparable exchange rates in ex vivo mouse brains.

THEORY AND METHODS

FEXI and DKI(t) data were acquired from ex vivo mouse brains on a preclinical MRI system. Phase cycling and negative slice prewinder gradients were used to minimize the interferences from imaging gradients.

RESULTS

In the corpus callosum, apparent exchange rate (AXR) from FEXI correlated with the exchange rate (the inverse of exchange time, 1/τex ) from DKI(t) along the radial direction. In comparison, discrepancies between FEXI and DKI(t) were found in the cortex due to low filter efficiency and confounding effects from tissue microstructure.

CONCLUSION

The results suggest that FEXI and DKI(t) are sensitive to the same exchange processes in white matter when separated from restrictive effects of microstructure. The complex microstructure in gray matter, with potential exchange among multiple compartments and confounding effects of microstructure, still pose a challenge for FEXI and DKI(t).

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.

Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.

Application of diffusion kurtosis imaging in neonatal brain development

Background

Deviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI).

Materials and methods

In total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the Kruskal-Wallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ≤7 days; the second group, neonates aged 8-14 days; and the third group, neonates aged 15-28 days. The rate of change in DKI metrics relative to the first group was computed.

Results

The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value.

Conclusion

DKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development.

Brain microstructure abnormalities in the 3xTg-AD mouse - A diffusion MRI and morphology correlation study

The widely studied triple transgenic (3xTg-AD) mouse provides a robust model of Alzheimer's disease (AD) with region dependent patterns of progressive amyloid-β (Aß) and tau pathology. Using diffusion MRI (dMRI), we investigated the sensitivity of dMRI measures in capturing AD pathology associated microstructure alterations in older 3xTg-AD mice, and the degree to which dMRI changes correlate with measurements of Aβ and tau pathology. 3xTg-AD and normal control (NC) mice, 15 to 21 months of age, were used in this study. In vivo dMRI data were acquired for the generation of diffusion tensor (DT) and diffusional kurtosis (DK) measures within the hippocampus and fimbria (Fi). For these same brain regions, Aβ and tau pathology were quantified by morphological analysis of Aß1-42 and AT8 immunoreactivity. Two-tailed, two-sample t-tests were performed to assess group differences in each brain region of interest (ROI), with the Benjamini-Hochberg false discovery rate (FDR) method being applied to adjust for multiple comparisons. Spearman correlation coefficients were calculated to investigate associations between diffusion and morphological measures. Our results revealed, depending on the brain region, DT and DK measures were able to detect group differences. In the dorsal hippocampus (HD), fractional anisotropy (FA) was significantly higher in the 3xTg-AD mice compared with NC mice. In the subiculum (SUB), FA, axial diffusivity (D||) and radial kurtosis (K┴) were significantly higher in 3xTg-AD mice compared with NC mice. Morphological quantification of Aß1-42 and AT8 immunoreactivity showed elevated Aß and tau in the Fi, ventral hippocampus (HV) and SUB of 3xTg-AD mice. The presence of Aβ and tau was significantly correlated with several DT and DK measures, particularly in the SUB, where an increase in tau correlated with an increase in mean kurtosis (MK) and K┴. This work demonstrates significant dMRI differences between older 3xTg-AD and NC mice in the hippocampus and Fi. Significant correlations were found between dMRI and morphological measures of Aβ and tau pathology. These results support the potential of dMRI-derived parameters as biomarkers of AD pathology. Since the imaging methods employed here are easily translatable to clinical MRI, our results are also relevant for human AD patients.

Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition

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, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> in mm²  s^-1 and a fractional exponent, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> , 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>b</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn></mml:mrow> <mml:annotation>$$ b=0 $$</mml:annotation></mml:semantics> </mml:math> and 5000 s mm^-2 . The effects of varying maximum b-value ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> ), number of b-value shells, and the effects of Rician noise were investigated.

RESULTS

QDTI measures showed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> dependence, most significantly for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, which monotonically decreased with higher <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> 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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ \kern0.50em {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and underestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, and overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>5000</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=5000 $$</mml:annotation></mml:semantics> </mml:math>  s mm^-2 , and 4 b-value shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>3960</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=3960 $$</mml:annotation></mml:semantics> </mml:math>  s mm^-2 , providing minimal bias in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> 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.

Association between attention-deficit/hyperactivity disorder symptom severity and white matter integrity moderated by in-scanner head motion

Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder associated with various negative life impacts. The manifestation of ADHD is very heterogeneous, and previous investigations on neuroanatomical alterations in ADHD have yielded inconsistent results. We investigated the mediating effect of in-scanner head motion and ADHD hyperactivity severity on motion-corrected fractional anisotropy (FA) using diffusion tensor imaging in the currently largest sample (n = 739) of medication-naïve children and adolescents (age range 5-22 years). We used automated tractography to examine whole-brain and mean FA of the tracts most frequently reported in ADHD; corpus callosum forceps major and forceps minor, left and right superior-longitudinal fasciculus, and left and right corticospinal tract (CST). Associations between FA and hyperactivity severity appeared when in-scanner head motion was not accounted for as mediator. However, causal mediation analysis revealed that these effects are fully mediated through in-scanner head motion for whole-brain FA, the corpus callosum forceps minor, and left superior-longitudinal fasciculus. Direct effect of hyperactivity severity on FA was only found for the left CST. This study illustrates the crucial role of in-scanner head motion in the identification of white matter integrity alterations in ADHD and shows how neglecting irremediable motion artifacts causes spurious findings. When the mediating effect of in-scanner head motion on FA is accounted for, an association between hyperactivity severity and FA is only present for the left CST; this may play a crucial role in the manifestation of hyperactivity and impulsivity symptoms in ADHD.

Combination of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Provides Potential Biomarkers for Early-Stage Parkinson's Disease

Purpose: This study aimed to detect changes in iron deposition and neural microstructure in the substantia nigra (SN), red nucleus (RN), and basal ganglia of Parkinson's disease (PD) patients at different stages using quantitative susceptibility mapping and diffusion kurtosis imaging to identify potential indicators of early-stage PD. Methods: We enrolled 20 early-stage and 15 late-stage PD patients, as well as 20 age- and sex-matched controls. All participants underwent quantitative susceptibility mapping and diffusion kurtosis imaging to determine magnetic susceptibility (MS), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) in several brain regions. Results: Compared with the control group, MS and MK values in the SN were significantly increased in the early- and late-stage PD group, whereas MS values in the red nucleus (RN), globus pallidus (GP), and caudate nucleus (CN), FA value in the CN and GP, and MK value in the CN and putamen (PU) were significantly increased in the late-stage PD group. There were positive correlations between MS and MK values in the CN and MS and FA values in the GP. Furthermore, the combination of MS and MK values in the SN provided high accuracy for distinguishing early-stage PD patients from controls. Conclusions: This study identified MS and MK in the SN as potential indicators of early-stage PD.

Decomposing the role of alpha oscillations during brain maturation

Childhood and adolescence are critical stages of the human lifespan, in which fundamental neural reorganizational processes take place. A substantial body of literature investigated accompanying neurophysiological changes, focusing on the most dominant feature of the human EEG signal: the alpha oscillation. Recent developments in EEG signal-processing show that conventional measures of alpha power are confounded by various factors and need to be decomposed into periodic and aperiodic components, which represent distinct underlying brain mechanisms. It is therefore unclear how each part of the signal changes during brain maturation. Using multivariate Bayesian generalized linear models, we examined aperiodic and periodic parameters of alpha activity in the largest openly available pediatric dataset (N=2529, age 5-22 years) and replicated these findings in a preregistered analysis of an independent validation sample (N=369, age 6-22 years). First, the welldocumented age-related decrease in total alpha power was replicated. However, when controlling for the aperiodic signal component, our findings provided strong evidence for an age-related increase in the aperiodic-adjusted alpha power. As reported in previous studies, also relative alpha power revealed a maturational increase, yet indicating an underestimation of the underlying relationship between periodic alpha power and brain maturation. The aperiodic intercept and slope decreased with increasing age and were highly correlated with total alpha power. Consequently, earlier interpretations on age-related changes of total alpha power need to be reconsidered, as elimination of active synapses rather links to decreases in the aperiodic intercept. Instead, analyses of diffusion tensor imaging data indicate that the maturational increase in aperiodic-adjusted alpha power is related to increased thalamocortical connectivity. Functionally, our results suggest that increased thalamic control of cortical alpha power is linked to improved attentional performance during brain maturation.

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10-4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

Optimization of scan parameters to reduce acquisition time for RESOLVE-based diffusion kurtosis imaging (DKI) in nasopharyngeal carcinoma (NPC)

OBJECTIVE

To shorten acquisition time of readout segmentation of long variable echo trains (RESOLVE)-based diffusion kurtosis imaging (DKI) via Readout Partial Fourier (RPF) and b-value combinations.

METHODS

The RESOLVE-based DKI images of 38 patients with nasopharyngeal carcinoma (NPC) were prospectively enrolled. For RESOLVE-based DKI images with 5/8 RPF and without RPF, objective and subjective evaluations of image quality were performed. A total of nine groups with different b-value combinations were simulated, and the influence of different b-value combinations for RESOLVE-RPF-based DKI sequences was assessed using the intraclass correlation coefficient (ICC).

RESULTS

The mean values of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in DKI images without RPF were higher than those with 5/8 RPF (252.9 ± 77.7 vs 247.3 ± 85.5 and 5.8 ± 2.8 vs 5.4 ± 2.3, respectively), but not significantly (p = 0.460 and p = 0.180, respectively). In comparing the ICCs between nine groups of different b-value combinations in RESOLVE-RPF-based DKI, group (200, 800, 2000 s/mm2), group (200, 400, 800, 2000 s/mm2) and group (200, 800, 1500, 2000 s/mm2) were not significantly different (p > 0.001) and showed excellent agreement (0.81-1.00) with that of group (200, 400, 800, 1500, 2000 s/mm2). Using b-value optimization and RPF technology, the group with RPF (200, 400, 800, 2000 s/mm2) showed a 56% reduced scanning compared with the group without RPF (200, 400, 800, 1500, 2000 s/mm2; 3 min 46 s vs 8 min 31 s, respectively).

CONCLUSION

DKI with RPF did not significantly affect image quality, but both RPF and different b-value combinations can affect the scanning time. The combination of RPF and b-value optimization can ensure the stability of DKI parameters and reduce the scanning time by 56%.

ADVANCES IN KNOWLEDGE

This work is to optimize scan parameters, e.g. RPF and b-value combinations, to reduce acquisition time for RESOLVE-based DKI in NPC. To our knowledge, the effect of RESOLVE-RPF and b-value combinations on DKI has not been reported.

Brain Micro-Structural and Functional Alterations for Cognitive Function Prediction in the End-Stage Renal Disease Patients Undergoing Maintenance Hemodialysis

RATIONALE AND OBJECTIVES

The goal of this study was to investigate the relationship between altered brain micro-structure and function, and cognitive function in patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis. Specially, diffusion kurtosis imaging (DKI), the resting-state functional connectivity (FC) algorithm, and the least squares support vector regression machine (LSSVRM) were utilized to conduct our study.

MATERIALS AND METHODS

A total of 50 patients and 36 matched healthy controls were prospectively enrolled in our study. All subjects completed the Montreal cognitive assessment scale (MoCA) test. DKI and resting-state functional magnetic resonance imaging were measured. Relationship between DKI parameters, FC, and MoCA scores was evaluated. LSSVRM combined with the whale optimization algorithm (WOA) was used to predict cognitive function scores.

RESULTS

In ESRD patients, altered DKI metrics were identified in 12 brain regions. Furthermore, we observed changes in FC values based on regions of interest (ROIs) in nine brain regions, involved in default mode network (DMN), frontoparietal network (FPN), and the limbic system. Significant correlations among DKI values, FC values, and MoCA scores were found. To some extent, altered FC showed significant correlations with changed DKI parameters. Furthermore, optimized prediction models were applied to more accurately predict the cognitive function associated with ESRD patients.

CONCLUSION

Micro-structural and functional brain changes were found in ESRD patients, which may account for the onset of cognitive impairment in affected patients. These quantitative parameters combined with our optimized prediction model may be helpful to establish more reliable imaging markers to detect and monitor cognitive impairment associated with ESRD.

Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC.

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum

Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters β_SUB and D_SUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19-78 years was used in this study. Results revealed that anomalous diffusion model parameters α and β have shown consistent positive correlation with age in the corpus callosum, indicating α and β are sensitive to tissue microstructural changes in ageing.

Mapping white matter maturational processes and degrees on neonates by diffusion kurtosis imaging with multiparametric analysis

White matter maturation has been characterized by diffusion tensor (DT) metrics. However, maturational processes and degrees are not fully investigated due to limitations of univariate approaches and limited specificity/sensitivity. Diffusion kurtosis imaging (DKI) provides kurtosis tensor (KT) and white matter tract integrity (WMTI) metrics, besides DT metrics. Therefore, we tried to investigate performances of DKI with the multiparametric analysis in characterizing white matter maturation. Developmental changes in metrics were investigated by using tract-based spatial statistics and the region of interest analysis on 50 neonates with postmenstrual age (PMA) from 37.43 to 43.57 weeks. Changes in metrics were combined into various patterns to reveal different maturational processes. Mahalanobis distance based on DT metrics (D_M,DT ) and that combing DT and KT metrics (D_M,DT-KT ) were computed, separately. Performances of D_M,DT-KT and D_M,DT were compared in revealing correlations with PMA and the neurobehavioral score. Compared with DT metrics, WMTI metrics demonstrated additional changing patterns. Furthermore, variations of D_M,DT-KT across regions were in agreement with the maturational sequence. Additionally, D_M,DT-KT demonstrated stronger negative correlations with PMA and the neurobehavioral score in more regions than D_M,DT . Results suggest that DKI with the multiparametric analysis benefits the understanding of white matter maturational processes and degrees on neonates.

Microstructural changes of white matter assessed with diffusional kurtosis imaging in extremely preterm infants with severe intraventricular hemorrhage

OBJECTIVE

Intraventricular hemorrhage (IVH) is a serious neurological complication in premature infants. This study aimed to investigate the white matter impairments and neurodevelopmental outcomes of severe IVH in extremely preterm infants with gestation age less than 28 weeks.

METHODS

We retrospectively evaluated the extremely preterm infants between 2017 and 2020. Neurodevelopmental outcomes were evaluated with the Bayley Scales of Infant and Toddler Development-III at 2 years of corrected age. Diffusional kurtosis imaging (DKI) was employed to evaluate the microstructural changes in white matter tracts. Mean kurtosis (MK) and fractional anisotropy (FA) values of DKI were measured in the brain regions including posterior limbs of the internal capsule (PLIC) and the corpus callosum at term equivalent age.

RESULTS

Of 32 extremely preterm infants with severe IVH during the follow-up period, 18 cases were identified as neurodevelopmental impairments. The delay rates of motor and language were 58.4% and 52.7%. The cases with neurodevelopmental impairments had lower MK and FA values in both bilateral PLIC and the corpus callosum. The analysis of multivariable regression models predicting motor and language outcomes at 2 years of corrected age, showed that the decreases of MK values in both PLIC and the corpus callosum at the term equivalent age contributed to a significantly increased risk of neurodevelopmental impairments (all p < 0.05). During follow-up period, obvious loss of nerve fiber bundles was observed with DKI tractography.

CONCLUSION

Motor and language abilities at age 2 years were associated with MK values of DKI at the term equivalent age in both PLIC and the corpus callosum of extremely preterm infants with severe IVH. The evaluation of white matter microstructural changes with MK values might provide feasible indicators of neurodevelopmental outcomes of extremely preterm infants with severe intraventricular hemorrhage.

Diffusion MRI in Peripheral Nerves: Optimized b Values and the Role of Non-Gaussian Diffusion

Background Diffusion-weighted imaging (DWI) provides specific in vivo information about tissue microstructure, which is increasingly recognized for various applications outside the central nervous system. However, standard sequence parameters are commonly adopted from optimized central nervous system protocols, thus potentially neglecting differences in tissue-specific diffusional behavior. Purpose To characterize the optimal tissue-specific diffusion imaging weighting scheme over the b domain in peripheral nerves under physiologic and pathologic conditions. Materials and Methods In this prospective cross-sectional study, 3-T MR neurography of the sciatic nerve was performed in healthy volunteers (n = 16) and participants with type 2 diabetes (n = 12). For DWI, 16 b values in the range of 0-1500 sec/mm² were acquired in axial and radial diffusion directions of the nerve. With a region of interest-based approach, diffusion-weighted signal behavior as a function of b was estimated using standard monoexponential, biexponential, and kurtosis fitting. Goodness of fit was assessed to determine the optimal b value for two-point DWI/diffusion tensor imaging (DTI). Results Non-Gaussian diffusional behavior was observed beyond b values of 600 sec/mm² in the axial and 800 sec/mm² in the radial diffusion direction in both participants with diabetes and healthy volunteers. Accordingly, the biexponential and kurtosis models achieved a better curve fit compared with the standard monoexponential model (Akaike information criterion >99.9% in all models), but the kurtosis model was preferred in the majority of cases. Significant differences between healthy volunteers and participants with diabetes were found in the kurtosis-derived parameters D_k and K. The results suggest an upper bound b value of approximately 700 sec/mm² for optimal standard DWI/DTI in peripheral nerve applications. Conclusion In MR neurography, an ideal standard diffusion-weighted imaging/diffusion tensor imaging protocol with b = 700 sec/mm² is suggested. This is substantially lower than in the central nervous system due to early-occurring non-Gaussian diffusion behavior and emphasizes the need for tissue-specific b value optimization. Including higher b values, kurtosis-derived parameters may represent promising novel imaging markers of peripheral nerve disease. ©RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Jang and Du in this issue.

Diffusion-Weighted Imaging: Recent Advances and Applications

Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.

Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI

Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.

White matter tracts in Bipolar Disorder patients: A comparative study based on diffusion kurtosis and tensor imaging

BACKGROUND

Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), is a powerful tool for studying human brain.The purpose is to investigate differences between DKI and DTI by comparing parameters in same analysis methods with bipolar disorder (BD) patients.

METHODS

In this study, we attained in 47 BD patients and 49 age-, sex-, and education-matched healthy controls, complimented DTI and DKI scanning and got Fractional Anisotropy (FA), Mean Diffusion (MD) and Mean Kurtosis (MK). Voxel-wise statistical analysis was performed by the tract-based spatial statistics (TBSS) analysis and atlas-based regional data analysis.

RESULTS

TBSS analysis showed more widespread regions and higher fidelity in DKI parameters than DTI parameters with the same p-value threshold, and DKI parameters showed significant alterations after Family-Wise Error correction. The DKI-FA value in the corpus callosum, bilateral cingulum (cingulate gyrus), bilateral superior corona radiata, left anterior corona radiata and left posterior corona radiata of BD patients was negatively correlated with the duration of illness. In the atlas-based regional data analysis, the effect size of DTI-FA, DTI-MD, DKI-FA and DKI-MD were quantified using Cohen's d value. DKI-FA and DKI-MD demonstrated more between-group different regions and the higher (p < 0.001) absolute Cohen's d value than DTI-FA.

LIMITATIONS

This study did not consider the difference between sub-types of BD.

CONCLUSIONS

Compared to DTI parameters, DKI parameters were more sensitive and stable to probe the local microstructure, and particularly powerful to exploit cerebral alterations in BD patients.

Toward more robust and reproducible diffusion kurtosis imaging

PURPOSE

The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values.

THEORY AND METHODS

A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit.

RESULTS

The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast.

CONCLUSION

Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.

Optimized bias and signal inference in diffusion-weighted image analysis (OBSIDIAN)

PURPOSE

Correction of Rician signal bias in magnitude MR images.

METHODS

A model-based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation σ g on a pixel-by-pixel basis in magnitude MR images. A precomputed function that relates absolute residuals between measured signals and model fit to σ g is used to iteratively estimate σ g . The feasibility of the method is evaluated and compared to maximum likelihood estimation (MLE) for diffusion signal decay simulations and diffusion-weighted images of the prostate considering 21 linearly spaced b-values from 0 to 3000 s/mm² . A multidirectional analysis was performed with publically available brain data.

RESULTS

Model simulations show that the Rician bias correction algorithm is fast, with an accuracy and precision that is on par to model-based MLE and direct fitting in the case of pure Gaussian noise. Increased accuracy in parameter prediction in a low signal-to-noise ratio (SNR) scenario is ideally achieved by using a composite of multiple signal decays from neighboring voxels as input for the algorithm. For patient data, good agreement with high SNR reference data of diffusion in prostate is achieved.

CONCLUSIONS

OBSIDIAN is a novel, alternative, simple to implement approach for rapid Rician bias correction applicable in any case where differences between true signal decay and underlying model function can be considered negligible in comparison to noise. The proposed composite fitting approach permits accurate parameter estimation even in typical clinical scenarios with low SNR, which significantly simplifies comparison of complex diffusion parameters among studies.

The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy

The effectivity of diffusion-weighted MRI methods in detecting the epileptogenic zone (EZ) was tested. Patients with refractory epilepsy (N=25) who subsequently underwent resective surgery were recruited. First, the extent of white matter (WM) asymmetry from mean kurtosis (MK) was calculated in order to detect the lobe with the strongest impairment. Second, a newly developed metric was used, reflecting a selection of brain areas with concurrently increased mean Diffusivity, reduced fractional Anisotropy, and reduced mean Kurtosis (iDrArK). A two-step EZ detection was performed as (1) lobe-specific detection, (2) iDrArK voxel-wise detection (with a possible lobe-specific restriction if the result of the first step was significant in a given subject). The method results were compared with the surgery resection zones. From the whole cohort (N=25), the numbers of patients with significant results were: 10 patients in lobe detection and 9 patients in EZ detection. From these subsets of patients with significant results, the impaired lobe was successfully detected with 100% accuracy; the EZ was successfully detected with 89% accuracy. The detection of the EZ using iDrArK was substantially more successful when compared with solo diffusional parameters (or their pairwise combinations). For a subgroup with significant results from step one (N=10), iDrArK without lobe restriction achieved 37.5% accuracy; lobe-restricted iDrArK achieved 100% accuracy. The study shows the plausibility of MK for detecting widespread WM changes and the benefit of combining different diffusional voxel-wise parameters.

Diffusion MRI detects basal forebrain cholinergic abnormalities in the 3xTg-AD mouse model of Alzheimer's disease

Degeneration of the basal forebrain (BF) is detected early in the course of Alzheimer's disease (AD). Reduction in the number of BF cholinergic (ChAT) neurons associated with age-related hippocampal cholinergic neuritic dystrophy is described in the 3xTg-AD mouse model; however, no prior diffusion MRI (dMRI) study has explored the presence of BF alterations in this model. Here we investigated the ability of diffusion MRI (dMRI) to detect abnormalities in BF microstructure for the 3xTg-AD mouse model, along with related pathology in the hippocampus (HP) and white matter (WM) tracks comprising the septo-hippocampal pathway. 3xTg-AD and normal control (NC) mice were imaged in vivo using the specific dMRI technique known as diffusional kurtosis imaging (DKI) at 2, 8, and 15 months of age, and 8 dMRI parameters were measured at each time point. Our results revealed significant lower dMRI values in the BF of 2 months-old 3xTg-AD mice compared with NC mice, most likely related to the increased number of ChAT neurons seen in this AD mouse model at this age. They also showed significant age-related dMRI changes in the BF of both groups between 2 and 8 months of age, mainly a decrease in fractional anisotropy and axial diffusivity, and an increase in radial kurtosis. These dMRI changes in the BF may be reflecting the complex aging and pathological microstructural changes described in this region. Group differences and age-related changes were also observed in the HP, fimbria (Fi) and fornix (Fx). In the HP, diffusivity values were significantly higher in the 2 months-old 3xTg-AD mice, and the HP of NC mice showed a significant increase in axial kurtosis after 8 months, reflecting a normal pattern of increased fiber density complexity, which was not seen in the 3xTg-AD mice. In the Fi, mean and radial diffusivity values were significantly higher, and fractional anisotropy, radial kurtosis and kurtosis fractional anisotropy were significantly lower in the 2 months-old 3xTg-AD mice. The age trajectories for both NC and TG mice in the Fi and Fx were similar between 2 and 8 months, but after 8 months there was a significant decrease in diffusivity metrics associated with an increase in kurtosis metrics in the 3xTg-AD mice. These later HP, Fi and Fx dMRI changes probably reflect the growing number of dystrophic neurites and AD pathology progression in the HP, accompanied by WM disruption in the septo-hippocampal pathway. Our results demonstrate that dMRI can detect early cytoarchitectural abnormalities in the BF, as well as related aging and neurodegenerative changes in the HP, Fi and Fx of the 3xTg-AD mice. Since DKI is widely available on clinical scanners, these results also support the potential of the considered dMRI parameters as in vivo biomarkers for AD disease progression.

Diffusion kurtosis imaging for the assessment of renal fibrosis of chronic kidney disease: A preliminary study

PURPOSE

To investigate the potential of diffusion kurtosis imaging (DKI) for the assessment of renal fibrosis in chronic kidney disease (CKD), using histopathology as the reference standard.

METHODS

Eighty-nine CKD patients and twenty healthy volunteers were recruited in this study. DKI was performed in all participants and all CKD patients received renal biopsy. The values of mean diffusivity (MD) and mean kurtosis (MK) in the renal cortex and medulla were compared between CKD patients and healthy volunteers. The Spearman correlation coefficient was calculated to assess the relationship between MD, MK values and the estimated glomerular filtration rate (eGFR), serum creatinine (SCr), 24 h urinary protein (24 h-UPRO), histopathological fibrosis score.

RESULTS

The medullary MD values were significantly lower than cortex, while the cortical MK values were significantly lower than medulla for all participants. Renal parenchymal MD values were significantly lower in the CKD patients than healthy controls, whereas MK values were significantly higher in the CKD patients than healthy controls. In the CKD patients, the significantly negative correlation was observed between the renal parenchymal MD values and the 24 h-UPRO, SCr, histopathological fibrosis score, as well as between the renal parenchymal MK values and the eGFR, while the significantly positive correlation was found between the renal parenchymal MD values and the eGFR, as well as between the renal parenchymal MK values and the 24 h-UPRO, SCr, histopathological fibrosis score.

CONCLUSION

DKI shows great potential in the noninvasive assessment of renal fibrosis in CKD.

Characterization of Brain Microstructural Abnormalities in High Myopia Patients: A Preliminary Diffusion Kurtosis Imaging Study

Objective

To evaluate microstructural damage in high myopia (HM) patients using 3T diffusion kurtosis imaging (DKI).

Materials and Methods

This prospective study included 30 HM patients and 33 age- and sex-matched healthy controls (HCs) with DKI. Kurtosis parameters including kurtosis fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) as well as diffusion metrics including FA, mean diffusivity, axial diffusivity (AD), and radial diffusivity derived from DKI were obtained. Group differences in these metrics were compared using tract-based spatial statistics. Partial correlation analysis was used to evaluate correlations between microstructural changes and disease duration.

Results

Compared to HCs, HM patients showed significantly reduced AK, RK, MK, and FA and significantly increased AD, predominately in the bilateral corticospinal tract, right inferior longitudinal fasciculus, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and left thalamus (all p < 0.05, threshold-free cluster enhancement corrected). In addition, DKI-derived kurtosis parameters (AK, RK, and MK) had negative correlations (r = −0.448 to −0.376, all p < 0.05) and diffusion parameter (AD) had positive correlations (r = 0.372 to 0.409, all p < 0.05) with disease duration.

Conclusion

HM patients showed microstructural alterations in the brain regions responsible for motor conduction and vision-related functions. DKI is useful for detecting white matter abnormalities in HM patients, which might be helpful for exploring and monitoring the pathogenesis of the disease.