正常肾脏的磁共振扩散峰度成像研究

目的:验证肾脏扩散峰度成像(DKI)的可行性,并明确年龄因素对肾脏水分子扩散特性是否存在影响。方法:用3.0T磁共振扫描仪对年龄范围在20-60岁之间的41名健康志愿者进行磁共振DKI扫描。按年龄因素分四组(20-29岁)、(30-39岁)、(40-49岁)、(50-59岁),行t-test及方差分析及比较不同年龄因素对肾皮质和髓质的分数各向异性(FA)值、平均扩散(MD)值、峰度各向异性(FAK)值、平均峰度(MK)值的影响并进行统计学分析。结果:正常肾皮质的FA值、FAK值、MK值(0.327±0.047,0.325±0.088,0.688±0.087)显著低于髓质(0.389±0.062,0.396±0.091,0.802±0.124);而正常肾皮质MD值(1.633±0.157)显著高于髓质(1.588±0.162)。不同年龄段之间的FA、MD、FAK、MK值均无统计学差异(P0.05);左、右肾之间的比较无统计学差异(P0.05)。结论:正常肾脏DKI良好的揭示了肾皮质与髓质的水分子扩散特性;年龄因素对肾脏水分子扩散特性没有影响。     

Local Diffusion Homogeneity (LDH): An Inter-Voxel Diffusion MRI Metric for Assessing Inter-Subject White Matter Variability

Many diffusion parameters and indices (e.g., fractional anisotropy [FA] and mean diffusivity [MD]) have been derived from diffusion magnetic resonance imaging (MRI) data. These parameters have been extensively applied as imaging markers for localizing white matter (WM) changes under various conditions (e.g., development, degeneration and disease). However, the vast majority of the existing parameters is derived from intra-voxel analyses and represents the diffusion properties solely within the voxel unit. Other types of parameters that characterize inter-voxel relationships have been largely overlooked. In the present study, we propose a novel inter-voxel metric referred to as the local diffusion homogeneity (LDH). This metric quantifies the local coherence of water molecule diffusion in a model-free manner. It can serve as an additional marker for evaluating the WM microstructural properties of the brain. To assess the distinguishing features between LDH and FA/MD, the metrics were systematically compared across space and subjects. As an example, both the LDH and FA/MD metrics were applied to measure age-related WM changes. The results indicate that LDH reveals unique inter-subject variability in specific WM regions (e.g., cerebral peduncle, internal capsule and splenium). Furthermore, there are regions in which measurements of age-related WM alterations with the LDH and FA/MD metrics yield discrepant results. These findings suggest that LDH and FA/MD have different sensitivities to specific WM microstructural properties. Taken together, the present study shows that LDH is complementary to the conventional diffusion-MRI markers and may provide additional insights into inter-subject WM variability. Further studies, however, are needed to uncover the neuronal mechanisms underlying the LDH.     

脊髓损伤扩散峰度成像参数与脊髓型颈椎病患者神经功能评分的相关性研究

目的:研究扩散峰度成像(DKI)参数与脊髓型颈椎病(CSM)患者神经功能评分的相关性及临床意义。方法:选取2018年12月至2019年6月本院收治的CSM患者37例作为研究组及健康志愿者的30例作为对照组,采用GE3.0磁共振机分别对两组人员行磁共振成像(MRI)及DKI扫描,观察其影像学特征及DKI参数的变化情况,并分析DKI参数值与临床行为评分的相关性。结果:所有研究对象的MRI图像均符合诊断要求。志愿者颈髓形态完整、信号均匀;不同年龄组颈髓平均弥散各向异性分数(FA)值、平均弥散峰度(MK)值比较差异无统计学意义(P>0.05)。根据MRI的T2加权图像上椎管受压程度及脊髓信号改变,将实验组分为A、B、C组,对照组与各实验组的MK值、FA值比较差异有统计学意义(P<0.05)。实验组FA值与mJOA评分呈显著正相关(r=0.34),与NDI评分呈负相关(r=-0.38);MK值与mJOA评分呈正相关(r=0.67),与NDI评分呈负相关(r=-0.46)。结论:DKI序列对CSM诊断具有参考较高价值,其参数与临床行为评分关系密切,能够评估早期CSM患者的脊髓损伤情况,并为诊断和治疗提供参考。     

Characterization of Breast Tumors Using Diffusion Kurtosis Imaging (DKI)

Aim

The aim of this study was to investigate and evaluate the role of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in characterizing breast lesions.

Materials and Methods

One hundred and twenty-four lesions in 103 patients (mean age: 57±14 years) were evaluated by MR DKI performed with 7 b-values of 0, 250, 500, 750, 1,000, 1,500, 2,000 s/mm² and dynamic contrast-enhanced (DCE) MR imaging. Breast lesions were histologically characterized and DKI related parameters—mean diffusivity (MD) and mean kurtosis (MK)—were measured. The MD and MK in normal fibroglandular breast tissue, benign and malignant lesions were compared by One-way analysis of variance (ANOVA) with Tukey''s multiple comparison test. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of MD and MK in the diagnosis of breast lesions.

Results

The benign lesions (n = 42) and malignant lesions (n = 82) had mean diameters of 11.4±3.4 mm and 35.8±20.1 mm, respectively. The MK for malignant lesions (0.88±0.17) was significantly higher than that for benign lesions (0.47±0.14) (P<0.001), and, in contrast, MD for benign lesions (1.97±0.35 (10⁻³ mm²/s)) was higher than that for malignant lesions (1.20±0.31 (10⁻³ mm²/s)) (P<0.001). At a cutoff MD/MK 1.58 (10⁻³ mm²/s)/0.69, sensitivity and specificity of MD/MK for the diagnosis of malignant were 79.3%/84.2% and 92.9%/92.9%, respectively. The area under the curve (AUC) is 0.86/0.92 for MD/MK.

Conclusions

DKI could provide valuable information on the diffusion properties related to tumor microenvironment and increase diagnostic confidence of breast tumors.     

Initial Application of Diffusional Kurtosis Imaging in Evaluating Brain Development of Healthy Preterm Infants

ObjectiveTo explore the parametric characteristics of diffusional kurtosis imaging (DKI) in the brain development of healthy preterm infants.ResultsMK and FA values were positively correlated with PMA in most selected WM regions, such as the posterior limbs of the internal capsule (PLIC) and the splenium of the corpus callosum (SCC). The positive correlation between MK value and PMA in the deep GM region was higher than that between FA and PMA. The MK value gradually decreased from the PLIC to the cerebral lobe. In addition, DKI parameters exhibited subtle differences in the parietal WM between the preterm and term control groups.ConclusionsMK may serve as a more reliable imaging marker of the normal myelination process and provide a more robust characterization of deep GM maturation.     

Differentiation of glioma malignancy grade using diffusion MRI

Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500 s/mm² with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p < 0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed.     

Evaluation of Non-Local Means Based Denoising Filters for Diffusion Kurtosis Imaging Using a New Phantom

Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW) image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM). Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE), Bias and standard deviation (Std). The result of the comparison based on the phantom was consistent with those based on real datasets.     

Extrapolation-Based References Improve Motion and Eddy-Current Correction of High B-Value DWI Data: Application in Parkinson’s Disease Dementia

Purpose

Conventional motion and eddy-current correction, where each diffusion-weighted volume is registered to a non diffusion-weighted reference, suffers from poor accuracy for high b-value data. An alternative approach is to extrapolate reference volumes from low b-value data. We aim to compare the performance of conventional and extrapolation-based correction of diffusional kurtosis imaging (DKI) data, and to demonstrate the impact of the correction approach on group comparison studies.

Methods

DKI was performed in patients with Parkinson’s disease dementia (PDD), and healthy age-matched controls, using b-values of up to 2750 s/mm². The accuracy of conventional and extrapolation-based correction methods was investigated. Parameters from DTI and DKI were compared between patients and controls in the cingulum and the anterior thalamic projection tract.

Results

Conventional correction resulted in systematic registration errors for high b-value data. The extrapolation-based methods did not exhibit such errors, yielding more accurate tractography and up to 50% lower standard deviation in DKI metrics. Statistically significant differences were found between patients and controls when using the extrapolation-based motion correction that were not detected when using the conventional method.

Conclusion

We recommend that conventional motion and eddy-current correction should be abandoned for high b-value data in favour of more accurate methods using extrapolation-based references.     

Inter Subject Variability and Reproducibility of Diffusion Tensor Imaging within and between Different Imaging Sessions

The aim of these studies was to provide reference data on intersubject variability and reproducibility of diffusion tensor imaging. Healthy volunteers underwent imaging on two occasions using the same 3T Siemens Verio magnetic resonance scanner. At each session two identical diffusion tensor sequences were obtained along with standard structural imaging. Fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity maps were created and regions of interest applied in normalised space. The baseline data from all 26 volunteers were used to calculate the intersubject variability, while within session and between session reproducibility were calculated from all the available data. The reproducibility of measurements were used to calculate the overall and within session 95% prediction interval for zero change. The within and between session reproducibility data were lower than the values for intersubject variability, and were different across the brain. The regional mean (range) coefficient of variation figures for within session reproducibility were 2.1 (0.9–5.5%), 1.2 (0.4–3.9%), 1.2 (0.4–3.8%) and 1.8 (0.4–4.3%) for fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity, and were lower than between session reproducibility measurements (2.4 (1.1–5.9%), 1.9 (0.7–5.7%), 1.7 (0.7–4.7%) and 2.4 (0.9–5.8%); p<0.001). The calculated overall and within session 95% prediction intervals for zero change were similar. This study provides additional reference data concerning intersubject variability and reproducibility of diffusion tensor imaging conducted within the same imaging session and different imaging sessions. These data can be utilised in interventional studies to quantify change within a single imaging session, or to assess the significance of change in longitudinal studies of brain injury and disease.     

A Robust Post-Processing Workflow for Datasets with Motion Artifacts in Diffusion Kurtosis Imaging

Purpose

The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).

Materials and methods

The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).

Results

The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).

Conclusion

The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.     

ADC Histograms from Routine DWI for Longitudinal Studies in Cerebral Small Vessel Disease: A Field Study in CADASIL

Diffusion tensor imaging (DTI) histogram metrics are correlated with clinical parameters in cerebral small vessel diseases (cSVD). Whether ADC histogram parameters derived from simple diffusion weighted imaging (DWI) can provide relevant markers for long term studies of cSVD remains unknown. CADASIL patients were evaluated by DWI and DTI in a large cohort study overa6-year period. ADC histogram parameters were compared to those derived from mean diffusivity (MD) histograms in 280 patients using intra-class correlation and Bland-Altman plots. Impact of image corrections applied to ADC maps was assessed and a mixed effect model was used for analyzing the effects of scanner upgrades. The results showed that ADC histogram parameters are strongly correlated to MD histogram parameters and that image corrections have only limited influence on these results. Unexpectedly, scanner upgrades were found to have major effects on diffusion measures with DWI or DTI that can be even larger than those related to patients’ characteristics. These data support that ADC histograms from daily used DWI can provide relevant parameters for assessing cSVD, but the variability related to scanner upgrades as regularly performed in clinical centers should be determined precisely for longitudinal and multicentric studies using diffusion MRI in cSVD.     

Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography

Diffusion tensor imaging (DTI) based fiber tractography (FT) is the most popular approach for investigating white matter tracts in vivo, despite its inability to reconstruct fiber pathways in regions with "crossing fibers." Recently, constrained spherical deconvolution (CSD) has been developed to mitigate the adverse effects of "crossing fibers" on DTI based FT. Notwithstanding the methodological benefit, the clinical relevance of CSD based FT for the assessment of white matter abnormalities remains unclear. In this work, we evaluated the applicability of a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in 25 patients with early Alzheimer's disease. Both CSD and DTI based FT were used to reconstruct two white matter tracts: one with regions of "crossing fibers," i.e., the superior longitudinal fasciculus (SLF) and one which contains only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). The DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD), obtained from these tracts were related to memory function. Our results show that in the tract with "crossing fibers" the relation between FA/MD and memory was stronger with CSD than with DTI based FT. By contrast, in the fiber bundle where one fiber population predominates, the relation between FA/MD and memory was comparable between both tractography methods. Importantly, these associations were most pronounced after adjustment for the planar diffusion coefficient, a measure reflecting the degree of fiber organization complexity. These findings indicate that compared to conventionally applied DTI based FT, CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture.     

Dynamic Responses of Selective Brain White Matter Fiber Tracts to Binge Alcohol and Recovery in the Rat

To determine the dynamics of white matter vulnerability to excessive alcohol consumption, diffusion tensor imaging (DTI) was used in an animal model of alcohol exposure. Quantitative, in vivo fiber tracking results are presented from rats with DTI conducted at 3 time points: baseline; after 4 days of intragastric alcohol to blood alcohol levels of ~250mg/dL; and after one week of recovery. Binge alcohol followed by a week of sobriety resulted in rapidly reversible decreases in fractional anisotropy (FA), a measure of the coherence of fiber tracts, in callosal genu and fimbria-fornix but not splenium; and increases in mean diffusivity (MD), an index of freely diffusing water in tissue, selective to the fimbria-fornix. These effects were confirmed with tract-based spatial statistics (TBSS). The directionality of changes in DTI metrics reproduce those observed in human alcoholism. That a single exposure to binge alcohol can cause substantial transient changes detectable in DTI metrics demonstrates the potential for rapid neuroplasticity.     

Diffusion Tensor Imaging of the Auditory Neural Pathway for Clinical Outcome of Cochlear Implantation in Pediatric Congenital Sensorineural Hearing Loss Patients

Although conventional structural MRI provides vital information in the evaluation of congenital sensorineural hearing loss (SNHL), it is relatively insensitive to white matter microstructure. Our objective was to evaluate possible changes in microstructure of the auditory pathway in children with congenital sensorineural hearing loss (SNHL), and the possible distinction between good and poor outcome of cochlear implantation (CI) patients by using diffusion tensor imaging (DTI). Twenty-four patients with congenital SNHL and 20 healthy controls underwent conventional MRI and DTI examination using a 1.5T MR scanner. The DTI metrics of fractional anisotropy (FA) and mean diffusivity (MD) of six regions of interest (ROIs) positioned along the auditory pathway—the trapezoid body, superior olivary nucleus, inferior colliculus, medial geniculate body, auditory radiation and white matter of Heschl''s gyrus—was measured in all subjects. Among the 24 patients, 8 patients with a categorie of auditory performance (CAP) score over 6 were classified into the good outcome group, and 16 patients with a CAP score below 6 were classified into the poor outcome group. A significant decrease was observed in FA values while MD values remained unchanged at the six ROIs of SNHL patients compared with healthy controls. Compared to good outcome subjects, poor outcome subjects displayed decreased FA values at all of the ROIs. No changes were observed in MD values. Correlation analyses only revealed strong correlations between FA values and CAP scores, and strong correlations between CAP scores and age at implant were also found. No correlations of FA values with age at implant were observed. Our results show that preoperative DTI can be used to evaluate microstructural alterations in the auditory pathway that are not detectable by conventional MR imaging, and may play an important role in evaluating the outcome of CI. Early cochlear implantation might be more effectively to restore hearing in SNHL patients.     

Wallerian degeneration in central nervous system: dynamic associations between diffusion indices and their underlying pathology

Background

Although diffusion tensor imaging has been used to monitor Wallerian degeneration, the exact relationship between the evolution of diffusion indices and its underlying pathology, especially in central nervous system, remains largely unknown. Here we aimed to address this question using a cat Wallerian degeneration model of corticospinal tract.

Methodology/Principal Findings

Twenty-five domestic mature Felis catus were included in the present study. The evolution of diffusion indices, including mean diffusivity (MD), fractional anisotropy (FA), primary (λ1) and transverse eigenvalues (λ23) of the degenerated corticospinal tract, were observed at baseline (before modeling) and at 2, 4, 6, 8, 10, 15, 20, 25, 30, 45 and 60 days after modeling in 4 cats. Pathological examinations were performed at eight time points mentioned above. Wallerian degeneration can be detected as early as the 2nd day after modeling by both diffusion tensor imaging and pathology. According to the evolution of diffusion indices, Wallerian degeneration can be classified into 2 stages. During the early stage (within 8 days after modeling), progressive disintegration of axons and myelin sheaths underlies the decreases in FA and λ1 and the increase in λ23. However, during the late stage (after 8 days), the gradual increases in FA, MD and λ1 and the unchanged λ23 seem to be a comprehensive reflection of the pathological processes including microglia activation, myelin clearance, and astrocytosis.

Conclusions/Significance

Our findings help the understanding of the altered diffusion indices in the context of pathology and suggest that diffusion tensor imaging has the potential to monitor the processes of Wallerian degeneration in the central nervous system in vivo after acute damage.     

Alteration of Fractional Anisotropy and Mean Diffusivity in Glaucoma: Novel Results of a Meta-Analysis of Diffusion Tensor Imaging Studies

Objectives

We hypothesized that a meta-analysis of existing studies may help to reveal significant changes on diffusion tensor imaging (DTI) in patients with glaucoma. Therefore, a meta-analysis was utilized to investigate the possibility that DTI can detect white matter damage in patients with glaucoma.

Methods

The study design and report adhered to the PRISMA Statement guidelines. DTI studies that compared glaucoma patients and controls were surveyed using PubMed, Web of Science and EMBASE (January 2008 to September 2013). Stata was used to analyze the decrease in fractional anisotropy (FA) and increase in mean diffusivity (MD) in the optic nerve and optic radiation in patients with glaucoma.

Results

Eleven DTI studies were identified through a comprehensive literature search, and 10 independent DTI studies of glaucoma patients were eligible for the meta-analysis. A random effects model revealed a significant FA reduction in the optic nerve and optic radiation, as well as a significant MD increase in the tracts. A heterogeneity analysis suggested that FA may be related to glaucoma severity.

Conclusions

Our findings revealed that the optic nerve and optic radiation were vulnerable regions in patients with glaucoma and that FA may be correlated with glaucoma severity and age. Furthermore, this study suggests that magnetic resonance imaging in patients with glaucoma may help to provide objective evidence to aid in the diagnosis and management of glaucoma.     

Transmural heterogeneity of diffusion anisotropy in the sheep myocardium characterized by MR diffusion tensor imaging

The orientation of MRI-measured diffusion tensor in the myocardium has been directly correlated to the tissue fiber direction and widely characterized. However, the scalar anisotropy indexes have mostly been assumed to be uniform throughout the myocardial wall. The present study examines the fractional anisotropy (FA) as a function of transmural depth and circumferential and longitudinal locations in the normal sheep cardiac left ventricle. Results indicate that FA remains relatively constant from the epicardium to the midwall and then decreases (25.7%) steadily toward the endocardium. The decrease of FA corresponds to 7.9% and 12.9% increases in the secondary and tertiary diffusion tensor diffusivities, respectively. The transmural location of the FA transition coincides with the location where myocardial fibers run exactly circumferentially. There is also a significant difference in the midwall-endocardium FA slope between the septum and the posterior or lateral left ventricular free wall. These findings are consistent with the cellular microstructure from histological studies of the myocardium and suggest a role for MR diffusion tensor imaging in characterization of not only fiber orientation but, also, other tissue parameters, such as the extracellular volume fraction.     

Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.     

Diffusion Tensor Imaging Studies of Cervical Spondylotic Myelopathy: A Systemic Review and Meta-Analysis

A meta-analysis was conducted to assess alterations in measures of diffusion tensor imaging (DTI) in the patients of cervical spondylotic myelopathy (CSM), exploring the potential role of DTI as a diagnosis biomarker. A systematic search of all related studies written in English was conducted using PubMed, Web of Science, EMBASE, CINAHL, and Cochrane comparing CSM patients with healthy controls. Key details for each study regarding participants, imaging techniques, and results were extracted. DTI measurements, such as fractional anisotropy (FA), apparent diffusion coefficient (ADC), and mean diffusivity (MD) were pooled to calculate the effect size (ES) by fixed or random effects meta-analysis. 14 studies involving 479 CSM patients and 278 controls were identified. Meta-analysis of the most compressed levels (MCL) of CSM patients demonstrated that FA was significantly reduced (ES -1.52, 95% CI -1.87 to -1.16, P < 0.001) and ADC was significantly increased (ES 1.09, 95% CI 0.89 to 1.28, P < 0.001). In addition, a notable ES was found for lowered FA at C2-C3 for CSM vs. controls (ES -0.83, 95% CI -1.09 to -0.570, P < 0.001). Meta-regression analysis revealed that male ratio of CSM patients had a significant effect on reduction of FA at MCL (P = 0.03). The meta-analysis of DTI studies of CSM patients clearly demonstrated a significant FA reduction and ADC increase compared with healthy subjects. This result supports the use of DTI parameters in differentiating CSM patients from health subjects. Future researches are required to investigate the diagnosis performance of DTI in cervical spondylotic myelopathy.     

Tract specific reproducibility of tractography based morphology and diffusion metrics

Introduction

The reproducibility of tractography is important to determine its sensitivity to pathological abnormalities. The reproducibility of tract morphology has not yet been systematically studied and the recently developed tractography contrast Tract Density Imaging (TDI) has not yet been assessed at the tract specific level.

Materials and Methods

Diffusion tensor imaging (DTI) and probabilistic constrained spherical deconvolution (CSD) tractography are performed twice in 9 healthy subjects. Tractography is based on common space seed and target regions and performed for several major white matter tracts. Tractograms are converted to tract segmentations and inter-session reproducibility of tract morphology is assessed using Dice similarity coefficient (DSC). The coefficient of variation (COV) and intraclass correlation coefficient (ICC) are calculated of the following tract metrics: fractional anisotropy (FA), apparent diffusion coefficient (ADC), volume, and TDI. Analyses are performed both for proximal (deep white matter) and extended (including subcortical white matter) tract segmentations.

Results

Proximal DSC values were 0.70–0.92. DSC values were 5–10% lower in extended compared to proximal segmentations. COV/ICC values of FA, ADC, volume and TDI were 1–4%/0.65–0.94, 2–4%/0.62–0.94, 3–22%/0.53–0.96 and 8–31%/0.48–0.70, respectively, with the lower COV and higher ICC values found in the proximal segmentations.

Conclusion

For all investigated metrics, reproducibility depended on the segmented tract. FA and ADC had relatively low COV and relatively high ICC, indicating clinical potential. Volume had higher COV but its moderate to high ICC values in most tracts still suggest subject-differentiating power. Tract TDI had high COV and relatively low ICC, which reflects unfavorable reproducibility.