MR diffusion kurtosis imaging for neural tissue characterization

White Matter Microstructural Abnormalities in Type 2 Diabetes Mellitus: A Diffusional Kurtosis Imaging Analysis

BACKGROUND AND PURPOSE

Increasing DTI studies have demonstrated that white matter microstructural abnormalities play an important role in type 2 diabetes mellitus-related cognitive impairment. In this study, the diffusional kurtosis imaging method was used to investigate WM microstructural alterations in patients with type 2 diabetes mellitus and to detect associations between diffusional kurtosis imaging metrics and clinical/cognitive measurements.

MATERIALS AND METHODS

Diffusional kurtosis imaging and cognitive assessments were performed on 58 patients with type 2 diabetes mellitus and 58 controls. Voxel-based intergroup comparisons of diffusional kurtosis imaging metrics were conducted, and ROI-based intergroup comparisons were further performed. Correlations between the diffusional kurtosis imaging metrics and cognitive/clinical measurements were assessed after controlling for age, sex, and education in both patients and controls.

RESULTS

Altered diffusion metrics were observed in the corpus callosum, the bilateral frontal WM, the right superior temporal WM, the left external capsule, and the pons in patients with type 2 diabetes mellitus compared with controls. The splenium of the corpus callosum and the pons had abnormal kurtosis metrics in patients with type 2 diabetes mellitus. Additionally, altered diffusion metrics in the right prefrontal WM were significantly correlated with disease duration and attention task performance in patients with type 2 diabetes mellitus.

CONCLUSIONS

With both conventional diffusion and additional kurtosis metrics, diffusional kurtosis imaging can provide additional information on WM microstructural abnormalities in patients with type 2 diabetes mellitus. Our results indicate that WM microstructural abnormalities occur before cognitive decline and may be used as neuroimaging markers for predicting the early cognitive impairment in patients with type 2 diabetes mellitus.

Early white matter injuries in patients with acute carbon monoxide intoxication: A tract-specific diffusion kurtosis imaging study and STROBE compliant article

Evaluation of acute white matter injuries caused by carbon monoxide (CO) poisoning can be limited by conventional magnetic resonance (MR) imaging. We aim to evaluate the feasibility of diffusion kurtosis imaging (DKI) for early detection of white matter alterations in patients with acute CO intoxication.A total of 30 subjects including 15 acute CO patients and 15 age- and sex-matched healthy volunteers were enrolled in this study. MR examinations were performed on a 3T MR scanner within 8 days after CO intoxication. DKI data were acquired to derive axial, radial, and mean kurtosis, as well as fractional anisotropy (FA), axial, radial, and mean diffusivity for tract-specific comparisons between the 2 groups.Significant decreases of mean kurtosis were shown in the genu of corpus callosum, cingulum, and motor-related tracts (corticospinal and corticobulbar tracts) in patients with acute CO intoxication as compared with controls. On the contrary, significant differences of FA values were merely shown in the regions of corticospinal tracts.DKI demonstrated comparably stronger potential than diffusion tensor imaging in terms of early detection of white matter changes in patients with acute CO intoxication. This may have implications in therapeutic strategy for managing acute CO intoxication patients.

Microstructural Abnormalities Were Found in Brain Gray Matter from Patients with Chronic Myofascial Pain

Myofascial pain, presented as myofascial trigger points (MTrPs)-related pain, is a common, chronic disease involving skeletal muscle, but its underlying mechanisms have been poorly understood. Previous studies have revealed that chronic pain can induce microstructural abnormalities in the cerebral gray matter. However, it remains unclear whether the brain gray matters of patients with chronic MTrPs-related pain undergo alteration. In this study, we employed the Diffusion Kurtosis Imaging (DKI) technique, which is particularly sensitive to brain microstructural perturbation, to monitor the MTrPs-related microstructural alterations in brain gray matter of patients with chronic pain. Our results revealed that, in comparison with the healthy controls, patients with chronic myofascial pain exhibited microstructural abnormalities in the cerebral gray matter and these lesions were mainly distributed in the limbic system and the brain areas involved in the pain matrix. In addition, we showed that microstructural abnormalities in the right anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC) had a significant negative correlation with the course of disease and pain intensity. The results of this study demonstrated for the first time that there are microstructural abnormalities in the brain gray matter of patients with MTrPs-related chronic pain. Our findings may provide new insights into the future development of appropriate therapeutic strategies to this disease.

Can we shorten the q-space imaging to make it clinically feasible?

PURPOSE

Q-space imaging (QSI) is a novel magnetic resonance imaging (MRI) technique that enables assessment of micro-structural changes of white matter. The acquisition time, however, is comparatively long to use for routine clinical assessment. Therefore, the present study investigated the clinically feasible b value combinations to measure the water molecular displacement probability density function (PDF) in healthy subjects.

METHODS

The subjects consisted of five healthy volunteers (1 female and 4 male; 40.8 ± 13.2 years). All MRIs were examined at 1.5 T. The QSI was acquired using a single-shot echo-planar imaging and Δ/δ = 142/17 ms. The magnitude of the gradients was incremented in nine steps to reach a maximal b = 10,000 s/mm². The total acquisition time of this original QSI was 17.4 min. The feasibility of ten alternative b value combinations with the zero-filling or curve fitting technique was assessed. The mean diffusivities (MDs), kurtosis, and zero displacement probability (ZDP) were obtained, and these results were compared in manually segmented regions of interest.

RESULTS

There were alternative b value combinations with a 7.5-min acquisition time and with almost the same PDF.

CONCLUSION

A few alternative b value combinations with the curve fitting technique were found to be able to shorten the QSI acquisition for its clinical feasibility of MD and ZDP.

Evaluation of histopathological changes in the microstructure at the center and periphery of glioma tumors using diffusional kurtosis imaging

OBJECTIVE

To explore the relationship between alterations in gliomas revealed by diffusional kurtosis imaging (DKI) and the histopathological microstructural changes.

METHODS

Thirty-seven patients with cerebral gliomas underwent conventional MRI and DKI at 3.0T. Normalized fractional anisotropy (FA), mean diffusivity (MD) and mean kurtosis (MK) were compared in different regions of glioma tumors. Parameters with a high sensitivity and specificity regarding the discrimination of glioma grade were evaluated using receiver operating characteristic (ROC) curve analysis. Correlations between normalized FA, MD, and MK and histopathological findings (tumor cell density, total vascular area [TVA], and Ki-67 labeling index [LI]) were assessed using Pearson correlation analyses.

RESULTS

Normalized FA, MD, and MK differed significantly between low-grade gliomas (LGGs) and high-grade gliomas (HGGs) (P=0.02, P=0.001 and P<0.001, respectively) at the center of the tumor. Normalized MK exhibited the highest sensitivity (80%) and specificity (100%) in distinguishing HGGs from LGGs. Relative to the tumor center, normalized MK was significantly increased in the tumor periphery (P<0.001) in LGGs and significantly decreased (P=0.002) in HGGs. The significant correlations were found between normalized MK and all histopathological findings (tumor cell density: r=0.596, P=0.006; TVA: r=0.764, P<0.001; and Ki-67 LI: r=0.766, P<0.001) among samples from the center of the tumor.

CONCLUSION

DKI, especially concerning the MK parameter, demonstrated high sensitivity in the detection of microstructural changes in patients with brain gliomas.

Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation

Conventional diffusion imaging techniques are not sufficiently accurate for evaluating glioma grade and cellular proliferation, which are critical for guiding glioma treatment. Diffusion kurtosis imaging (DKI), an advanced non-Gaussian diffusion imaging technique, has shown potential in grading glioma; however, its applications in this tumor have not been fully elucidated. In this study, DKI and diffusion weighted imaging (DWI) were performed on 74 consecutive patients with histopathologically confirmed glioma. The kurtosis and conventional diffusion metric values of the tumor were semi-automatically obtained. The relationships of these metrics with the glioma grade and Ki-67 expression were evaluated. The diagnostic efficiency of these metrics in grading was further compared. It was demonstrated that compared with the conventional diffusion metrics, the kurtosis metrics were more promising imaging markers in distinguishing high-grade from low-grade gliomas and distinguishing among grade II, III and IV gliomas; the kurtosis metrics also showed great potential in the prediction of Ki-67 expression. To our best knowledge, we are the first to reveal the ability of DKI to assess the cellular proliferation of gliomas, and to employ the semi-automatic method for the accurate measurement of gliomas. These results could have a significant impact on the diagnosis and subsequent therapy of glioma.

Effects of perfusion on DTI and DKI estimates in the skeletal muscle

PURPOSE

In this study, we evaluated the effects of perfusion of the skeletal muscle on diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI) parameters and their reproducibility.

METHODS

Diffusion tensor imaging and DKI models, with and without intravoxel incoherent motion (IVIM) correction, were applied to simulated data at different physiological conditions and signal-to-noise ratio levels. Next, the same models were applied to data of the right calf of five healthy volunteers, acquired twice at 3 telsa. For six muscles, we evaluated the correlation of the perfusion signal fraction, with parameters derived from DTI and DKI, and performed repeatability analysis with and without IVIM correction. Additionally, the IVIM correction was compared to a multishell acquisition approach that minimizes perfusion effects on DTI estimates.

RESULTS

Simulations and acquired data showed that DTI and DKI estimates were biased proportionally to the perfusion signal fraction, and that IVIM correction was needed for accurate estimation of the DTI and DKI parameters. However, taking perfusion into account did not improve repeatability.

CONCLUSION

Blood perfusion has an effect on DTI and DKI estimations, but it can be minimized with IVIM correction or multishell acquisition strategies. Magn Reson Med 78:233-246, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Diffusion kurtosis imaging study on temporal lobe after nasopharyngeal carcinoma radiotherapy

PURPOSE

Diffusion kurtosis imaging (DKI) is a MRI technique which can measure alterations in the diffusion of water molecules to reflect tissue changes in both white and grey matter. This study evaluated the potential of DKI for the early diagnosis of radiation-induced temporal lobe changes in the grey and white matter of the temporal lobe in patients with nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

Sixty patients with NPC who had normal MRI brain scans were enrolled and underwent DKI at 1 week (n=20), 6 months (n=20) or 1 year (n=20) after radiotherapy; 20 normal control individuals were also evaluated. Nonlinear fitting routines and equations were used to calculate mean diffusion (MD) and mean kurtosis (MK) and fractional anisotropy (FA). Analysis of variance was used to compare the MK/MD/FA values of white and grey matter between groups.

RESULTS

Compared to the normal control group, grey and white matter MK values were significantly higher at 1 week after radiotherapy and significantly lower at 6 months and 1 year after radiotherapy in patients with NPC, whereas the grey and white matter MD values were significantly lower at 1 week after radiotherapy and returned to normal by 6 months and 1 year after radiotherapy.

CONCLUSION

DKI can be used to detect radiotherapy-induced changes in both the white and grey matter of temporal lobe in patients with NPC. MK and MD values may represent reliable indicators for the early diagnosis of radiation-induced temporal lobe changes in NPC.

Evaluation of mean diffusion and kurtosis MRI mismatch in subacute ischemic stroke: Comparison with NIHSS score

Neurological deterioration (ND) is a devastating complication following ischemic stroke. This study aimed to identify the differences in lesion characteristics in subacute ischemic stroke patients with and without ND using diffusional kurtosis imaging (DKI), as well as to confirm the responsible lesions that may lead to ND, as assessed by the National Institutes of Health Stroke Scale (NIHSS) score. Seventy-nine patients with subacute cerebral infarction were allocated to the ND (-) and ND (+) groups according to the NIHSS score and lesion number. The mean diffusion (MD) lesions were significantly larger than the mean kurtosis (MK) deficits in the ND (+) group (P<0.05); however, there was no significant difference in the ND (-) group (P>0.05). The MD and MK in the lesion recovered to normal levels over time; however, the recovery trends in the ND (+) group were substantially slower than the ND (-) group. The differences between the two groups were only significant regarding the MK (p<0.05). Furthermore, multiple infarction lesions exhibited good consistency in the ND (-) group, but were non-homogeneous in the ND (+) group. To the best of our knowledge, this is the first study to demonstrate that a significant MD/MK mismatch and heterogeneity of multiple ischemic lesions on MK in subacute ischemic stroke may represent a new expansion of an ischemic lesion or acute reinfarction, which is closely related to ND.

The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)

The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MK_T), and DIVIDE was used to decompose MK_T into components caused by microscopic anisotropy (MK_A) and isotropic heterogeneity (MK_I). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MK_A correlated with cell eccentricity (r=0.95, p<10^-7) and MK_I with the cell density variance (r=0.83, p<10^-3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r=0.80, p<10^-3) and microscopic scale (μFA, r=0.93, p<10^-6). A multiple regression analysis showed that the conventional MK_T parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MK_A was associated only to cell eccentricity, and MK_I only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean±s.d.) MK_A=1.11±0.33 vs MK_I=0.44±0.20 (p<10^-3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MK_I=0.57±0.30 vs MK_A=0.26±0.11 (p<0.05). In conclusion, DIVIDE allows non-invasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.

Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline

There is evolving evidence that individuals categorized with subjective cognitive decline (SCD) are potentially at higher risk for developing objective and progressive cognitive impairment compared to cognitively healthy individuals without apparent subjective complaints. Interestingly, SCD, during advancing preclinical Alzheimer's disease (AD), may denote very early, subtle cognitive decline that cannot be identified using established standardized tests of cognitive performance. The substantial heterogeneity of existing SCD-related research data has led the Subjective Cognitive Decline Initiative (SCD-I) to accomplish an international consensus on the definition of a conceptual research framework on SCD in preclinical AD. In the area of biological markers, the cerebrospinal fluid signature of AD has been reported to be more prevalent in subjects with SCD compared to healthy controls; moreover, there is a pronounced atrophy, as demonstrated by magnetic resonance imaging, and an increased hypometabolism, as revealed by positron emission tomography, in characteristic brain regions affected by AD. In addition, SCD individuals carrying an apolipoprotein ɛ4 allele are more likely to display AD-phenotypic alterations. The urgent requirement to detect and diagnose AD as early as possible has led to the critical examination of the diagnostic power of biological markers, neurophysiology, and neuroimaging methods for AD-related risk and clinical progression in individuals defined with SCD. Observational studies on the predictive value of SCD for developing AD may potentially be of practical value, and an evidence-based, validated, qualified, and fully operationalized concept may inform clinical diagnostic practice and guide earlier designs in future therapy trials.

Diffusional kurtosis imaging for differentiating between high-grade glioma and primary central nervous system lymphoma

BACKGROUND

The aim of this study was to assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters for differentiating high-grade gliomas (HGGs) from primary central nervous system lymphomas (PCNSLs).

METHODS

Diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ// ), radial diffusivity (λ⊥ ); and kurtosis parameters, including mean kurtosis (MK), axial kurtosis (K// ), and radial kurtosis (K⊥ ), were normalized to contralateral normal-appearing white matter (NAWMc) to decrease inter-individual and inter-regional changes across the entire brain, and then compared with the solid parts of 20 HGGs and 11 PCNSLs [median 95% confidence interval (CI), P < 0.004; 0.05/14], significance level, Kolmogorov-Smirnov test, Bonferroni correction].

RESULTS

FA, MD, λ// , and λ⊥ values were higher in HGGs than in PCNSLs, but not significantly [HGGs: 0.209 (95% CI, 0.134-0.338), 1.385 (95% CI, 1.05-1.710), 1.655 (95% CI, 1.30-2.060), 1.228 (95% CI, 0.932-1.480), respectively; PCNSLs: 0.143 (95% CI, 0.110-0.317), 1.070 (95% CI, 0.842-1.470), 1.260 (95% CI, 0.960-1.930), 1.010 (95% CI, 0.782-1.240)], respectively; P = 0.120, 0.010, 0.004, and 0.004, respectively). However, MK and K// were significantly higher in PCNSLs compared with HGGs [PCNSLs: 0.765 (95% CI, 0.697-0.890), 0.787 (95% CI, 0.615-1.030), respectively; HGGs: 0.531 (95% CI, 0.402-0.766), 0.532 (95% CI, 0.432-0.680], respectively; P = 0.001, 0.000, respectively); but not K⊥ [0.774 (95% CI, 0.681-0.899) for PCNSLs; 0.554 (95% CI, 0.389-0.954) for HGGs; P = 0.024].

CONCLUSION

There were significant differences in kurtosis parameters (MK and K// ) between HGGs and PCNSLs, while differences in diffusion parameters between them did not reach significance; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters. J. Magn. Reson. Imaging 2016;44:30-40.

Characterization of clear cell renal cell carcinoma with diffusion kurtosis imaging: correlation between diffusion kurtosis parameters and tumor cellularity

The aim of this study was to evaluate the role of diffusion kurtosis imaging (DKI) in the characterization of clear cell renal cell carcinoma (ccRCC) and to correlate DKI parameters with tumor cellularity. Fifty-nine patients with pathologically diagnosed ccRCCs were evaluated by DKI on a 3-T scanner. Regions of interest were drawn on the maps of the mean diffusion coefficient (MD) and mean diffusion kurtosis (MK). All ccRCCs were histologically graded according to the Fuhrman classification system. Tumor cellularity was measured by the nuclear-to-cytoplasm (N/C) ratio and the number of tumor cell nuclei (NTCN). ccRCCs were classified as grade 1 (n = 23), grade 2 (n = 24), grade 3 (n = 10) and grade 4 (n = 3). Both MD and MK could readily discriminate between normal renal parenchyma and ccRCCs (p < 0.001), and receiver operating characteristic (ROC) curve analysis showed that MK exhibited a better performance with an area under the ROC curve of 0.874 and sensitivity/specificity of 68.33%/100% (p < 0.001). Further, MD and MK were significantly different between grade 1 and grades 3 and 4 (p = 0.01, p < 0.001) and between grade 2 and grades 3 and 4 (p = 0.015, p < 0.005), respectively. However, no significant difference was found between grade 1 and grade 2 (p > 0.05) for both MD and MK. With regard to NTCN, no significant difference was found between any two grades (p > 0.05), and the N/C ratio changed significantly with grade (p < 0.01, between any two grades). Negative correlations were found between MK and MD (r = -0.56, p < 0.001), and between MD and N/C ratio (r = -0.36, p < 0.005), whereas MK and the N/C ratio were positively correlated (r = 0.45, p = 0.003). DKI could quantitatively characterize ccRCC with different grades by probing non-Gaussian diffusion properties related to changes in the tumor microenvironment or tissue complexities in the tumor. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison of image sensitivity between conventional tensor-based and fast diffusion kurtosis imaging protocols in a rodent model of acute ischemic stroke

Diffusion kurtosis imaging (DKI) can offer a useful complementary tool to routine diffusion MRI for improved stratification of heterogeneous tissue damage in acute ischemic stroke. However, its relatively long imaging time has hampered its clinical application in the emergency setting. A recently proposed fast DKI approach substantially shortens the imaging time, which may help to overcome the scan time limitation. However, to date, the sensitivity of the fast DKI protocol for the imaging of acute stroke has not been fully described. In this study, we performed routine and fast DKI scans in a rodent model of acute stroke, and compared the sensitivity of diffusivity and kurtosis indices (i.e. axial, radial and mean) in depicting acute ischemic lesions. In addition, we analyzed the contrast-to-noise ratio (CNR) between the ipsilateral ischemic and contralateral normal regions using both conventional and fast DKI methods. We found that the mean kurtosis shows a relative change of 47.1 ± 7.3% between the ischemic and contralateral normal regions, being the most sensitive parameter in revealing acute ischemic injury. The two DKI methods yielded highly correlated diffusivity and kurtosis measures and lesion volumes (R(2)  ⩾ 0.90, p < 0.01). Importantly, the fast DKI method exhibited significantly higher CNR of mean kurtosis (1.6 ± 0.2) compared with the routine tensor protocol (1.3 ± 0.2, p < 0.05), with its CNR per unit time (CNR efficiency) approximately doubled when the scan time was taken into account. In conclusion, the fast DKI method provides excellent sensitivity and efficiency to image acute ischemic tissue damage, which is essential for image-guided and individualized stroke treatment.

Assessment of severity of leukoaraiosis: a diffusional kurtosis imaging study

OBJECTIVE

The objective was to investigate the capabilities of diffusional kurtosis imaging (DKI) in detection of age-related white matter (WM) changes in elderly patients with leukoaraiosis.

MATERIAL AND METHODS

Fractional anisotropy (FA), kurtosis, and diffusion parameters in the frontal lobe and parietal lobe were compared between 14 patients at Fazekas scale 0 and 1, and 15 patients at Fazekas scale 2 and 3.

RESULTS

FA and DKI parameters were significantly altered in the ischemic lesions vs normal regions of WM in the severe patients.

CONCLUSION

DKI can provide sensitive imaging biomarkers for assessing the severity of leukoaraiosis in reference to Fazekas score.

Diffusional Kurtosis Imaging in Idiopathic Normal Pressure Hydrocephalus: Correlation with Severity of Cognitive Impairment

PURPOSE

Diffusional kurtosis imaging (DKI) is an emerging technique that describes diffusion of water molecules in terms of deviation from Gaussian distribution. This study investigated correlations between DKI metrics and cognitive function in patients with idiopathic normal pressure hydrocephalus (iNPH).

MATERIALS AND METHODS

DKI was performed in 29 iNPH patients and 14 age-matched controls. Mini-mental state examination (MMSE), frontal assessment battery (FAB), and trail making test A (TMT-A) were used as cognitive measures. Tract-based spatial statistics (TBSS) analyses were performed to investigate the between-group differences and correlations with the cognitive measures of the diffusion metrics, including mean kurtosis (MK), fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD).

RESULTS

In iNPH patients, FA and MK identified positive correlations with cognitive function in similar regions, predominantly in the frontal lobes (P < 0.05, corrected for multiple comparisons). The frontoparietal subcortical white matter showed significant correlations with FAB and TMT-A across more extensive areas in MK analyses than in FA. ADC, AD, and RD analyses showed no significant correlations with MMSE and FAB, while negative correlation with TMT-A was observed in the limited portion of the frontal deep white matter.

CONCLUSION

Both FA and MK correlated well with cognitive impairment in iNPH. The observed differences between FA and MK results suggest DKI may play a complementary role to conventional FA and ADC analyses, especially for evaluation of the subcortical white matter.

Separation and quantification of lactate and lipid at 1.3 ppm by diffusion-weighted magnetic resonance spectroscopy

PURPOSE

To separate the spectrally overlapped lactate and lipid signals at 1.3 ppm using diffusion-weighted magnetic resonance spectroscopy (DW-MRS) based on their large diffusivity difference.

METHODS

DW-MRS was applied to the gel phantoms containing lactate and lipid droplets, and to the rat brain tumors. Lactate and lipid signals and their apparent diffusion coefficients were computed from the diffusion-weighted proton spectra. Biexponential fitting and direct spectral subtraction approaches were employed and compared.

RESULTS

DW-MRS could effectively separate lactate and lipid signals both in phantoms and rat brain C6 glioma by biexponential fitting. In phantoms, lactate and lipid signals highly correlated with the known lactate concentration and lipid volume fractions. In C6 glioma, both lactate and lipid signals were detected, and the lipid signal was an order of magnitude higher than lactate signal. The spectral subtraction approach using three diffusion weightings also allowed the separation of lactate and lipid signals, yielding results comparable to those by the biexponential fitting approach.

CONCLUSION

DW-MRS presents a new approach to separate and quantify spectrally overlapped molecules and/or macromolecules, such as lactate and lipid, by using the diffusivity difference associated with their different sizes or mobility within tissue microstructure. Magn Reson Med 77:480-489, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Various diffusion magnetic resonance imaging techniques for pancreatic cancer

Pancreatic cancer is one of the most common malignant tumors and remains a treatment-refractory cancer with a poor prognosis. Currently, the diagnosis of pancreatic neoplasm depends mainly on imaging and which methods are conducive to detecting small lesions. Compared to the other techniques, magnetic resonance imaging (MRI) has irreplaceable advantages and can provide valuable information unattainable with other noninvasive or minimally invasive imaging techniques. Advances in MR hardware and pulse sequence design have particularly improved the quality and robustness of MRI of the pancreas. Diffusion MR imaging serves as one of the common functional MRI techniques and is the only technique that can be used to reflect the diffusion movement of water molecules in vivo. It is generally known that diffusion properties depend on the characterization of intrinsic features of tissue microdynamics and microstructure. With the improvement of the diffusion models, diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique to the more complex. In this review, the various diffusion MRI techniques for pancreatic cancer are discussed, including conventional diffusion weighted imaging (DWI), multi-b DWI based on intra-voxel incoherent motion theory, diffusion tensor imaging and diffusion kurtosis imaging. The principles, main parameters, advantages and limitations of these techniques, as well as future directions for pancreatic diffusion imaging are also discussed.

Experimental considerations for fast kurtosis imaging

PURPOSE

The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor W¯ and mean diffusivity ( D¯) was made possible from 13 distinct diffusion weighted MRI acquisitions (the 1-3-9 protocol) with simple postprocessing. Here, we analyze the effects of noise and nonideal diffusion encoding, and propose a new correction strategy. We also present a 1-9-9 protocol with increased robustness to experimental imperfections and minimal additional scan time. This refinement does not affect computation time and also provides a fast estimate of fractional anisotropy (FA).

THEORY AND METHODS

1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of D¯, FA, W¯ from human brains are compared with traditional estimates from an extensive diffusion kurtosis imaging data set. Simulations are used to evaluate the influence of noise and diffusion encodings deviating from the scheme, and the performance of the correction strategy. Optimal b-values are determined from simulations and data.

RESULTS

Accuracy and precision in D¯ and W¯ are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in nonideal data.

CONCLUSION

The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented. Magn Reson Med 76:1455-1468, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Physics, Techniques and Review of Neuroradiological Applications of Diffusion Kurtosis Imaging (DKI)

In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely map the complex underlying cellular components and structures, which hinder and restrict the diffusion of water molecules. These limitations can be partially overcome by means of diffusion kurtosis imaging (DKI). The aim of this paper is the description of the theory of DKI, a new topic of growing interest in radiology. DKI is a higher order diffusion model that is a straightforward extension of the DTI model. Here, we analyze the physics underlying this method, we report our MRI acquisition protocol with the preprocessing pipeline used and the DKI parametric maps obtained on a 1.5 T scanner, and we review the most relevant clinical applications of this technique in various neurological diseases.

Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure

Diffusion tensor imaging (DTI) is the most widely used method for characterizing noninvasively structural and architectural features of brain tissues. However, the assumption of a Gaussian spin displacement distribution intrinsic to DTI weakens its ability to describe intricate tissue microanatomy. Consequently, the biological interpretation of microstructural parameters, such as fractional anisotropy or mean diffusivity, is often equivocal. We evaluate the clinical feasibility of assessing brain tissue microstructure with mean apparent propagator (MAP) MRI, a powerful analytical framework that efficiently measures the probability density function (PDF) of spin displacements and quantifies useful metrics of this PDF indicative of diffusion in complex microstructure (e.g., restrictions, multiple compartments). Rotation invariant and scalar parameters computed from the MAP show consistent variation across neuroanatomical brain regions and increased ability to differentiate tissues with distinct structural and architectural features compared with DTI-derived parameters. The return-to-origin probability (RTOP) appears to reflect cellularity and restrictions better than MD, while the non-Gaussianity (NG) measures diffusion heterogeneity by comprehensively quantifying the deviation between the spin displacement PDF and its Gaussian approximation. Both RTOP and NG can be decomposed in the local anatomical frame for reference determined by the orientation of the diffusion tensor and reveal additional information complementary to DTI. The propagator anisotropy (PA) shows high tissue contrast even in deep brain nuclei and cortical gray matter and is more uniform in white matter than the FA, which drops significantly in regions containing crossing fibers. Orientational profiles of the propagator computed analytically from the MAP MRI series coefficients allow separation of different fiber populations in regions of crossing white matter pathways, which in turn improves our ability to perform whole-brain fiber tractography. Reconstructions from subsampled data sets suggest that MAP MRI parameters can be computed from a relatively small number of DWIs acquired with high b-value and good signal-to-noise ratio in clinically achievable scan durations of less than 10min. The neuroanatomical consistency across healthy subjects and reproducibility in test-retest experiments of MAP MRI microstructural parameters further substantiate the robustness and clinical feasibility of this technique. The MAP MRI metrics could potentially provide more sensitive clinical biomarkers with increased pathophysiological specificity compared to microstructural measures derived using conventional diffusion MRI techniques.

Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice

Technologic advances enable performance of diffusion-weighted imaging (DWI) at ultrahigh b-values, where standard monoexponential model analysis may not apply. Rather, non-Gaussian water diffusion properties emerge, which in cellular tissues are, in part, influenced by the intracellular environment that is not well evaluated by conventional DWI. The novel technique, diffusion kurtosis imaging (DKI), enables characterization of non-Gaussian water diffusion behavior. More advanced mathematical curve fitting of the signal intensity decay curve using the DKI model provides an additional parameter Kapp that presumably reflects heterogeneity and irregularity of cellular microstructure, as well as the amount of interfaces within cellular tissues. Although largely applied for neural applications over the past decade, a small number of studies have recently explored DKI outside the brain. The most investigated organ is the prostate, with preliminary studies suggesting improved tumor detection and grading using DKI. Although still largely in the research phase, DKI is being explored in wider clinical settings. When assessing extracranial applications of DKI, careful attention to details with which body radiologists may currently be unfamiliar is important to ensure reliable results. Accordingly, a robust understanding of DKI is necessary for radiologists to better understand the meaning of DKI-derived metrics in the context of different tumors and how these metrics vary between tumor types and in response to treatment. In this review, we outline DKI principles, propose biostructural basis for observations, provide a comparison with standard monoexponential fitting and the apparent diffusion coefficient, report on extracranial clinical investigations to date, and recommend technical considerations for implementation in body imaging.

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination

Although MRI is the gold standard for the diagnosis and monitoring of multiple sclerosis (MS), current conventional MRI techniques often fail to detect cortical alterations and provide little information about gliosis, axonal damage and myelin status of lesioned areas. Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) provide sensitive and complementary measures of the neural tissue microstructure. Additionally, specific white matter tract integrity (WMTI) metrics modelling the diffusion in white matter were recently derived. In the current study we used the well-characterized cuprizone mouse model of central nervous system demyelination to assess the temporal evolution of diffusion tensor (DT), diffusion kurtosis tensor (DK) and WMTI-derived metrics following acute inflammatory demyelination and spontaneous remyelination. While DT-derived metrics were unable to detect cuprizone induced cortical alterations, the mean kurtosis (MK) and radial kurtosis (RK) were found decreased under cuprizone administration, as compared to age-matched controls, in both the motor and somatosensory cortices. The MK remained decreased in the motor cortices at the end of the recovery period, reflecting long lasting impairment of myelination. In white matter, DT, DK and WMTI-derived metrics enabled the detection of cuprizone induced changes differentially according to the stage and the severity of the lesion. More specifically, the MK, the RK and the axonal water fraction (AWF) were the most sensitive for the detection of cuprizone induced changes in the genu of the corpus callosum, a region less affected by cuprizone administration. Additionally, microgliosis was associated with an increase of MK and RK during the acute inflammatory demyelination phase. In regions undergoing severe demyelination, namely the body and splenium of the corpus callosum, DT-derived metrics, notably the mean diffusion (MD) and radial diffusion (RD), were among the best discriminators between cuprizone and control groups, hence highlighting their ability to detect both acute and long lasting changes. Interestingly, WMTI-derived metrics showed the aptitude to distinguish between the different stages of the disease. Both the intra-axonal diffusivity (Da) and the AWF were found to be decreased in the cuprizone treated group, Da specifically decreased during the acute inflammatory demyelinating phase whereas the AWF decrease was associated to the spontaneous remyelination and the recovery period. Altogether our results demonstrate that DKI is sensitive to alterations of cortical areas and provides, along with WMTI metrics, information that is complementary to DT-derived metrics for the characterization of demyelination in both white and grey matter and subsequent inflammatory processes associated with a demyelinating event.

Differentiation of high-grade-astrocytomas from solitary-brain-metastases: Comparing diffusion kurtosis imaging and diffusion tensor imaging

OBJECTIVE

To compare the value of MRI diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in differentiating high-grade-astrocytomas from solitary-brain-metastases.

METHODS

Thirty-one high-grade-astrocytomas and twenty solitary-brain-metastases were retrospectively identified. DKI parameters [mean kurtosis (MK), radial kurtosis (Kr), and axial kurtosis (Ka)] and DTI parameters [fractional anisotropy (FA) and mean diffusivity (MD)] values with and without correction by contralateral normal-appearing white matter (NAWM) in the tumoral solid part and peritumoral edema, were compared using the t-test. Receiver operating characteristic (ROC) curves were used to test for the best parameters.

RESULTS

The DKI values (MK, Kr, and Ka) and DTI values (FA and MD) in tumoral solid parts did not show significant differences between the two groups. Corrected and uncorrected MK, Kr, and Ka values in peritumoral edema were significantly higher in high-grade-astrocytomas than solitary-brain-metastases, and MD values without correction were lower in high-grade astrocytomas than solitary-brain-metastases. The areas under curve (AUC) of corrected Ka (1.000), MK (0.889), and Kr (0.880) values were significantly higher than those of MD (0.793) and FA (0.472) values. The optimal thresholds for corrected MK, Kr, Ka, and MD were 0.369, 0.405, 0.483, and 2.067, respectively.

CONCLUSION

DKI and directional analysis could lead to improved differentiation with better sensitivity and directional specificity than DTI.

Optimization of white matter fiber tractography with diffusional kurtosis imaging

Diffusional kurtosis imaging (DKI) is a clinically feasible diffusion MRI technique for white matter (WM) fiber tractography (FT) with the ability to directly resolve intra-voxel crossing fibers by means of the kurtosis diffusion orientation distribution function (dODF). Here we expand on previous work by exploring properties of the kurtosis dODF and their subsequent effects on WM FT for in vivo human data. For comparison, the results are contrasted with fiber bundle orientation estimates provided by the diffusion tensor, which is the primary quantity obtained from diffusion tensor imaging. We also outline an efficient method for performing DKI-based WM FT that can substantially decrease the computational requirements. The recommended method for implementing the kurtosis ODF is demonstrated to optimize the reproducibility and sensitivity of DKI for detecting crossing fibers while reducing the occurrence of non-physically-meaningful, negative values in the kurtosis dODF approximation. In addition, DKI-based WM FT is illustrated for different protocols differing in image acquisition times from 48 to 5.3 min.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

BACKGROUND

We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas.

METHODS

In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients.

RESULTS

Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%.

CONCLUSIONS

Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.

Evaluation of diffusion kurtosis imaging in ex vivo hypomyelinated mouse brains

Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and DKI-derived white matter tract integrity metrics (WMTI) were experimentally evaluated ex vivo through comparisons to histological measurements and established magnetic resonance imaging (MRI) measures of myelin in two knockout mouse models with varying degrees of hypomyelination. DKI metrics of mean and radial kurtosis were found to be better indicators of myelin content than conventional DTI metrics. The biophysical WMTI model based on the DKI framework reported on axon water fraction with good accuracy in cases with near normal axon density, but did not provide additional specificity to myelination. Overall, DKI provided additional information regarding white matter microstructure compared with DTI, making it an attractive method for future assessments of white matter development and pathology.

Diffusion-MRI in neurodegenerative disorders

The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.

Diffusion tensor imaging findings in semi-acute mild traumatic brain injury

The past 10 years have seen a rapid increase in the use of diffusion tensor imaging to identify biomarkers of traumatic brain injury (TBI). Although the literature generally indicates decreased anisotropic diffusion at more chronic injury periods and in more severe injuries, considerable debate remains regarding the direction (i.e., increased or decreased) of anisotropic diffusion in the acute to semi-acute phase (here defined as less than 3 months post-injury) of mild TBI (mTBI). A systematic review of the literature was therefore performed to (1) determine the prevalence of different anisotropic diffusion findings (increased, decreased, bidirectional, or null) during the semi-acute injury phase of mTBI and to (2) identify clinical (e.g., age of injury, post-injury scan time, etc.) and experimental factors (e.g., number of unique directions, field strength) that may influence these findings. Results from the literature review indicated 31 articles with independent samples of semi-acute mTBI patients, with 13 studies reporting decreased anisotropic diffusion, 11 reporting increased diffusion, 2 reporting bidirectional findings, and 5 reporting null findings. Chi-squared analyses indicated that the total number of diffusion-weighted (DW) images was significantly associated with findings of either increased (DW ≥ 30) versus decreased (DW ≤ 25) anisotropic diffusion. Other clinical and experimental factors were not statistically significant for direction of anisotropic diffusion, but these results may have been limited by the relatively small number of studies within each domain (e.g., pediatric studies). In summary, current results indicate roughly equivalent number of studies reporting increased versus decreased anisotropic diffusion during semi-acute mTBI, with the number of unique diffusion images being statistically associated with the direction of findings.

Hypercellularity Components of Glioblastoma Identified by High b-Value Diffusion-Weighted Imaging

PURPOSE

Use of conventional magnetic resonance imaging (MRI) for target definition may expose glioblastomas (GB) to inadequate radiation dose coverage of the nonenhanced hypercellular subvolume. This study aimed to develop a technique to identify the hypercellular components of GB by using high b-value diffusion-weighted imaging (DWI) and to investigate its relationship with the prescribed 95% isodose volume (PDV) and progression-free survival (PFS).

METHODS AND MATERIALS

Twenty-one patients with GB underwent chemoradiation therapy post-resection and biopsy. Radiation therapy (RT) treatment planning was based upon conventional MRI. Pre-RT DWIs were acquired in 3 orthogonal directions with b-values of 0, 1000, and 3000 s/mm(2). Hypercellularity volume (HCV) was defined on the high b-value (3000 s/mm(2)) DWI by a threshold method. Nonenhanced signified regions not covered by the Gd-enhanced gross tumor volume (GTV-Gd) on T1-weighted images. The PDV was used to evaluate spatial coverage of the HCV by the dose plan. Association between HCV and PFS or other clinical covariates were assessed using univariate proportional hazards regression models.

RESULTS

HCVs and nonenhanced HCVs varied from 0.58 to 67 cm(3) (median: 9.8 cm(3)) and 0.15 to 60 cm(3) (median: 2.5 cm(3)), respectively. Fourteen patients had incomplete dose coverage of the HCV, 6 of whom had >1 cm(3) HCV missed by the 95% PDV (range: 1.01-25.4 cm(3)). Of the 15 patients who progressed, 5 progressed earlier, within 6 months post-RT, and 10 patients afterward. Pre-RT HCVs within recurrent GTVs-Gd were 78% (range: 65%-89%) for the 5 earliest progressions but lower, 53% (range: 0%-85%), for the later progressions. HCV and nonenhanced HCV were significant negative prognostic indicators for PFS (P<.002 and P<.01, respectively). The hypercellularity subvolume not covered by the 95% PDV was a significant negative predictor for PFS (P<.05).

CONCLUSIONS

High b-value DWI identifies the hypercellular components of GB and could aid in RT target volume definition. Future studies will allow us to investigate the role of high b-value DWI in identifying radiation boost volumes and diagnosing progression.

Parsimonious continuous time random walk models and kurtosis for diffusion in magnetic resonance of biological tissue

In this paper, we provide a context for the modeling approaches that have been developed to describe non-Gaussian diffusion behavior, which is ubiquitous in diffusion weighted magnetic resonance imaging of water in biological tissue. Subsequently, we focus on the formalism of the continuous time random walk theory to extract properties of subdiffusion and superdiffusion through novel simplifications of the Mittag-Leffler function. For the case of time-fractional subdiffusion, we compute the kurtosis for the Mittag-Leffler function, which provides both a connection and physical context to the much-used approach of diffusional kurtosis imaging. We provide Monte Carlo simulations to illustrate the concepts of anomalous diffusion as stochastic processes of the random walk. Finally, we demonstrate the clinical utility of the Mittag-Leffler function as a model to describe tissue microstructure through estimations of subdiffusion and kurtosis with diffusion MRI measurements in the brain of a chronic ischemic stroke patient.

Exploring the 3D geometry of the diffusion kurtosis tensor--impact on the development of robust tractography procedures and novel biomarkers

Diffusion kurtosis imaging (DKI) is a diffusion-weighted technique which overcomes limitations of the commonly used diffusion tensor imaging approach. This technique models non-Gaussian behaviour of water diffusion by the diffusion kurtosis tensor (KT), which can be used to provide indices of tissue heterogeneity and a better characterisation of the spatial architecture of tissue microstructure. In this study, the geometry of the KT is elucidated using synthetic data generated from multi-compartmental models, where diffusion heterogeneity between intra- and extra-cellular media is taken into account, as well as the sensitivity of the results to each model parameter and to synthetic noise. Furthermore, based on the assumption that the maxima of the KT are distributed perpendicularly to the direction of well-aligned fibres, a novel algorithm for estimating fibre direction directly from the KT is proposed and compared to the fibre directions extracted from DKI-based orientation distribution function (ODF) estimates previously proposed in the literature. Synthetic data results showed that, for fibres crossing at high intersection angles, direction estimates extracted directly from the KT have smaller errors than the DKI-based ODF estimation approaches (DKI-ODF). Nevertheless, the proposed method showed smaller angular resolution and lower stability to changes of the simulation parameters. On real data, tractography performed on these KT fibre estimates suggests a higher sensitivity than the DKI-based ODF in resolving lateral corpus callosum fibres reaching the pre-central cortex when diffusion acquisition is performed with five b-values. Using faster acquisition schemes, KT-based tractography did not show improved performance over the DKI-ODF procedures. Nevertheless, it is shown that direct KT fibre estimates are more adequate for computing a generalised version of radial kurtosis maps.

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.

Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia

Diffusion kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI), exhibiting improved sensitivity and specificity in detecting developmental and pathological changes in neural tissues. However, little attention was paid to the performances of DKI and DTI in detecting white matter abnormality in schizophrenia. In this study, DKI and DTI were performed in 94 schizophrenia patients and 91 sex- and age-matched healthy controls. White matter integrity was assessed by fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK) of DKI and FA, MD, AD and RD of DTI. Group differences in these parameters were compared using tract-based spatial statistics (TBSS) (P < 0.01, corrected). The sensitivities in detecting white matter abnormality in schizophrenia were MK (34%) > AK (20%) > RK (3%) and RD (37%) > FA (24%) > MD (21%) for DKI, and RD (43%) > FA (30%) > MD (21%) for DTI. DKI-derived diffusion parameters (RD, FA and MD) were sensitive to detect abnormality in white matter regions (the corpus callosum and anterior limb of internal capsule) with coherent fiber arrangement; however, the kurtosis parameters (MK and AK) were sensitive to reveal abnormality in white matter regions (the juxtacortical white matter and corona radiata) with complex fiber arrangement. In schizophrenia, the decreased AK suggests axonal damage; however, the increased RD indicates myelin impairment. These findings suggest that diffusion and kurtosis parameters could provide complementary information and they should be jointly used to reveal pathological changes in schizophrenia.

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Kurtosis parameters are suitable to assess WM regions with complex fiber arrangement.

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Diffusion parameters are suitable to assess WM regions with coherent fiber arrangement.

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Increased RD suggests myelin abnormalities in schizophrenia.

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Decreased AK indicates axonal abnormalities in schizophrenia.

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.

Kurtosis analysis of neural diffusion organization

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.

Preoperative diffusion tensor imaging: improving neurosurgical outcomes in brain tumor patients

Preoperative mapping has revolutionized neurosurgical care for brain tumor patients. Maximizing resections has improved diagnosis, optimized treatment algorithms, and decreased potentially devastating postoperative deficits. Although mapping has multiple steps and complimentary localization sources, diffusion tensor imaging (DTI) excels in its essential role in depicting white matter tracts. A thorough understanding of DTI, data visualization methods, and limitations with mastery of functional and dysfunctional white matter anatomy is necessary to realize the potential of DTI. By establishing spatial relationships between lesion borders and functional networks preoperatively and intraoperatively, DTI is central to high-risk neurosurgical resections and becoming the standard of care.

Stress-induced alterations in large-scale functional networks of the rodent brain

Stress-related psychopathology is associated with altered functioning of large-scale brain networks. Animal research into chronic stress, one of the most prominent environmental risk factors for development of psychopathology, has revealed molecular and cellular mechanisms potentially contributing to human mental disease. However, so far, these studies have not addressed the system-level changes in extended brain networks, thought to critically contribute to mental disorders. We here tested the effects of chronic stress exposure (10 days immobilization) on the structural integrity and functional connectivity patterns in the brain, using high-resolution structural MRI, diffusion kurtosis imaging, and resting-state functional MRI, while confirming the expected changes in neuronal dendritic morphology using Golgi-staining. Stress effectiveness was confirmed by a significantly lower body weight and increased adrenal weight. In line with previous research, stressed animals displayed neuronal dendritic hypertrophy in the amygdala and hypotrophy in the hippocampal and medial prefrontal cortex. Using independent component analysis of resting-state fMRI data, we identified ten functional connectivity networks in the rodent brain. Chronic stress appeared to increase connectivity within the somatosensory, visual, and default mode networks. Moreover, chronic stress exposure was associated with an increased volume and diffusivity of the lateral ventricles, whereas no other volumetric changes were observed. This study shows that chronic stress exposure in rodents induces alterations in functional network connectivity strength which partly resemble those observed in stress-related psychopathology. Moreover, these functional consequences of stress seem to be more prominent than the effects on gross volumetric change, indicating their significance for future research.

A longitudinal evaluation of diffusion kurtosis imaging in patients with mild traumatic brain injury

PRIMARY OBJECTIVE

To investigate longitudinal diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) changes in white and grey matter in patients with mild traumatic brain injury (mTBI).

RESEARCH DESIGN

A prospective case-control study.

METHODS AND PROCEDURES

DKI data was obtained from 24 patients with mTBI along with cognitive assessments within 10 days, 1 month and 6 months post-injury and compared with age-matched control (n¼ 24). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusion (l(r)), mean kurtosis (MK) and radial kurtosis (Kr) were extracted from the thalamus, internal capsule and corpus callosum.

MAIN OUTCOMES AND RESULTS

Results demonstrate reduced Kr and MK in the anterior internal capsule in patients with mTBI across the three visits, and reduced MK in the posterior internal capsule during the 10 day time point. Correlations were observed between the change in MK or Kr between 1–6 months and the improvements in cognition between the 1 and 6 month visits in the thalamus, internal capsule and corpus callosum.

CONCLUSIONS

These data demonstrate that DKI may be sensitive in tracking pathophysiological changes associated with mTBI and may provide additional information to conventional DTI parameters in evaluating longitudinal changes following TBI.