MR diffusion kurtosis imaging for neural tissue characterization

Diffusion kurtosis imaging of white matter in bipolar disorder

White matter pathology likely contributes to the pathogenesis of bipolar disorder (BD). Most studies of white matter in BD have used diffusion tensor imaging (DTI), but the advent of more advanced multi-shell diffusion MRI imaging offers the possibility to investigate other aspects of white matter microstructure. Diffusion kurtosis imaging (DKI) extends the DTI model and provides additional measures related to diffusion restriction. Here, we investigated white matter in BD by applying whole-brain voxel-based analysis (VBA) and a network-based connectivity approach using constrained spherical deconvolution tractography to assess differences in DKI and DTI metrics between BD (n = 25) and controls (n = 24). The VBA showed lower mean kurtosis in the corona radiata and posterior association fibers in BD. Regional differences in connectivity were indicated by lower mean kurtosis and kurtosis anisotropy in streamlines traversing the temporal and occipital lobes, and lower mean axial kurtosis in the right cerebellar, thalamo-subcortical pathways in BD. Significant differences were not seen in DTI metrics following FDR-correction. The DKI findings indicate altered connectivity across cortical, subcortical and cerebellar areas in BD. DKI is sensitive to different microstructural properties and is a useful complementary technique to DTI to more fully investigate white matter in BD.

Diffusion kurtosis imaging combined with dynamic susceptibility contrast-enhanced MRI in differentiating high-grade glioma recurrence from pseudoprogression

OBJECTIVES

To compare the added value of diffusion kurtosis imaging (DKI) with the combination of dynamic susceptibility contrast-enhanced (DSC) MRI in differentiating glioma recurrence from pseudoprogression.

METHODS

Thirty-four patients with high-grade gliomas developing new and/or increasing enhanced lesions within six months after surgery and chemoradiotherapy were retrospectively analyzed. All patients were pathologically confirmed to have recurrent glioma (n = 22) or pseudoprogression (n = 12). The DKI and DSC MRI parameters were calculated based on the enhanced lesions on contrast-enhanced T1WI. ROC analysis was performed on significant variables to determine their diagnostic performance. Multivariate logistic regression was used to determine the best prediction model for discrimination.

RESULTS

The relative mean kurtosis (rMK), relative axial kurtosis (rKa), relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) of glioma recurrence were higher than those of pseudoprogression (all, P < 0.05). The AUCs and diagnostic accuracy were 0.879 and 82.35% for rMK, 0.723 and 70.59% for rKa, 0.890 and 82.35% for rCBV, 0.765 and 73.53% for rMTT, respectively. A multivariate logistic regression model showed a significant contribution of rMK (P = 0.006) and rCBV (P = 0.009) as independent imaging classifiers for discrimination. The combined use of rMK and rCBV improved the AUC to 0.924 (P < 0.001) and the diagnostic accuracy to 88.24%.

CONCLUSION

DKI may be a valuable non-invasive tool in differentiating glioma recurrence from pseudoprogression, and its use in combination with DSC MRI can improve diagnostic performance in assessing treatment response compared with either technique alone.

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.

Magnetic Resonance Image of Neonatal Acute Bilirubin Encephalopathy: A Diffusion Kurtosis Imaging Study

Background and Objective: The abnormal T1-weighted imaging of MRI can be used to characterize neonatal acute bilirubin encephalopathy (ABE) in newborns, but has limited use in evaluating the severity and prognosis of ABE. This study aims to assess the value of diffusion kurtosis imaging (DKI) in detecting ABE and understanding its pathogenesis.

Method: Seventy-six newborns with hyperbilirubinemia were grouped into three groups (mild group, moderate group, and severe group) based on serum bilirubin levels. All the patients underwent conventional MRI and DKI serial, as well as 40 healthy full-term infants (control group). The regions of interest (ROIs) were the bilateral globus pallidus, dorsal thalamus, frontal lobe, auditory radiation, superior temporal gyrus, substantia nigra, hippocampus, putamen, and inferior olivary nucleus. The values of mean diffusivity (MD), axial kurtosis (AK), radial kurtosis (RK), and mean kurtosis (MK), and fractional anisotropy (FA), radial diffusivity (RD), and axis diffusivity (AD) of the ROIs were evaluated. All newborns were followed up and evaluated using the Denver Development Screening Test (DDST). According to the follow-up results, the patients were divided into the normal group, the suspicious abnormal group, and the abnormal group.

Result: Compared with the control group, significant differences were observed with the increased MK of dorsal thalamus, AD of globus pallidus in the moderate group, and increased RD, MK, AK, and RK value of globus pallidus, dorsal thalamus, auditory radiation, superior temporal gyrus, and hippocampus in the severe group. The peak value of total serum bilirubin was moderately correlated with the MK of globus pallidus, dorsal thalamus, and auditory radiation and was positively correlated with the other kurtosis value. Out of 76 patients, 40 finished the DDST, and only 9 patients showed an abnormality. Compared with the normal group, the AK value of inferior olivary nucleus showed significant differences (p < 0.05) in the suspicious abnormal group, and the MK of globus pallidus, temporal gyrus, and auditory radiation; RK of globus pallidus, dorsal thalamus, and auditory radiation; and MD of globus pallidus showed significant differences (p < 0.05) in the abnormal group.

Conclusion: DKI can reflect the subtle structural changes of neonatal ABE, and MK is a sensitive indicator to indicate the severity of brain damage.

White Matter Changes Following Chronic Restraint Stress and Neuromodulation: A Diffusion Magnetic Resonance Imaging Study in Young Male Rats

Background

Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique, is an effective treatment for depression. However, few studies have used diffusion magnetic resonance imaging to investigate the longitudinal effects of rTMS on the abnormal brain white matter (WM) described in depression.

Methods

In this study, we acquired diffusion magnetic resonance imaging from young adult male Sprague Dawley rats to investigate 1) the longitudinal effects of 10- and 1-Hz low-intensity rTMS (LI-rTMS) in healthy animals; 2) the effect of chronic restraint stress (CRS), an animal model of depression; and 3) the effect of 10 Hz LI-rTMS in CRS animals. Diffusion magnetic resonance imaging data were analyzed using tract-based spatial statistics and fixel-based analysis.

Results

Similar changes in diffusion and kurtosis fractional anisotropy were induced by 10- and 1-Hz stimulation in healthy animals, although changes induced by 10-Hz stimulation were detected earlier than those following 1-Hz stimulation. Additionally, 10-Hz stimulation increased axial and mean kurtosis within the external capsule, suggesting that the two protocols may act via different underlying mechanisms. Brain maturation–related changes in WM, such as increased corpus callosum, fimbria, and external and internal capsule fiber cross-section, were compromised in CRS animals compared with healthy control animals and were rescued by 10-Hz LI-rTMS. Immunohistochemistry revealed increased myelination within the corpus callosum in LI-rTMS–treated CRS animals compared with those that received sham or no stimulation.

Conclusions

Overall, decreased WM connectivity and integrity in the CRS model corroborate findings in patients experiencing depression with high anxiety, and the observed LI-rTMS–induced effects on WM structure suggest that LI-rTMS might rescue abnormal WM by increasing myelination.

Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging

SIMPLE SUMMARY

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM.

PURPOSE

Non-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival.

METHODS AND MATERIALS

Thirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS.

RESULTS

The large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).

CONCLUSION

The DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis

Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.

Diffusion Kurtosis Imaging Reflects GFAP, TopoIIα, and MGMT Expression in Astrocytomas

Objective

Preliminary study of magnetic resonance (MR) diffusion kurtosis imaging (DKI) assessing the pathological glial fibrillary acidic protein (GFAP), TopoIIα, and O 6-methylguanine-DNA methyltransferase (MGMT) expression in astrocytomas.

Materials and Methods

This study was approved by the local ethics committee, and informed consent was obtained from all participants. Sixty-six cases with pathologically proven astrocytomas were enrolled in this study; of which, 34 were high grade and remaining 32 were low grade. They patients underwent conventional MRI head scan, DKI scan, and enhanced scan under the same conditions. Fractional anisotropy (FA) and mean kurtosis (MK) calculated from DKI, as well as GFAP, TopoIIα, and MGMT expression level were compared prospectively between high and low-grade astrocytomas. Spearman rank correlation analysis was used for comparing values of DKI and GFAP, TopoIIα, and MGMT expression level in the two groups.

Results

The MK values were significantly higher in high-grade astrocytomas than those in low-grade astrocytomas (P < 0.05); FA values demonstrated no significant difference between the two groups (P = 0.331). GFAP expression level was significantly lower in high-grade astrocytomas than in low-grade astrocytomas (P < 0.05). Topo-IIα expression level were significantly higher in high-grade astrocytomas than in low-grade astrocytomas (P < 0.05). There was no significant difference in MGMT expression level between the two groups (P = 0.679). MK values were negatively correlated with the expression of GFAP (r = -0.836; P = 0.03), however, they were positively correlated with the expression of Topo-IIα (r = 0.896; P = 0.01). FA values were not correlated with the expression of GFAP (r = 0.366; P = 0.05), Topo-IIα (r = -0.562; P = 0.05), and MGMT (r = -0.153; P = 0.10).

Conclusion

MK, the DKI parameter values of astrocytomas, was significantly correlated to the expression of GFAP and TopoIIα. To a certain extent, applying DKI may provide the biological behavior of tumor cell differentiation, proliferation activity, invasion and metastasis, and can guide individual treatment.

MRI phantom for tissue simulation with respect to diffusion coefficient and kurtosis - Validation with injection of liposomal theranostics

This study presents gelatine-based and agar-based phantoms with an addition of glycerol, safflower oil, silicone oil and cellulose microcrystalline with a potential to cover the entire range of tissue diffusion coefficients and kurtosis values. Forty types of phantoms were prepared and examined for NMR relaxation times T₁ and T₂ and diffusional metrics D, K and ADC. Wide ranges of values of D (0.0003-0.0031 mm²s^-1), K (0.00-7.24) and ADC (0.0002-0.0031 mm²s^-1) were observed. Two of the phantoms closely mimic muscle and cortical gray matter with respect to water diffusion parameters. Although many of the presented phantoms display both D and K values within the range of human tissues, they match different tissues with respect to D and K. The imaging results for the gray matter simulating phantom injected with the liposomal solution, bear a resemblance to the particle size effect described in the literature. The phantoms presented in this work are simple in preparation and affordable tissue-simulating materials to be used primarily in development of diffusion kurtosis-based MRI methods and possibly in a preliminary assessment of MRI contrast agents. Further adjustments of the chemical compositions could potentially lead to development of new types of phantoms mimicking diffusional properties of more kinds of soft tissues.

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.

The association between White matter microstructure alterations detected by Diffusional kurtosis imaging in Neural circuit and post-stroke depression

AIM

In order to study the mechanism of post-stroke depression (PSD), we used diffusion kurtosis imaging (DKI) to describe the changes in white matter (WM) microstructure in PSD patients, to investigate the association between WM damage in limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit and PSD, and the utility of DKI in the diagnosis of PSD.

METHODS

Fifty-eight participants were divided into different groups: control group (n=20), stroke patients without depression (Non-PSD, n=21) and PSD group (n=17). All were taken DKI scans. The WM of bilateral superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, temporal lobe, parietal lobe, occipital lobe, the anterior and posterior limb of internal capsule, the genu and splenium of corpus callosum were selected as the regions of interest (ROI) and selected mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK) as the DKI parameters.

RESULTS

Compared with control and Non-PSD, MK of PSD group in bilateral superior frontal gyrus, middle frontal gyrus, temporal lobe and the genu of corpus callosum were decreased significantly, as well as the RK in left superior frontal gyrus, bilateral middle frontal gyrus and temporal lobe. But there was no significant difference in AK. Besides, the decrease in MK and RK in frontal and temporal lobe was negatively associated with the severity of PSD.

CONCLUSION

Our research indicated that the damage to WM microstructure in the frontal lobe, temporal lobe and the genu of corpus callosum may be related toPSD. DKI explores the microstructural changes of WM in PSD patients and may be an auxiliary diagnostic method for PSD.

Elucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI

To understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has typically used electron microscopy techniques, but this methodology cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; micro diffusion magnetic resonance imaging (micro-dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme optimized to image the locust brain. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and inferring connectivity of large tracts in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) can be extracted. These metrics could, in future, be used to quantify longitudinal structural changes in the nervous system of the locust brain that occur due to environmental stressors or ageing.

Heterogeneity of multiple sclerosis lesions in fast diffusional kurtosis imaging

Background

Mean kurtosis (MK), one of the parameters derived from diffusion kurtosis imaging (DKI), has shown increased sensitivity to tissue microstructure damage in several neurological disorders.

Methods

Thirty-seven patients with relapsing-remitting MS and eleven healthy controls (HC) received brain imaging on a 3T MR scanner, including a fast DKI sequence. MK and mean diffusivity (MD) were measured in the white matter of HC, normal-appearing white matter (NAWM) of MS patients, contrast-enhancing lesions (CE-L), FLAIR lesions (FLAIR-L) and black holes (BH).

Results

Overall 1529 lesions were analyzed, including 30 CE-L, 832 FLAIR-L and 667 BH. Highest MK values were obtained in the white matter of HC (0.814 ± 0.129), followed by NAWM (0.724 ± 0.137), CE-L (0.619 ± 0.096), FLAIR-L (0.565 ± 0.123) and BH (0.549 ± 0.12). Lowest MD values were obtained in the white matter of HC (0.747 ± 0.068 10⁻³mm²/sec), followed by NAWM (0.808 ± 0.163 10⁻³mm²/sec), CE-L (0.853 ± 0.211 10⁻³mm²/sec), BH (0.957 ± 0.304 10⁻³mm²/sec) and FLAIR-L (0.976 ± 0.35 10⁻³mm²/sec). While MK differed significantly between CE-L and non-enhancing lesions, MD did not.

Conclusion

MK adds predictive value to differentiate between MS lesions and might provide further information about diffuse white matter injury and lesion microstructure.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Structural and functional brain abnormalities in postherpetic neuralgia: A systematic review of neuroimaging studies

In recent decades, an increasing number of neuroimaging studies utilizing magnetic resonance imaging (MRI) have explored the differential effects of postherpetic neuralgia (PHN) on brain structure and function. We systematically reviewed and integrated the findings from relevant neuroimaging studies in PHN patients. A total of 15 studies with 16 datasets were ultimately included in the present study, which were categorized by the different neuroimaging modalities. The results revealed that PHN was closely associated with structural/microstructural and functional abnormalities of the brain mainly located in the 'pain matrix', including the thalamus, insula, parahippocampus, amygdala, dorsolateral prefrontal cortex, precentral gyrus and inferior parietal lobe, as well as other regions, such as the precuneus, lentiform nucleus and brainstem. Furthermore, a disruption of multiple networks, including the default-mode network, salience network and limbic system, may contribute to the neurophysiological mechanisms underlying PHN. The findings indicate that the cerebral abnormalities of PHN were not restricted to the pain matrix but extended to other regions, profoundly affecting the regulation and moderation of pain processing in PHN. Future prospective and longitudinal neuroimaging studies with larger samples will elucidate the progressive trajectory of neural changes in the pathophysiological process of PHN.

Diffusion kurtosis imaging of gray matter in young adults with autism spectrum disorder

Prior ex vivo histological postmortem studies of autism spectrum disorder (ASD) have shown gray matter microstructural abnormalities, however, in vivo examination of gray matter microstructure in ASD has remained scarce due to the relative lack of non-invasive methods to assess it. The aim of this work was to evaluate the feasibility of employing diffusional kurtosis imaging (DKI) to describe gray matter abnormalities in ASD in vivo. DKI data were examined for 16 male participants with a diagnosis of ASD and IQ>80 and 17 age- and IQ-matched male typically developing (TD) young adults 18-25 years old. Mean (MK), axial (AK), radial (RK) kurtosis and mean diffusivity (MD) metrics were calculated for lobar and sub-lobar regions of interest. Significantly decreased MK, RK, and MD were found in ASD compared to TD participants in the frontal and temporal lobes and several sub-lobar regions previously associated with ASD pathology. In ASD participants, decreased kurtosis in gray matter ROIs correlated with increased repetitive and restricted behaviors and poor social interaction symptoms. Decreased kurtosis in ASD may reflect a pathology associated with a less restrictive microstructural environment such as decreased neuronal density and size, atypically sized cortical columns, or limited dendritic arborizations.

Diagnostic performance between MR amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction at 3 T

PURPOSE

Accurate glioma grading and IDH mutation status prediction are critically essential for individualized preoperative treatment decisions. This study aims to compare the diagnostic performance of magnetic resonance (MR) amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction.

METHOD

Fifty-one glioma patients without treatment were retrospectively included. APT-weighted (APTw) effect and DKI indices, including mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK), and kurtosis FA (KFA) were obtained from APT and diffusion-weighted images, respectively. DKI indices in tumors were normalized to that in contralateral normal appearing white matter (CNAWM) and the APTw difference (ΔAPTw) between the two regions was calculated. Student's t-test, one-way ANOVA and ROC analyses were conducted.

RESULTS

Among the enrolled 51 patients, 13 had glioma-II, 17 had glioma-III and 21 had glioma-IV. 25 patients were diagnosed as IDH-mutant, and 26 as IDH-wild type. MD and MK differed significantly between glioma-IV and glioma II/III (P < 0.05), but not between glioma-II and glioma-III. FA and KFA showed no significant difference among the three groups (P > 0.05). IDH-mutant group exhibited significantly higher MD and lower FA, MK and ΔAPTw than IDH-wild type (P < 0.05), whereas the two groups showed comparable KFA values. In contrast, ΔAPTw differed significantly across tumor grades and IDH mutation status (P < 0.05), with consistently better discriminatory performance than DKI indices in glioma grading and IDH mutation status prediction.

CONCLUSIONS

APT imaging was superior to DKI in glioma grading and IDH mutation status prediction, benefiting accurate diagnoses and treatment decisions.

Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging

PURPOSE

To compare the diagnostic value of DKI and DTI in differentiation of high-grade glioma recurrence and pseudoprogression (PsP).

METHOD

Forty patients with high-grade gliomas who exhibited new enhancing lesions (24 high-grade glioma recurrence and 16 PsP) within 6 months after surgery followed by completion of chemoradiation therapy. All patients underwent repeat surgery or biopsy after routine MRI and DKI (including DTI). They were histologically classified into high-grade glioma recurrence and PsP groups. DKI (mean kurtosis [MK], axial kurtosis [Ka], and radial kurtosis [Kr]) and DTI (mean diffusivity [MD] and fractional anisotropy [FA]) parameters in the enhancing lesions and in the perilesional edema were measured. Inter-group differences between high-grade glioma recurrence and PsP were compared using the Mann-Whitney U test The receiver operating characteristic (ROC) curve was used to assess differential diagnostic efficacy of each parameter, and Z-scores were used to compare the value between DKI and DTI.

RESULTS

Relative MK (rMK) was significantly higher and relative MD (rMD) was significantly lower in the enhancing lesions of high-grade glioma recurrence compared to PsP (P <  0.001, P = 0.006, respectively). The AUC was 0.914 for rMK and 0.760 for rMD, and this difference was significant (P = 0.030). In the perilesional edema, rMK values were significantly higher and rMD values were significantly lower in high-grade glioma recurrence compared to PsP (P <  0.001, P =  0.005).

CONCLUSIONS

DKI had superior performance in differentiating high-grade glioma recurrence from PsP, and rMK appeared to be the best independent predictor.

Diffusional kurtosis imaging in evaluation of microstructural changes of spinal cord in cervical spondylotic myelopathy feasibility study

To explore the value of diffusion kurtosis imaging in the changes of spinal cord microstructures in patients with early cervical spondylotic myelopathy.Twenty nine patients with cervical myelopathy were selected in this study. All images were acquired on a 3.0 T MR scanner (Skyra, Siemens Medical Systems, Germany). The imaging parameters for diffusion kurtosis imaging were as follows: repetition time/echo time, 3000/91 ms; averages, 2; slice thickness/gap, 3/0.3 mm; number of slices, 17; field of view, 230 × 230 mm; Voxel size, 0.4 × 0.4 × 3.0 mm; 3 b-values (0, 1000, and 2000 s/mm) with diffusion encoding in 20 directions for each b-value. Values for fractional anisotropy, mean diffusivity, and mean diffusional kurtosis (MK) were calculated and compared between unaffected and affected spinal cords.In all patients MK was significantly lower in normal appearing spinal cords adjacent to the affected cervical spinal cords than in normal cervical spinal cords (0.862 ± 0.051 vs 0.976 ± 0.0924, P < .0001), but the difference of fractional anisotropy and apparent diffusion coefficient was no significant (P > .05). The affected cervical spinal cords had lower MK (0.716 ± 0.0753), FA and higher apparent diffusion coefficient than normal cervical spinal cords (P < .001).MK values in the cervical spinal cord may reflect microstructural changes of spinal cord damage in cervical myelopathy, and it could potentially provide more information that obtained with conventional diffusion metrics.

Advanced non-invasive MRI of neuroplasticity in ischemic stroke: Techniques and applications

Ischemic stroke represents a serious medical condition which could cause survivors suffer from long-term and even lifetime disabilities. After a stroke attack, the brain would undergo varying degrees of recovery, in which the central nervous system could be reorganized spontaneously or with the help of appropriate rehabilitation. Magnetic resonance imaging (MRI) is a non-invasive technique which can provide comprehensive information on structural, functional and metabolic features of brain tissue. In the last decade, there has been an increased technical advancement in MR techniques such as voxel-based morphological analysis (VBM), diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI), arterial spin-labeled perfusion imaging (ASL), magnetic sensitivity weighted imaging (SWI), quantitative sensitivity magnetization (QSM) and magnetic resonance spectroscopy (MRS) which have been proven to be a valuable tool to study the brain tissue reorganization. Due to MRI indices of neuroplasticity related to neurological outcome could be translated to the clinic. The ultimate goal of this review is to equip readers with a fundamental understanding of advanced MR techniques and their corresponding clinical application for improving the ability to predict neuroplasticity that are most suitable for stroke management.

Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture

PURPOSE

In diffusion MRI (dMRI), it remains unclear to know how much increase of b-value is conveying additional biological meaning. We tested the correlations between cortical microarchitecture and diffusion metrics computed from standard (1000 s/mm²), high (3000 s/mm²), to very high (5000 s/mm²) b-value dMRI.

METHODS

Healthy volunteers were scanned with a dMRI pulse sequence that was first optimized together with a T1-WI and T2-WI. Averaged cortical surface map of estimated myelin (T1-WI/T2-WI) was compared with surface maps of mean diffusivity (MD) computed from each b-value (MD₁₀₀₀, MD₃₀₀₀, and MD₅₀₀₀) and to surface map of mean kurtosis (MK computed from the 0-, 1000-, to 3000-s/mm² shells) in 360 cortical parcels using Spearman correlations, multiple linear regressions, and Akaike information criteria (AIC).

RESULTS

Surface map from MD₁₀₀₀ showed variations not related to myelin but the MD₃₀₀₀ and MD₅₀₀₀ maps inversely mirrored estimated myelin map; lower MD values being observed in more myelinated cortical areas. MK mirrored myelinated cortical areas. Quantitatively, Spearman correlations between myelin and MD became more and more negative as long as b-values increased while the correlation was positive between myelin and MK. Multiple regression models confirmed negative associations between myelin and MD that were significantly better from MD₁₀₀₀ to MD₃₀₀₀ and MD₅₀₀₀ (R² = 0.33, p < 0.001; R² = 0.43, p < 0.001; and R² = 0.50, p < 0.001) and positive association between myelin and MK (R² = 0.53, p < 0.001). Comparisons of the 3 statistical models showed the best performances with MK and MD₅₀₀₀ (AIC_MK < AIC_MD5000 < AIC_MD3000 < AIC_MD1000).

CONCLUSION

Higher b-values are more closely related to subtle cellular variations of the cortical microarchitecture.

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

BACKGROUND

Diffusion kurtosis imaging (DKI) quantifies the non-Gaussian diffusion of water within tissue microstructure. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes.

PURPOSE

To assess the reproducibility in whole-brain high-resolution DKI at varying b-values.

STUDY TYPE

Retrospective.

SUBJECTS AND PHANTOMS

In all, 44 individuals from the test-retest Human Connectome Project (HCP) database and 12 3D-printed phantoms.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted multiband echo-planar imaging sequence at 3T and 9.4T. magnetization-prepared rapid acquisition gradient echo at 3T for in vivo structural data only.

ASSESSMENT

From HCP data with b-values = 1000, 2000, 3000 s/mm² (dataset A), two additional datasets with b-values = 1000, 3000 s/mm² (dataset B) and b-values = 1000, 2000 s/mm² (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in white matter (WM) and gray matter (GM) based on whole-brain and regions of interest (ROIs).

STATISTICAL TESTS

DKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson's correlation to investigate the presence of systematic biases. Repeated measures analysis of variance was used for statistical comparison.

RESULTS

Datasets A and B exhibited lower DKI CoVs (<20%) compared to C (<50%) in both WM and GM ROIs (all P < 0.05). This effect varies between DKI and DTI parameters (P < 0.005). Whole-brain fitting residuals were consistent across datasets (P > 0.05), but lower residuals in dataset B were detected for the WM ROIs (P < 0.001). A similar trend was observed for the phantom data CoVs (<7.5%) at varying fiber orientations for datasets A and B. Finally, dataset C was characterized by higher residuals across the different fiber crossings (P < 0.05).

DATA CONCLUSION

The study demonstrates that high reproducibility can still be achieved within a reasonable scan time, specifically dataset B, supporting the potential of DKI for aiding clinical tools in detecting microstructural changes.

Diffusion Kurtosis Imaging Reveals Optic Tract Damage That Correlates with Clinical Severity in Glaucoma

Glaucoma is a neurodegenerative disease of the visual system and is the leading cause of irreversible blindness worldwide. To date, its pathophysiological mechanisms remain unclear. This study evaluated the feasibility of advanced diffusion magnetic resonance imaging techniques for examining the microstructural environment of the visual pathway in glaucoma. While conventional diffusion tensor imaging (DTI) showed lower fractional anisotropy and higher directional diffusivities in the optic tracts of glaucoma patients than healthy controls, diffusion kurtosis imaging (DKI) and the extended white matter tract integrity (WMTI) model indicated lower radial kurtosis, higher axial and radial diffusivities in the extra-axonal space, lower axonal water fraction, and lower tortuosity in the same regions in glaucoma patients. These findings suggest glial involvements apart from compromised axonal integrity in glaucoma. In addition, DKI and WMTI but not DTI parameters significantly correlated with clinical ophthalmic measures via optical coherence tomography and visual field perimetry testing. Taken together, DKI and WMTI provided sensitive and comprehensive imaging biomarkers for quantifying glaucomatous damage in the white matter tract across clinical severity complementary to DTI.

Neuroanatomical underpinning of diffusion kurtosis measurements in the cerebral cortex of healthy macaque brains

PURPOSE

To investigate the neuroanatomical underpinning of healthy macaque brain cortical microstructure measured by diffusion kurtosis imaging (DKI), which characterizes non-Gaussian water diffusion.

METHODS

High-resolution DKI was acquired from 6 postmortem macaque brains. Neurofilament density (ND) was quantified based on structure tensor from neurofilament histological images of a different macaque brain sample. After alignment of DKI-derived mean kurtosis (MK) maps to the histological images, MK and histology-based ND were measured at corresponding regions of interests characterized by distinguished cortical MK values in the prefrontal/precentral-postcentral and temporal cortices. Pearson correlation was performed to test significant correlation between these cortical MK and ND measurements.

RESULTS

Heterogeneity of cortical MK across different cortical regions was revealed, with significantly and consistently higher MK measurements in the prefrontal/precentral-postcentral cortex compared to those in the temporal cortex across all six scanned macaque brains. Corresponding higher ND measurements in the prefrontal/precentral-postcentral cortex than in the temporal cortex were also found. The heterogeneity of cortical MK is associated with heterogeneity of histology-based ND measurements, with significant correlation between cortical MK and corresponding ND measurements (P < .005).

CONCLUSION

These findings suggested that DKI-derived MK can potentially be an effective noninvasive biomarker quantifying underlying neuroanatomical complexity inside the cerebral cortical mantle for clinical and neuroscientific research.

Cerebral diffusion kurtosis imaging to assess the pathophysiology of postpartum depression

Postpartum depression (PPD), a main cause of maternal suicide, is an important issue in perinatal mental health. Recently, cerebral diffusion tensor imaging (DTI) studies have shown reduced fractional anisotropy (FA) in major depressive disorder (MDD) patients. There are, however, no reports using diffusion kurtosis imaging (DKI) for evaluation of PPD. This was a Japanese single-institutional prospective study from 2016 to 2019 to examine the pathophysiological changes in the brain of PPD patients using DKI. The DKI data from 3.0 T MRI of patients one month after delivery were analyzed; the patients were examined for PPD by a psychiatrist. The mean kurtosis (MK), FA and mean diffusivity (MD) were calculated from the DKI data and compared between PPD and non-PPD groups using tract-based spatial statistics analysis. Of the 75 patients analyzed, eight patients (10.7%) were diagnosed as having PPD. In the PPD group, FA values in the white matter and thalamus were significantly lower and MD values in the white matter and putamen were significantly higher. The area with significant differences in MD value was more extensive (40.8%) than the area with significant differences in FA value (6.5%). These findings may reflect pathophysiological differences of PPD compared with MDD.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability

OBJECTIVES

Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data.

METHODS

Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed.

RESULTS

The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81).

CONCLUSIONS

Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

KEY POINTS

• The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

Changes in the Corticospinal Tract Beyond the Ischemic Lesion Following Acute Hemispheric Stroke: A Diffusion Kurtosis Imaging Study

BACKGROUND

The degeneration of the corticospinal tract (CST) in chronic stroke has been widely described using diffusion tensor imaging and correlates with the extent of motor deficits. However, only a few studies have reported the early degeneration in the distal CST during the acute stage of stroke and pathological changes in the distal CST have not been described.

PURPOSE

To study the microstructural changes along the CST beyond the ischemic lesion in acute stroke using diffusion kurtosis imaging (DKI).

STUDY TYPE

Prospective.

POPULATION

In all, 48 patients (26 males, 22 females; mean age 58.27 ± 12.89 years) with acute ischemic stroke.

SEQUENCE

A DKI sequence with three b-values (0, 1000, and 2000 s/mm2 ) at 3.0T MRI.

ASSESSMENT

The kurtosis and tensor parameters were derived from DKI and were compared along the length of the CST beyond the ischemic lesion between the affected and unaffected hemispheres using both voxelwise and slicewise analysis. The degree of neurological deficits was evaluated using the National Institute of Health Stroke Score (NIHSS) and the Barthel index and the clinical outcome at 3 months was evaluated using a modified Rankin scale.

STATISTICAL TESTS

Paired t-tests, a linear mixed model, and multivariate linear regression.

RESULTS

Voxelwise analysis demonstrated increased mean kurtosis, increased axial kurtosis, and decreased axial diffusivity in the affected CST, which were seen only at the level of the cerebral peduncle (all corrected P < 0.05). Slicewise analysis also demonstrated increased axial kurtosis in the cerebral peduncle of the affected CST (corrected P < 0.05). The axial kurtosis from slicewise analysis independently correlated with the motor component of NIHSS (β = 0.297, P = 0.040).

DATA CONCLUSION

Our findings suggest that early anterograde degeneration occurs along the axon direction in the distal CST in acute stroke, and can be detected using DKI. Moreover, acute axonal degeneration along the CST correlated with motor deficits.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:512-519.

Comparison of diffusion-weighted imaging mono-exponential mode with diffusion kurtosis imaging for predicting pathological grades of clear cell renal cell carcinoma

PURPOSE

To evaluate the role of diffusion kurtosis imaging (DKI¹) in the characterization of clear cell renal cell carcinoma (ccRCC²) compared with standard diffusion-weighted imaging (DWI³).

METHODS

89 patients with histologically proven ccRCC were evaluated by DKI and DWI on a 3-T scanner. All ccRCCs were classified as grade 1-4 according to the Fuhrman classification system. The apparent diffusion coefficient (ADC⁴), fractional anisotropy (FA⁵), mean diffusivity (MD⁶), mean kurtosis (MK⁷), axial kurtosis (Ka⁸) and radial kurtosis (Kr⁹) values were recorded. The differences in DWI and DKI parameters were evaluated by independent-sample t test and a receiver operating characteristic (ROC¹⁰) analysis was performed. The DeLong test was performed to compare the ROCs.

RESULTS

Compared to normal renal parenchyma, ADC and MD values of ccRCC decreased and MK, Ka, and Kr values increased (p < 0.05). ADC and MD values of ccRCC decreased with the increase in pathological grade, while MK, Ka, and Kr values were increased (p < 0.05). ADC could discriminate G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05) except for G1 vs G2 (p > 0.05). Ka and Kr could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G4, and G3 vs G4 (p < 0.05) except for G2 vs G3 (p > 0.05). MD and MK could discriminate G1 vs G2, G1 vs G3, G1 vs G4, G2 vs G3, G2 vs G4, and G3 vs G4 (p < 0.05). The AUC of MK was the highest. The DeLong test showed that there were significant differences regarding ROCs between ADC/MK, ADC/Ka, ADC/Kr in grading G1/G2, and ADC/MK, MK/Ka in grading G3/G4 (p < 0.05).

CONCLUSION

DKI was superior compared to the mono-exponential mode of DWI in grading ccRCC.

Comparison of intravoxel incoherent motion imaging, diffusion kurtosis imaging, and conventional DWI in predicting the chemotherapeutic response of colorectal liver metastases

PURPOSE

To assess the usefulness and performance of intravoxel incoherent motion imaging (IVIM) with diffusion kurtosis imaging (DKI) and conventional DWI for predicting the chemotherapeutic response of colorectal liver metastases (CRLMs).

METHOD

A prospective study was conducted. Up to February 2018, forty consecutive patients treated with the standard first-line chemotherapy regimens were enrolled. MRI was performed within 1 week before chemotherapy, as well as 2-3 weeks and 6-8 weeks after chemotherapy. The apparent diffusion coefficient map, IVIM and DKI parameter maps were calculated using a prototype postprocessing software. The response was assessed by the Response Evaluation Criteria in Solid Tumors. The parameters were compared between the responding group (complete and partial response) and the nonresponding group (stable and progressive disease).

RESULTS

A total of 15 responding and 25 nonresponding patients were evaluated. Low baseline ADC, D_slow, and D values (P =  0.001, <0.001, and =0.003, respectively) and a high baseline K value (P =  0.002) were independently associated with a good response to chemotherapy. The combination of all the significant parameters yielded an AUC of 0.867. After treatment, the ADC, D_slow, and D values all showed an upward trend, while the K value showed a decreasing trend, but there were no significant differences (P >  0.05).

CONCLUSION

The study showed that the pretreatment IVIM (D_slow), DKI (D and K), and conventional DWI (ADC) parameters all demonstrated a good diagnostic performance in predicting the chemotherapeutic response of CRLMs.

Removing rician bias in diffusional kurtosis of the prostate using real-data reconstruction

PURPOSE

To compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase-corrected real data with those generated using magnitude data with and without noise compensation (NC).

METHODS

Diffusion-weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b-values (0-1500 s/mm2 ), each acquired with 6 signal averages along 3 diffusion directions, with noise-only images acquired to allow NC. In addition to conventional magnitude averaging, phase-corrected real data were averaged in an attempt to reduce rician noise-bias, with a range of phase-correction low-pass filter (LPF) sizes (8-128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase-corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ).

RESULTS

Simulations indicated LPF size can strongly affect K metrics, where 64-pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64-LPF real-data K were lower (P < 0.0001) by 4/10/7%, respectively.

CONCLUSION

Compared with magnitude data with NC, phase-corrected real data can produce similar K, although the choice of phase-correction LPF should be chosen carefully.

Diffusion kurtosis imaging in liver: a preliminary reproducibility study in healthy volunteers

OBJECTIVES

To systematically test the reproducibility of DKI technique in normal liver and report a complete set of DKI measurement data.

MATERIALS AND METHODS

Thirty-two healthy volunteers were examined with liver DKI twice on the GE 3.0 T MRI scanner and reviewed by three professional experts. DKI-derived parameters fractional anisotropy of kurtosis (FAk), mean diffusivity (Md), axial diffusivity (Da), radial diffusivity (Dr), mean kurtosis (Mk), axial kurtosis (Ka), and radial kurtosis (Kr) in eight segments divided by Couinaud octagonal method were collected. Inter-class correlation coefficient (ICC) was used to assess the agreement between three experts. For each expert, the reproducibility of twice scans was evaluated by Bland-Altman method. Multivariate analysis of variance was to explore the regional distribution characteristics of DKI-derived parameters, and showed with box-plot graph.

RESULTS

Using ICC analysis, except for FAk (ICC 0.312, 0.307), other DKI metric values showed high reproducibility (0.716 < ICC < 0.907) between three experts for each of two DKI measurements. With Bland-Altman method, liver segment 5 (S5) showed the best reproducibility between two DKI measurement, and the reproducibility of segment 4 (S4) was the worst. The reproducibility of the right lobe was significantly higher than the left lobe. The values of diffusion metrics (Md, Da, and Dr) and kurtosis metrics (Mk, Ka, and Kr) existed significantly difference between the right and left hepatic lobes.

CONCLUSION

DKI has shown excellent reproducibility in liver imaging. The range of values for multiple DKI parameters, derived from the normal liver, was reported, and may provide data reference for further clinical DKI applications. Additionally, DKI technique is a non-invasive method to reflect the perfusion or structural differences between the left and right hepatic lobes from the molecular level.

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences-motifs that are visually similar across different acquisitions-to infer changes on the parameters of pretrained models, and enable them to operate on new data. In particular, we examine the annotations of an existing acquisition to determine pivot locations that characterize the reference segmentation, and use a patch matching algorithm to find their candidate visual correspondences in a new volume. We aggregate all the candidate correspondences by a voting scheme and we use them to construct a consensus heatmap: a map of how frequently locations on the new volume are matched to relevant locations from the original acquisition. This information allows us to perform model adaptations in two different ways: either by a) optimizing model parameters under a Multiple Instance Learning formulation, so that predictions between reference locations and their sets of correspondences agree, or by b) using high-scoring regions of the heatmap as soft labels to be incorporated in other domain adaptation pipelines, including deep learning ones. We show that these unsupervised techniques allow us to obtain high-quality segmentations on unannotated volumes, qualitatively consistent with results obtained under full supervision, for both mitochondria and synapses, with no need for new annotation effort.

In Vivo Imaging of Gray Matter Microstructure in Major Psychiatric Disorders: Opportunities for Clinical Translation

Postmortem studies reveal that individuals with major neuropsychiatric disorders such as schizophrenia and autism spectrum disorder have gray matter microstructural abnormalities. These include abnormalities in neuropil organization, expression of proteins supporting neuritic and synaptic integrity, and myelination. Genetic and postmortem studies suggest that these changes may be causally linked to the pathogenesis of these disorders. Advances in diffusion-weighted magnetic resonance image (dMRI) acquisition techniques and biophysical modeling allow for the quantification of gray matter microstructure in vivo. While several biophysical models for imaging microstructural properties are available, one in particular, neurite orientation dispersion and density imaging (NODDI), holds great promise for clinical applications. NODDI can be applied to both gray and white matter and requires only a single extra shell beyond a standard dMRI acquisition. Since its development only a few years ago, the NODDI algorithm has been used to characterize gray matter microstructure in schizophrenia, Alzheimer's disease, healthy aging, and development. These investigations have shown that microstructural findings in vivo, using NODDI, align with postmortem findings. Not only do NODDI and other advanced dMRI-based modeling methods provide a window into the brain previously only available postmortem, but they may be more sensitive to certain brain changes than conventional magnetic resonance imaging approaches. This opens up exciting new possibilities for clinicians to more rapidly detect disease signatures and allows earlier intervention in the course of the disease. Given that neurites and gray matter microstructure have the capacity to rapidly remodel, these novel dMRI-based methods represent an opportunity to noninvasively monitor neuroplastic changes posttherapy within much shorter time scales.

Differentiate aquaporin-4 antibody negative neuromyelitis optica spectrum disorders from multiple sclerosis by multimodal advanced MRI techniques

BACKGROUND

It is clinically essential to distinguish aquaporin-4 antibody (AQP4-Ab) negative neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) because of different therapeutic strategies. Since clinical and lesion features may not allow the distinction, we aimed to identify advanced imaging features that could improve the distinction between two disorders.

METHODS

Multimodal imaging measures included fractional anisotropy, mean, axial, radial diffusivity (MD, AD, RD) and kurtosis (MK, AK, RK) from diffusion kurtosis imaging; functional connectivity strength (FCS) and density, regional homogeneity, amplitude of low frequency fluctuations from resting-state functional MRI; gray matter volume from structural MRI; and cerebral blood flow from arterial spin labeling imaging. Voxel-wise comparisons were performed to identify inter-group differences in imaging measures, and the performance of differentiating these two disorders was estimated by receiver operating characteristic curves.

RESULTS

Compared to MS, patients with AQP4-Ab negative NMOSD showed decreased MD and AD but increased MK and AK in white matter regions; and reduced FCS in the occipital cortex (P < 0.05, FWE corrected). The joint-use of these five imaging measures distinguished the two disorders with an accuracy of 94% (P < 0.001, 95%CI = 0.84-0.98). Other imaging measures showed no significant differences between the two patient groups.

CONCLUSIONS

The study showed less white matter damage and a more severe functional disconnection of the occipital cortex in patients with AQP4-Ab negative NMOSD compared to MS. The combined use of diffusion and functional connectivity could facilitate a better distinction between NMO and MS with seronegative AQP4-Ab in clinical management.

Editorial for "Evaluating the Therapeutic Effect of Low-Intensity Transcranial Ultrasound on Traumatic Brain Injury With Diffusion Kurtosis Imaging"

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 4 J. Magn. Reson. Imaging 2020;52:532-533.

Diffusion-time dependence of diffusional kurtosis in the mouse brain

PURPOSE

To investigate diffusion-time dependency of diffusional kurtosis in the mouse brain using pulsed-gradient spin-echo (PGSE) and oscillating-gradient spin-echo (OGSE) sequences.

METHODS

3D PGSE and OGSE kurtosis tensor data were acquired from ex vivo brains of adult, cuprizone-treated, and age-matched control mice with diffusion-time (t_D ) ~ 20 ms and frequency (f) = 70 Hz, respectively. Further, 2D acquisitions were performed at multiple times/frequencies ranging from f = 140 Hz to t_D = 30 ms with b-values up to 4000 s/mm² . Monte Carlo simulations were used to investigate the coupled effects of varying restriction size and permeability on time/frequency-dependence of kurtosis with both diffusion-encoding schemes. Simulations and experiments were further performed to investigate the effect of varying number of cycles in OGSE waveforms.

RESULTS

Kurtosis and diffusivity maps exhibited significant region-specific changes with diffusion time/frequency across both gray and white matter areas. PGSE- and OGSE-based kurtosis maps showed reversed contrast between gray matter regions in the cerebellar and cerebral cortex. Localized time/frequency-dependent changes in kurtosis tensor metrics were found in the splenium of the corpus callosum in cuprizone-treated mouse brains, corresponding to regional demyelination seen with histological assessment. Monte Carlo simulations showed that kurtosis estimates with pulsed- and oscillating-gradient waveforms differ in their sensitivity to exchange. Both simulations and experiments showed dependence of kurtosis on number of cycles in OGSE waveforms for non-zero permeability.

CONCLUSION

The results show significant time/frequency-dependency of diffusional kurtosis in the mouse brain, which can provide sensitivity to probe intrinsic cellular heterogeneity and pathological alterations in gray and white matter.

Evaluating the Therapeutic Effect of Low-Intensity Transcranial Ultrasound on Traumatic Brain Injury With Diffusion Kurtosis Imaging

BACKGROUND

Low-intensity transcranial ultrasound (LITUS) has a therapeutic effect on traumatic brain injury (TBI). Diffusion kurtosis imaging (DKI) might be able to evaluate the effect changes of injured brain microstructure.

PURPOSE

To evaluate the therapeutic effect of LITUS in a moderate TBI rat model with DKI parameters.

STUDY TYPE

Prospective case-control animal study.

ANIMAL MODEL

Forty-five rats were randomly divided into sham control, TBI, and LITUS treatment groups (n = 15).

FIELD STRENGTH/SEQUENCE

Single-shot spin echo echo-planar imaging and fast T₂ WI sequences at 3.0T.

ASSESSMENT

DKI parameters were obtained on days 1, 7, 14, 21, 28, 35, and 42 after TBI.

STATISTICAL TESTS

For the mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr) values, groups were compared using a two-way analysis of variance (ANOVA).

RESULTS

LITUS inhibited TBI and caused MK values to increase significantly during the early stage (LITUS vs. TBI, day 7, adjusted P < 0.0001) and decrease during the late stage (LITUS vs. TBI, day 42, adjusted P = 0.0156) in the damaged cortex. In the thalamus, the MK value of the TBI group began to rise on day 7, with no change observed in the LITUS group. TBI increases Ka value during the early stage in the cortex and decreases during the late stage in the cortex and thalamus. LITUS inhibited these Ka changes (LITUS vs. TBI, day 7, adjusted P = 0.0014; LITUS vs. TBI, day 42, adjusted P = 0.0026 and 0.0478, respectively, for cortex and thalamus). The Kr value increased slightly during the early stage in the cortex (TBI vs. Sham, day 1, adjusted P = 0.0016).

DATA CONCLUSION

The DKI parameter, particularly the MK value, evaluates primary cortical injury as well as the secondary brain injury that could not be detected by conventional T₂ WI.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;52:520-531.

Correlations Between the Microstructural Changes of the Medial Temporal Cortex and Mild Cognitive Impairment in Patients With Cerebral Small Vascular Disease (cSVD): A Diffusion Kurtosis Imaging Study

Object: The purpose of our study was to investigate the microstructural changes of the medial temporal cortex in mild cognitive impairment (MCI) patients with cerebral small vascular disease (cSVD) using diffusion kurtosis imaging (DKI) and to examine whether DKI parameters are correlated with MCI. Method: A total of 82 cSVD patients admitted to the Department of Neurology Beijing Chaoyang Hospital, Capital Medical University, were retrospectively enrolled in this study. The Montreal cognitive assessment scale (MoCA) score was used to assess overall cognitive function. According to the presence or absence of MCI, these patients were divided into an MCI group (n = 48) and a non-MCI group (n = 34). The general clinical data of the two groups were collected and analyzed. The regions of interest (ROIs) in the medial temporal cortex were selected for investigation. The averaged values of DKI parameters were measured in each ROI and compared between the two groups, and the correlations between DKI parameters and MoCA score and between diffusion and kurtosis parameters were examined. Results: Compared to the non-MCI group, MCI patients showed significantly increased mean diffusion (MD) and radial diffusion (RD) and significantly decreased mean kurtosis (MK) in the left hippocampus (P = 0.005, 0.006, 0.002, respectively). In the left hippocampus, fractional anisotropy (FA), MK, radial kurtosis (RK), and kurtosis fractional anisotropy (KFA) showed significantly positive correlations with MoCA score (r = 0.374, 0.37, 0.392, 0.242, respectively, all P < 0.05), while MK and RD were negatively correlated with MoCA score (r = -0.227, -0.255, respectively, both P < 0.05). In the left parahippocampal region, axial kurtosis (AK) and KFA were positively correlated with MoCA score (r = 0.228, 0.282, respectively, both P < 0.05), while RK was positively correlated with MoCA score in the right parahippocampal region (r = 0.231, P < 0.05). Significant correlations of MD with MK, RD with RK, and FA with KFA were observed in the medial temporal cortex (r = -0.254, -0.395, 0.807, respectively, all P < 0.05) but not of axial diffusion (AD) with AK. Conclusion: The DKI technique can be used to observe microstructural changes of the medial temporal cortex in MCI patients with cSVD. The DKI-derived parameters might be a feasible means of evaluating patients with MCI.

Probabilistic Assessment of Nerve Regeneration with Diffusion MRI in Rat Models of Peripheral Nerve Trauma

Nerve regeneration after injury must occur in a timely fashion to restore function. Unfortunately, current methods (e.g., electrophysiology) provide limited information following trauma, resulting in delayed management and suboptimal outcomes. Herein, we evaluated the ability of diffusion MRI to monitor nerve regeneration after injury/repair. Sprague-Dawley rats were divided into three treatment groups (sham = 21, crush = 23, cut/repair = 19) and ex vivo diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) was performed 1-12 weeks post-surgery. Behavioral data showed a distinction between crush and cut/repair nerves at 4 weeks. This was consistent with DTI, which found that thresholds based on the ratio of radial and axial diffusivities (RD/AD = 0.40 ± 0.02) and fractional anisotropy (FA = 0.53 ± 0.01) differentiated crush from cut/repair injuries. By the 12th week, cut/repair nerves whose behavioral data indicated a partial recovery were below the RD/AD threshold (and above the FA threshold), while nerves that did not recover were on the opposite side of each threshold. Additional morphometric analysis indicated that DTI-derived normalized scalar indices report on axon density (RD/AD: r = -0.54, p < 1e-3; FA: r = 0.56, p < 1e-3). Interestingly, higher-order DKI analyses did not improve our ability classify recovery. These findings suggest that DTI may provide promising biomarkers for distinguishing successful/unsuccessful nerve repairs and potentially identify cases that require reoperation.

MR Biomarkers of Degenerative Brain Disorders Derived From Diffusion Imaging

The incidence of neurodegenerative diseases has shown an increasing trend. These conditions typically cause progressive functional disability. Identification of robust biomarkers of neurodegenerative diseases is a key imperative to facilitate early identification of the pathological features and to foster a better understanding of the pathogenetic mechanisms of individual diseases. Diffusion tensor imaging (DTI) is the most widely used diffusion MRI technique for assessment of neurodegenerative diseases. The DTI parameters are promising biomarkers for evaluation of microstructural changes; however, some limitations of DTI restrict its wider clinical use. New diffusion MRI techniques, such as diffusion kurtosis imaging (DKI), bi-tensor DTI, and neurite orientation density and dispersion imaging (NODDI) have been demonstrated to provide value addition to DTI for evaluation of neurodegenerative diseases. In this review article, we summarize the key technical aspects and provide an overview of the current state of knowledge regarding the role of DKI, bi-tensor DTI, and NODDI as biomarkers of microstructural changes in representative neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1620-1636.

Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis

Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and associated animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals with varied grades of disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T₂* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T₂* images were used to delineate focal demyelination/inflammation. Our results reveal a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. Relationships between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity were similar to what has been found in other hypomyelinating MS models, and in patients. However, the changes in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability.

Replicable brain signatures of emotional bias and memory based on diffusion kurtosis imaging of white matter tracts

Diffusion MRI (dMRI) is sensitive to anisotropic diffusion within bundles of nerve axons and can be used to make objective measurements of brain networks. Many brain disorders are now recognised as being caused by network dysfunction or are secondarily associated with changes in networks. There is therefore great potential in using dMRI measures that reflect network integrity as a future clinical tool to help manage these conditions. Here, we used dMRI to identify replicable, robust and objective markers that meaningfully reflect cognitive and emotional performance. Using diffusion kurtosis analysis and a battery of cognitive and emotional tests, we demonstrated strong relationships between white matter structure across networks of anatomically and functionally specific brain regions with both emotional bias and emotional memory performance in a large healthy cohort. When the connectivity of these regions was examined using diffusion tractography, the terminations of the identified tracts overlapped precisely with cortical loci relating to these domains, drawn from an independent spatial meta‐analysis of available functional neuroimaging literature. The association with emotional bias was then replicated using an independently acquired healthy cohort drawn from the Human Connectome Project. These results demonstrate that, even in healthy individuals, white matter dMRI structural features underpin important cognitive and emotional functions. Our robust cross‐correlation and replication supports the potential of structural brain biomarkers from diffusion kurtosis MRI to characterise early neurological changes and risk in individuals with a reduced threshold for cognitive dysfunction, with further testing required to demonstrate clinical utility.

[Diffusion kurtosis imaging in diffuse axonal injury]

Diffuse axonal injury (DAI) is one of the most severe traumatic brain injuries. The availability of neuroimaging biomarkers for monitoring expansion of traumatic brain injury in vivo is a topical issue.

PURPOSE

To evaluate novel neuroimaging biomarkers for monitoring brain injury using diffusion kurtosis imaging (DKI) in patients with severe diffuse axonal injury.

MATERIAL AND METHODS

DKI data of 12 patients with severe DAI (11 patients with a Glasgow Coma Scale (GCS) score of ≤ 8 and 1 patient with a GCS score of 9) and 8 healthy volunteers (control group) were compared. MRI examination was performed 5 to 19 days after injury; 7 of the 12 patients underwent repeated MRI examinations. We assessed the following parameters: mean, axial, and radial kurtosis (MK, AK, RK, respectively) and kurtosis anisotropy (KA) of the white and gray matter; fractional anisotropy (FA), axonal water fraction (AWF), axial and radial extra-axonal diffusion (AxEAD and RadEAD, respectively), and tortuosity (TORT) of the extra-axonal space) of the white matter. Regions of interest (ROIs) were set bilaterally in the centrum semiovale, genu and splenium of the corpus callosum, anterior and posterior limbs of the internal capsule, putamen, thalamus, midbrain, and pons.

RESULTS

A significant reduction in KA (p<0.05) in most of ROIs set on the white matter was revealed. AK was increased (p<0.05) not only in the white matter but also in the putamen and thalamus. A significant reduction in MK with time was observed when the first and second DKI data were compared. AWF was reduced in the centrum semiovale and peduncles. The TORT parameter was decreased (p<0.05) in the majority of ROIs in the white matter, with the most pronounced changes occurring in the genu and splenium of the corpus callosum.

CONCLUSION

DKI provides novel data about microstructural injury in DAI and improves our knowledge of brain trauma pathophysiology. DKI parameters should be considered as potential biomarkers of brain injury and potential predictors of the outcome.

Brain microstructural abnormalities in type 2 diabetes mellitus: A systematic review of diffusion tensor imaging studies

Type 2 diabetes mellitus (T2DM) is associated with deficits in the structure and function of the brain. Diffusion tensor imaging (DTI) is a highly sensitive method for characterizing cerebral tissue microstructure. Using PRISMA guidelines, we identified 29 studies which have demonstrated widespread brain microstructural impairment and topological network disorganization in patients with T2DM. Most consistently reported structures with microstructural abnormalities were frontal, temporal, and parietal lobes in the lobar cluster; corpus callosum, cingulum, uncinate fasciculus, corona radiata, and internal and external capsules in the white matter cluster; thalamus in the subcortical cluster; and cerebellum. Microstructural abnormalities were correlated with pathological derangements in the endocrine profile as well as deficits in cognitive performance in the domains of memory, information-processing speed, executive function, and attention. Altogether, the findings suggest that the detrimental effects of T2DM on cognitive functions might be due to microstructural disruptions in the central neural structures.

Anomalous water dynamics in brain: a combined diffusion magnetic resonance imaging and neutron scattering investigation

Water diffusion is an optimal tool for investigating the architecture of brain tissue on which modern medical diagnostic imaging techniques rely. However, intrinsic tissue heterogeneity causes systematic deviations from pure free-water diffusion behaviour. To date, numerous theoretical and empirical approaches have been proposed to explain the non-Gaussian profile of this process. The aim of this work is to shed light on the physics piloting water diffusion in brain tissue at the micrometre-to-atomic scale. Combined diffusion magnetic resonance imaging and first pioneering neutron scattering experiments on bovine brain tissue have been performed in order to probe diffusion distances up to macromolecular separation. The coexistence of free-like and confined water populations in brain tissue extracted from a bovine right hemisphere has been revealed at the micrometre and atomic scale. The results are relevant for improving the modelling of the physics driving intra- and extracellular water diffusion in brain, with evident benefit for the diffusion magnetic resonance imaging technique, nowadays widely used to diagnose, at the micrometre scale, brain diseases such as ischemia and tumours.

Diffusion kurtosis imaging as an imaging biomarker for predicting prognosis of the patients with high-grade gliomas

PURPOSE

To retrospectively explore the utilization of MR diffusion kurtosis imaging (DKI) in predicting prognosis of the patients with high-grade gliomas.

MATERIALS AND METHODS

Thirty-three consecutive patients with cerebral gliomas underwent pretreatment DKI and diffusion-weighted imaging examination on a 3.0-T MR scanner. Diffusion parameters, including conventional tensor parameters, kurtosis metrics (mean kurtosis [MK], radial kurtosis [AK], and axial kurtosis [RK]), and minimum apparent diffusion coefficient (minADC), were obtained and normalized to the contralateral normal-appearing white matter. Correlations among each diffusion parameter and overall survival were analyzed by a Spearman method. The diagnostic efficiency of each parameter in predicting survival for patients with high-grade gliomas was assessed by a receiver operating characteristic curve. The favorable prognostic imaging biomarkers were further analyzed by using a Kaplan-Meier method with log-rank test.

RESULTS

In 33 patients, 17 patients reached overall survival >15 months (long survival group), whereas 16 showed overall survival <15 months (short survival group). Negative correlations between kurtosis metrics (MK, AK, and RK) and overall survival were obtained by using Spearman analysis (r = -0.63, -0.57, and -0.61, respectively, all P < 0.01), whereas minADC was positively correlated with overall survival (r = 0.56, P < 0.01). The kurtosis parameters of the long survival group were significantly lower than that of the short survival group (P < 0.001), while the minADC of the long survival group was significantly higher than that of the short survival group (P = 0.002). Among these diffusion parameters, the optimal cut-off value of MK (0.688) provided the best combination of sensitivity (93.75%) and specificity (76.47%) for differentiation of patients with long survival from those with short survival. High kurtosis metrics and low minADC were significant predictors of poor outcome. (P < 0.05).

CONCLUSION

Both kurtosis metrics and minADC have the potential to predict survival for the patients with high-grade gliomas. The preoperative kurtosis parameters, especially MK, can be taken as a preoperative prognostic biomarker to predict prognosis in patients with high-grade gliomas.