A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging

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.

Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T

Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.

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.

Widespread effects of dMRI data quality on diffusion measures in children

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

Structural network performance for early diagnosis of spastic cerebral palsy in periventricular white matter injury

Periventricular white matter injury (PWMI) is a common cause of spastic cerebral palsy (SCP). Diffusion tensor imaging (DTI) shows high sensitivity but moderate specificity for predicting SCP. The limited specificity may be due to the diverse and extensive brain injuries seen in infants with PWMI. We enrolled 72 infants with corrected age from 6 to 18 months in 3 groups: PWMI with SCP (n = 20), non-CP PWMI (n = 19), and control (n = 33) groups. We compared DTI-based brain network properties among the three groups and evaluated the diagnostic performance of brain network properties for SCP in PWMI infants. Our results show abnormal global parameters (reduced global and local efficiency, and increased shortest path length), and local parameters (reduced node efficiency) in the PWMI with SCP group. On logistic regression, the combined node efficiency of the bilateral precentral gyrus and right middle frontal gyrus had a high sensitivity (90%) and specificity (95%) for differentiating PWMI with SCP from non-CP PWMI, and significantly correlated with the Gross Motor Function Classification System scores. This study confirms that DTI-based brain network has great diagnostic performance for SCP in PWMI infants, and the combined node efficiency improves the diagnostic accuracy.

Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: application to neonatal white matter development

Background

Site-specific variations are challenges for pooling analyses in multi-center studies. This work aims to propose an inter-site harmonization method based on dual generative adversarial networks (GANs) for diffusion tensor imaging (DTI) derived metrics on neonatal brains.

Results

DTI-derived metrics (fractional anisotropy, FA; mean diffusivity, MD) are obtained on age-matched neonates without magnetic resonance imaging (MRI) abnormalities: 42 neonates from site 1 and 42 neonates from site 2. Significant inter-site differences of FA can be observed. The proposed harmonization approach and three conventional methods (the global-wise scaling, the voxel-wise scaling, and the ComBat) are performed on DTI-derived metrics from two sites. During the tract-based spatial statistics, inter-site differences can be removed by the proposed dual GANs method, the voxel-wise scaling, and the ComBat. Among these methods, the proposed method holds the lowest median values in absolute errors and root mean square errors. During the pooling analysis of two sites, Pearson correlation coefficients between FA and the postmenstrual age after harmonization are larger than those before harmonization. The effect sizes (Cohen’s d between males and females) are also maintained by the harmonization procedure.

Conclusions

The proposed dual GANs-based harmonization method is effective to harmonize neonatal DTI-derived metrics from different sites. Results in this study further suggest that the GANs-based harmonization is a feasible pre-processing method for pooling analyses in multi-center studies.

Prediction of adverse motor outcome for neonates with punctate white matter lesions by MRI images using radiomics strategy: protocol for a prospective cohort multicentre study

INTRODUCTION

Punctate white matter lesions (PWML) are prevalent white matter disease in preterm neonates, and may cause motor disorders and even cerebral palsy. However, precise individual-based diagnosis of lesions that result in an adverse motor outcome remains unclear, and an effective method is urgently needed to guide clinical diagnosis and treatment. Advanced radiomics for multiple modalities data can provide a possible look for biomarkers and determine prognosis quantitatively. The study aims to develop and validate a model for prediction of adverse motor outcomes at a corrected age (CA) of 24 months in neonates with PWML.

METHODS AND ANALYSIS

A prospective cohort multicentre study will be conducted in 11 Chinese hospitals. A total of 394 neonates with PWML confirmed by MRI will undergo a clinical assessment (modified Neonatal Behavioural Assessment Scale). At a CA of 18 months, the motor function will be assessed by Bayley Scales of Infant and Toddler Development-III (Bayley-III). Mild-to-severe motor impairments will be confirmed using the Bayley-III and Gross Motor Function Classification System at a CA of 24 months. During the data collection, the perinatal and clinical information will also be recorded. According to the radiomics strategy, the extracted imaging features and clinical information will be combined for exploratory analysis. After using multiple-modelling methodology, the accuracy, sensitivity and specificity will be computed. Internal and external validations will be used to evaluate the performance of the radiomics model.

ETHICS AND DISSEMINATION

This study has been approved by the institutional review board of The First Affiliated Hospital of Xi'an Jiaotong University (XJTU1AF2015LSK-172). All parents of eligible participants will be provided with a detailed explanation of the study and written consent will be obtained. The results of this study will be published in peer-reviewed journals and presented at local, national and international conferences.

TRIAL REGISTRATION NUMBER

NCT02637817; Pre-results.

Early white matter injuries associated with dopamine transporter dysfunction in patients with acute CO intoxication: A diffusion kurtosis imaging and Tc-99m TRODAT-1 SPECT study

PURPOSE

Patients with CO intoxication were demonstrated to exhibit white matter (WM) injuries, changes in substantia nigra, dopamine transporter dysfunctions of striatum and Parkinsonism symptoms. We aimed to investigate the relationship between WM injuries of dopaminergic pathways and dopamine transporter dysfunctions of the striatum in patients with acute CO intoxication using both diffusion kurtosis imaging (DKI) and single photon-emission computed tomography (SPECT).

MATERIALS AND METHODS

Seventeen patients with acute CO intoxication and 19 age- and gender-matched healthy subjects were enrolled. DKI data were acquired from all participants and Tc-99m-TRODAT-1 SPECT scan was performed on each patient. DKI datasets were fitted to obtain axial, radial and mean diffusivity, fractional anisotropy, axial, radial and mean kurtosis for voxel-based comparison. In addition, the TRODAT-1 binding ratio of the striatum was calculated using the occipital cortices as a reference. In significant regions, correlational analysis was performed to understand the relationship between DKI indices and TRODAT-1 binding ratio.

RESULTS

The results showed that DKI indices were significantly altered in multiple WM regions broadly involving the basal ganglia-thalamocortical circuit and nigrostriatal pathway. The correlation analysis further revealed significant correlations between DKI indices and the TRODAT-1 binding ratio in the nigrostriatal pathway (absolute correlation coefficients ranged from 0.5992 to 0.6950, p<0.05), suggesting that CO-induced early WM injuries were associated with dopamine transporter dysfunctions of striatum.

CONCLUSION

We concluded that DKI and Tc-99m-TRODAT-1 SPECT scans were helpful in early detection of global WM injuries associated with dysfunctions of dopamine transporter in patients with acute CO intoxication.

KEY POINTS

• Voxel-based diffusion kurtosis imaging analysis was helpful in globally detecting early white matter injuries in patients with acute CO intoxication. • CO-induced early white matter injuries were broadly located in basal ganglia-thalamocortical circuit and nigrostriatal pathway. • Early white matter injuries in dopaminergic pathways were significantly correlated with dopamine transporter dysfunctions of the striatum.

Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress

Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.

Sparse registration of diffusion weighted images

BACKGROUND AND OBJECTIVE

Registration is a critical step in group analysis of diffusion weighted images (DWI). Image registration is also necessary for construction of white matter atlases that can be used to identify white matter changes. A challenge in the registration of DWI is that the orientation of the fiber bundles should be considered in the process, making their registration more challenging than that of the scalar images. Most of the current registration methods use a model of diffusion profile, limiting the method to the used model.

METHODS

We propose a model-independent method for DWI registration. The proposed method uses a multi-level free-form deformation (FFD), a sparse similarity measure, and a dictionary. We also propose a synthesis K-SVD algorithm for sparse interpolation of images during the registration process. We utilize two dictionaries: analysis dictionary is learned based on diffusion signals while synthesis dictionary is generated based on image patches. The proposed multi-level approach registers anatomical structures at different scales. T-test is used to determine the significance of the differences between different methods.

RESULTS

We have shown the efficiency of the proposed approach using real data. The method results in smaller generalized fractional anisotropy (GFA) root mean square (RMS) error (0.05 improvements, p = 0.0237) and angular error (0.37 ° improvement, p = 0.0330) compared to the large deformation diffeomorphic metric mapping (LDDMM) method and advanced normalization tools (ANTs).

CONCLUSION

Sparse registration of diffusion signals enables registration of diffusion weighted images without using a diffusion model.

Characterization of Extensive Microstructural Variations Associated with Punctate White Matter Lesions in Preterm Neonates

BACKGROUND AND PURPOSE

Punctate white matter lesions are common in preterm neonates. Neurodevelopmental outcomes of the neonates are related to the degree of extension. This study aimed to characterize the extent of microstructural variations for different punctate white matter lesion grades.

MATERIALS AND METHODS

Preterm neonates with punctate white matter lesions were divided into 3 grades (from mild to severe: grades I-III). DTI-derived fractional anisotropy, axial diffusivity, and radial diffusivity between patients with punctate white matter lesions and controls were compared with Tract-Based Spatial Statistics and tract-quantification methods.

RESULTS

Thirty-three preterm neonates with punctate white matter lesions and 33 matched controls were enrolled. There were 15, 9, and 9 patients, respectively, in grades I, II, and III. Punctate white matter lesions were mainly located in white matter adjacent to the lateral ventricles, especially regions lateral to the trigone, posterior horns, and centrum semiovale and/or corona radiata. Extensive microstructural changes were observed in neonates with grade III punctate white matter lesions, while no significant changes in DTI metrics were found for grades I and II. A pattern of increased axial diffusivity, increased radial diffusivity, and reduced/unchanged fractional anisotropy was found in regions adjacent to punctate white matter lesion sites seen on T1WI and T2WI. Unchanged axial diffusivity, increased radial diffusivity, and reduced/unchanged fractional anisotropy were observed in regions distant from punctate white matter lesion sites.

CONCLUSIONS

White matter microstructural variations were different across punctate white matter lesion grades. Extensive change patterns varied according to the distance to the lesion sites in neonates with severe punctate white matter lesions. These findings may help in determining the outcomes of punctate white matter lesions and selecting treatment strategies.

Rapid and reliable tract-based spatial statistics pipeline for diffusion tensor imaging in the neonatal brain: Applications to the white matter development and lesions

PURPOSE

The relatively poor image contrast and variation in the neonatal brain size are technical challenges associated with the typical tract-based spatial statistics (TBSS) for the target identification and normalization. This study aimed to develop a rapid and reliable pipeline for the neonatal TBSS.

MATERIALS AND METHODS

A rapid TBSS strategy was proposed based on the group-wise target choice for fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). The most representative subject of the entire group was identified via (a) initial group-averaged template creation (b) followed by identification of the target with the minimum warp displacement score between the individual and the group-averaged template. The computation time, registration quality, measurement of regional values, and statistical analyses were evaluated in two applications: brain white matter development in normal term neonates, and alterations in preterm neonates with white matter lesions compared to the matched controls. These performances in the proposed pipeline were compared with those in the typical and previous neonatal TBSS workflows.

RESULTS

Target choice using the proposed strategy is faster, compared with the previous TBSS pipelines, especially with the increase of the sample size. Registration errors between individuals and the target are assessed through warp displacement scores. Smaller warp displacement scores are observed for the proposed method than the typical pipeline. Due to the relatively accurate registration, the proposed method results in lower standard deviations and higher averaged values of FA across subjects. Additionally, more areas with significant changes related to the development and white matter lesions are detected using the proposed method than previous TBSS pipelines. The proposed pipeline provides stronger correlation between FA and gestational age, and larger difference between preterm neonates with white matter lesions and controls.

CONCLUSION

The proposed TBSS pipeline improves the efficiency and reliability of the DTI analysis in the neonatal brain.

Differentiating T2 hyperintensity in neonatal white matter by two-compartment model of diffusional kurtosis imaging

In conventional neonatal MRI, the T2 hyperintensity (T2h) in cerebral white matter (WM) at term-equivalent age due to immaturity or impairment is still difficult to identify. To clarify such issue, this study used the metrics derived from a two-compartment WM model of diffusional kurtosis imaging (WM-DKI), including intra-axonal, extra-axonal axial and radial diffusivities (D_a, D_e,// and D_e,⊥), to compare WM differences between the simple T2h and normal control for both preterm and full-term neonates, and between simple T2h and complex T2h with hypoxic-ischemic encephalopathy (HIE). Results indicated that compared with control, the simple T2h showed significantly increased D_e,// and D_e,⊥, but no significant change in D_a in multiple premyelination regions, indicative of expanding extra-axonal diffusion microenvironment; while myelinated regions showed no changes. However, compared with simple T2h, the complex T2h with HIE had decreased D_a, increased D_e,⊥ in both premyelination and myelinated regions, indicative of both intra- and extra-axonal diffusion alterations. While diffusion tensor imaging (DTI) failed to distinguish simple T2h from complex T2h with HIE. In conclusion, superior to DTI-metrics, WM-DKI metrics showed more specificity for WM microstructural changes to distinguish simple T2h from complex T2h with HIE.

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

BACKGROUND

Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.

METHODS

All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.

RESULTS

TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.

CONCLUSION

Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.

A simple noise correction scheme for diffusional kurtosis imaging

PURPOSE

Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis.

METHODS

Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates.

RESULTS

As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR.

CONCLUSION

The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms.