Evaluation of a continuous-time random-walk diffusion model for the differentiation of malignant and benign breast lesions and its association with Ki-67 expression.

The purpose of the current study was to evaluate the performance of a continuous-time random-walk (CTRW) diffusion model for differentiating malignant and benign breast lesions and to consider the potential association between CTRW parameters and the Ki-67 expression. Sixty-four patients (46.2 ± 11.4 years) with breast lesions (29 malignant and 35 benign) were evaluated with the CTRW model, intravoxel incoherent motion model, and diffusion-weighted imaging. Echo planar diffusion-weighted imaging was conducted using 13 b-values (0-3000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity, α and ß, respectively, were obtained, and had MRI b-values of 0-3000 s/mm2 . Receiver operating characteristic (ROC) analysis was conducted to determine the sensitivity, specificity, and diagnostic accuracy of CTRW parameters for differentiating malignant from benign breast lesions. In malignant breast lesions, the CTRW parameters Dm , α, and ß were significantly lower than the corresponding parameters of benign breast lesions. In the malignant breast lesion group, the CTRW parameter Dm was significantly lower in high Ki-67 expression than in low Ki-67 expression. In ROC analysis, the combination of CTRW parameters (Dm , α, ß) demonstrated the highest area under the curve value (0.985) and diagnostic accuracy (94.23%) in differentiating malignant and benign breast lesions. The CTRW model effectively differentiated malignant from benign breast lesions. The CTRW diffusion model offers a new way for noninvasive assessment of breast malignancy and better understanding of the proliferation of malignant lesions.

Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions.

Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Universal Evolution of Fickian Non-Gaussian Diffusion in Two- and Three-Dimensional Glass-Forming Liquids.

Recent works show that glass-forming liquids display Fickian non-Gaussian Diffusion, with non-Gaussian displacement distributions persisting even at very long times, when linearity in the mean square displacement (Fickianity) has already been attained. Such non-Gaussian deviations temporarily exhibit distinctive exponential tails, with a decay length λ growing in time as a power-law. We herein carefully examine data from four different glass-forming systems with isotropic interactions, both in two and three dimensions, namely, three numerical models of molecular liquids and one experimentally investigated colloidal suspension. Drawing on the identification of a proper time range for reliable exponential fits, we find that a scaling law λ(t)∝tα, with α≃1/3, holds for all considered systems, independently from dimensionality. We further show that, for each system, data at different temperatures/concentration can be collapsed onto a master-curve, identifying a characteristic time for the disappearance of exponential tails and the recovery of Gaussianity. We find that such characteristic time is always related through a power-law to the onset time of Fickianity. The present findings suggest that FnGD in glass-formers may be characterized by a "universal" evolution of the distribution tails, independent from system dimensionality, at least for liquids with isotropic potential.

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner.

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-Gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (µK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, µK is typically ignored in diffusion MRI signal modelling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible µK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of µK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that µK significantly contributes to total diffusional kurtosis both in grey and white matter tissue but, as expected, not in the ventricles. The first µK maps of the human brain are presented, revealing that the spatial distribution of µK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring µK and assuming the multiple Gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

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

PURPOSE: Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, D 1 , 2 $$ {D}_{1,2} $$ in mm2  s-1 and a fractional exponent, α $$ \upalpha $$ , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. METHODS: Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between b = 0 $$ b=0 $$ and 5000 s mm-2 . The effects of varying maximum b-value ( b max $$ {b}_{\mathrm{max}} $$ ), number of b-value shells, and the effects of Rician noise were investigated. RESULTS: QDTI measures showed b max $$ {b}_{\mathrm{max}} $$ dependence, most significantly for α $$ \upalpha $$ in white matter, which monotonically decreased with higher b max $$ {b}_{\mathrm{max}} $$ leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of D 1 , 2 $$ \kern0.50em {D}_{1,2} $$ and underestimation of α $$ \upalpha $$ in white matter, and overestimation of D 1 , 2 $$ {D}_{1,2} $$ and α $$ \upalpha $$ anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at b max = 5000 $$ {b}_{\mathrm{max}}=5000 $$  s mm-2 , and 4 b-value shells at b max = 3960 $$ {b}_{\mathrm{max}}=3960 $$  s mm-2 , providing minimal bias in D 1 , 2 $$ {D}_{1,2} $$ and α $$ \upalpha $$ compared to the MbR. CONCLUSION: A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.

Quartile histogram assessment of glioma malignancy using high b-value diffusion MRI with a continuous-time random-walk model.

The purpose of this study is to investigate the feasibility of using a continuous-time random-walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo-planar diffusion-weighted imaging was conducted using 17 b-values (0-4000 s/mm2 ). Three CTRW model parameters, including an anomalous diffusion coefficient Dm , and two parameters related to temporal and spatial diffusion heterogeneity α and ß, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni-Holm-corrected Mann-Whitney U-test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first-quartile mean CTRW parameters yielded significant differences (p-values < 0.05) between pair-wise comparisons of GrII (Dm : 1.14 ± 0.37 µm2 /ms; α: 0.904 ± 0.03, ß: 0.913 ± 0.06), GrIII (Dm : 0.88 ± 0.21 µm2 /ms; α: 0.888 ± 0.01, ß: 0.857 ± 0.06) and GrIV gliomas (Dm : 0.73 ± 0.22 µm2 /ms; α: 0.878 ± 0.01; ß: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area-under-the-curve of using the combinations of the first-quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile-based analysis produced higher accuracy and area-under-the-curve than the full ROI-based analysis in all classifications. The CTRW diffusion model, together with a quartile-based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.

Assessment of chronic allograft injury in renal transplantation using diffusional kurtosis imaging.

BACKGROUND: Chronic allograft injury (CAI) is a significant reason for which many grafts were lost. The study was conducted to assess the usefulness of diffusional kurtosis imaging (DKI) technology in the non-invasive assessment of CAI. METHODS: Between February 2019 and October 2019, 110 renal allograft recipients were included to analyze relevant DKI parameters. According to estimated glomerular filtration rate (eGFR) (mL/min/ 1.73 m2) level, they were divided to 3 groups: group 1, eGFR ≥ 60 (n = 10); group 2, eGFR 30-60 (n = 69); group 3, eGFR < 30 (n = 31). We performed DKI on a clinical 3T magnetic resonance imaging system. We measured the area of interest to determine the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) of the renal cortex and medulla. We performed a Pearson correlation analysis to determine the relationship between eGFR and the DKI parameters. We used the receiver operating characteristic curve to estimate the predicted values of DKI parameters in the CAI evaluation. We randomly selected five patients from group 2 for biopsy to confirm CAI. RESULTS: With the increase of creatinine, ADC, and MD of the cortex and medulla decrease, MK of the cortex and medulla gradually increase. Among the three different eGFR groups, significant differences were found in cortical and medullary MK (P = 0.039, P < 0.001, P < 0.001, respectively). Cortical and medullary ADC and MD are negatively correlated with eGFR (r = - 0.49, - 0.44, - 0.57, - 0.57, respectively; P < 0.001), while cortical and medullary MK are positively correlated with eGFR (r = 0.42, 0.38; P < 0.001). When 0.491 was set as the cutoff value, MK's CAI assessment showed 87% sensitivity and 100% specificity. All five patients randomly selected for biopsy from the second group confirmed glomerulosclerosis and tubular atrophy/interstitial fibrosis. CONCLUSION: The DKI technique is related to eGFR as allograft injury progresses and is expected to become a potential non-invasive method for evaluating CAI.

Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.

To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 â€‹min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 â€‹s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.

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 (tD ) ~ 20 ms and frequency (f) = 70 Hz, respectively. Further, 2D acquisitions were performed at multiple times/frequencies ranging from f = 140 Hz to tD = 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.

Modeling an equivalent b-value in diffusion-weighted steady-state free precession.

PURPOSE: Diffusion-weighted steady-state free precession (DW-SSFP) is shown to provide a means to probe non-Gaussian diffusion through manipulation of the flip angle. A framework is presented to define an effective b-value in DW-SSFP. THEORY: The DW-SSFP signal is a summation of coherence pathways with different b-values. The relative contribution of each pathway is dictated by the flip angle. This leads to an apparent diffusion coefficient (ADC) estimate that depends on the flip angle in non-Gaussian diffusion regimes. By acquiring DW-SSFP data at multiple flip angles and modeling the variation in ADC for a given form of non-Gaussianity, the ADC can be estimated at a well-defined effective b-value. METHODS: A gamma distribution is used to model non-Gaussian diffusion, embedded in the Buxton signal model for DW-SSFP. Monte-Carlo simulations of non-Gaussian diffusion in DW-SSFP and diffusion-weighted spin-echo sequences are used to verify the proposed framework. Dependence of ADC on flip angle in DW-SSFP is verified with experimental measurements in a whole, human postmortem brain. RESULTS: Monte-Carlo simulations reveal excellent agreement between ADCs estimated with diffusion-weighted spin-echo and the proposed framework. Experimental ADC estimates vary as a function of flip angle over the corpus callosum of the postmortem brain, estimating the mean and standard deviation of the gamma distribution as 1.50·10-4  mm2 /s and 2.10·10-4  mm2 /s. CONCLUSION: DW-SSFP can be used to investigate non-Gaussian diffusion by varying the flip angle. By fitting a model of non-Gaussian diffusion, the ADC in DW-SSFP can be estimated at an effective b-value, comparable to more conventional diffusion sequences.

Imaging microstructure with diffusion and susceptibility MR: neuronal density correlation in Disrupted-in-Schizophrenia-1 mutant mice.

PURPOSE: To probe cerebral microstructural abnormalities and assess changes of neuronal density in Disrupted-in-Schizophrenia-1 (DISC1) mice using non-Gaussian diffusion and quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: Brain specimens of transgenic DISC1 mice (n = 8) and control mice (n = 7) were scanned. Metrics of neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI), as well as QSM, were acquired. Cell counting was performed on Nissl-stained sections. Group differences of imaging metrics and cell density were assessed. Pearson correlations between imaging metrics and cell densities were also examined. RESULTS: Significant increases of neuronal density were observed in the hippocampus of DISC1 mice. DKI metrics such as mean kurtosis exhibited significant group differences in the caudate putamen (P = 0.015), cerebral cortex (P = 0.021), and hippocampus (P = 0.011). However, DKI metrics did not correlate with cell density. In contrast, significant positive correlation between density of neurons and the neurite density index of NODDI in the hippocampus was observed (r = 0.783, P = 0.007). Significant correlation between density of neurons and susceptibility (r = 0.657, P = 0.039), as well as between density of neuroglia and susceptibility (r = 0.750, P = 0.013), was also observed in the hippocampus. CONCLUSION: The imaging metrics derived from DKI were not sensitive specifically to cell density, while NODDI could provide diffusion metrics sensitive to density of neurons. The magnetic susceptibility values derived from the QSM method can serve as a sensitive biomarker for quantifying neuronal density.

Diffusion MRI of the breast: Current status and future directions.

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Editorial for "Non-Gaussian Diffusion Models and T1rho Quantification in the Assessment of Hepatic Sinusoidal Obstruction Syndrome in Rats".

LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1122-1123.

The volumetric-tumour histogram-based analysis of intravoxel incoherent motion and non-Gaussian diffusion MRI: association with prognostic factors in HER2-positive breast cancer.

BACKGROUND: To evaluate the imaging biomarkers of human epidermal growth factor receptor 2 (HER2) positive breast cancer in comparison to other molecular subtypes and to determine the feasibility of identifying hormone receptor (HR) status and lymph node metastasis status using volumetric-tumour histogram-based analysis through intravoxel incoherent motion (IVIM) and non-Gaussian diffusion. METHODS: This study included 145 breast cancer patients with 148 lesions between January and November in 2018. Among the 148 lesions, 74 were confirmed to be HER2-positive. The volumetric-tumour histogram-based features were extracted from the combined IVIM and non-Gaussian diffusion model. IVIM and non-Gaussian diffusion parameters obtained from images of the subjects with different molecular prognostic biomarker statuses were compared by Student's t test or the Mann-Whitney U test. The area under the curve (AUC), sensitivity, and specificity at the best cut-off point were reported. The Spearman correlation coefficient was calculated to analyse the correlations of clinical tumor nodule metastasis (TNM) stage and Ki67 with the IVIM and non-Gaussian diffusion parameters. RESULTS: The entropy of mean kurtosis (MK) was significantly higher in the HER2-positive group than in the HER2-negative group (p = 0.015), with an AUC of 0.629 (95% CI 0.546, 0.707), a sensitivity of 62.6%, and a specificity of 66.2%. For HR status, the MD 5th percentile was higher in the HR-positive group of HER2-positive breast cancer (p = 0.041), with an AUC of 0.643 (95% CI 0.523, 0.751), while for lymph node status, the entropy of mean diffusivity (MK) was lower in the lymph node positive group (p = 0.040), with an AUC of 0.587 (95% CI 0.504, 0.668). The clinical TNM stage and Ki67 index were correlated with several histogram parameters. CONCLUSION: Volumetric-lesion histogram analysis of IVIM and the non-Gaussian diffusion model can be used to provide prognostic information about HER2-positive breast cancers and potentially contribute to individualized anti-HER2 targeted therapy plans .

Non-gaussian diffusion evaluation of the human kidney by Padé exponent model.

PURPOSE: To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects. MATERIALS AND METHODS: Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory-triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b-values [0-750 s/mm2 ]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R2 -test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion-weighted imaging signal decay. RESULTS: The mathematical accuracy of the PEM was significantly higher than for the other three-parameter and the monoexponential model (P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar Rbi2 0.989 ± 0.008, AICcbi 113.3 ± 6.6; cortical Rbi2 0.992 ± 0.006, AICcbi 113.3 ± 5.2) than the three-parameter models (medullar RPadé2 0.965 ± 0.016, AICcPadé 122.6 ± 6.4, RK2 0.954 ± 0.019, AICcK 128.5 ± 6.0; cortical RPadé2 0.989 ± 0.005, AICcPadé 116.3 ± 4.4, RK2 0.985 ± 0.007, AICcK 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar Rmono2 0.898 ± 0.039, AICcmono 141.4 ± 5.6; cortical Rmono2 0.961 ± 0.013, AICcmono 135.4 ± 4.8). CONCLUSION: The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:160-167.

Diffusion kurtosis metrics as biomarkers of microstructural development: A comparative study of a group of children and a group of adults.

The most common modality of diffusion MRI used in the ageing and development studies is diffusion tensor imaging (DTI) providing two key measures, fractional anisotropy and mean diffusivity. Here, we investigated diffusional changes occurring between childhood (average age 10.3 years) and mitddle adult age (average age 54.3 years) with the help of diffusion kurtosis imaging (DKI), a recent novel extension of DTI that provides additional metrics quantifying non-Gaussianity of water diffusion in brain tissue. We performed voxelwise statistical between-group comparison of diffusion tensor and kurtosis tensor metrics using two methods, namely, the tract-based spatial statistics (TBSS) and the atlas-based regional data analysis. For the latter, fractional anisotropy, mean diffusivity, mean diffusion kurtosis, and other scalar diffusion tensor and kurtosis tensor parameters were evaluated for white matter fibres provided by the Johns-Hopkins-University Atlas in the FSL toolkit (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases). Within the same age group, all evaluated parameters varied depending on the anatomical region. TBSS analysis showed that changes in kurtosis tensor parameters beyond adolescence are more widespread along the skeleton in comparison to the changes of the diffusion tensor metrics. The regional data analysis demonstrated considerably larger between-group changes of the diffusion kurtosis metrics than of diffusion tensor metrics in all investigated regions. The effect size of the parametric changes between childhood and middle adulthood was quantified using Cohen's d. We used Cohen's d related to mean diffusion kurtosis to examine heterogeneous maturation of various fibres. The largest changes of this parameter (interpreted as reflecting the lowest level of maturation by the age of children group) were observed in the association fibres, cingulum (gyrus) and cingulum (hippocampus) followed by superior longitudinal fasciculus and inferior longitudinal fasciculus. The smallest changes were observed in the commissural fibres, forceps major and forceps minor. In conclusion, our data suggest that DKI is sensitive to developmental changes in local microstructure and environment, and is particularly powerful to unravel developmental differences in major association fibres, such as the cingulum and superior longitudinal fasciculus.

The effect of noise and lipid signals on determination of Gaussian and non-Gaussian diffusion parameters in skeletal muscle.

This work characterizes the effect of lipid and noise signals on muscle diffusion parameter estimation in several conventional and non-Gaussian models, the ultimate objectives being to characterize popular fat suppression approaches for human muscle diffusion studies, to provide simulations to inform experimental work and to report normative non-Gaussian parameter values. The models investigated in this work were the Gaussian monoexponential and intravoxel incoherent motion (IVIM) models, and the non-Gaussian kurtosis and stretched exponential models. These were evaluated via simulations, and in vitro and in vivo experiments. Simulations were performed using literature input values, modeling fat contamination as an additive baseline to data, whereas phantom studies used a phantom containing aliphatic and olefinic fats and muscle-like gel. Human imaging was performed in the hamstring muscles of 10 volunteers. Diffusion-weighted imaging was applied with spectral attenuated inversion recovery (SPAIR), slice-select gradient reversal and water-specific excitation fat suppression, alone and in combination. Measurement bias (accuracy) and dispersion (precision) were evaluated, together with intra- and inter-scan repeatability. Simulations indicated that noise in magnitude images resulted in <6% bias in diffusion coefficients and non-Gaussian parameters (α, K), whereas baseline fitting minimized fat bias for all models, except IVIM. In vivo, popular SPAIR fat suppression proved inadequate for accurate parameter estimation, producing non-physiological parameter estimates without baseline fitting and large biases when it was used. Combining all three fat suppression techniques and fitting data with a baseline offset gave the best results of all the methods studied for both Gaussian diffusion and, overall, for non-Gaussian diffusion. It produced consistent parameter estimates for all models, except IVIM, and highlighted non-Gaussian behavior perpendicular to muscle fibers (α ~ 0.95, K ~ 3.1). These results show that effective fat suppression is crucial for accurate measurement of non-Gaussian diffusion parameters, and will be an essential component of quantitative studies of human muscle quality.

Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values.

PURPOSE: To demonstrate that a continuous-time random-walk (CTRW) diffusion model can improve diagnostic accuracy of differentiating low- and high-grade pediatric brain tumors. METHODS: Fifty-four children with histopathologically confirmed brain tumors underwent diffusion MRI scans at 3Twith 12 b-values (0-4000 s/mm(2) ). The diffusion imageswere fit to a simplified CTRW model to extract anomalous diffusion coefficient, Dm , and temporal and spatial heterogeneity parameters, α and ß, respectively. Using histopathology results as reference, a k-means clustering algorithm and a receiver operating characteristic (ROC) analysis were employed to determine the sensitivity, specificity, and diagnostic accuracy of the CTRW parameters in differentiating tumor grades. RESULTS: Significant differences between the low- and high-grade tumors were observed in the CTRW parameters (p-values<0.001). The k-means analysis showed that the combination of three CTRW parameters produced higher diagnostic accuracy (85% vs. 75%) and specificity (83% vs. 54%) than the apparent diffusion coefficient (ADC) from a mono-exponential model. The ROC analysis revealed that any combination of the CTRW parameters gave a larger area under the curve (0.90-0.96) than using ADC (0.80). CONCLUSION: With its sensitivity to intravoxel heterogeneity, the simplified CTRW model is useful for non-invasive grading of pediatric brain tumors, particularly when surgical biopsy is not feasible. Magn Reson Med 76:1149-1157, 2016. © 2015 Wiley Periodicals, Inc.

Non-Gaussian diffusion MR imaging of glioma: comparisons of multiple diffusion parameters and correlation with histologic grade and MIB-1 (Ki-67 labeling) index.

INTRODUCTION: This study was conducted to compare the association of Gaussian and non-Gaussian magnetic resonance imaging (MRI)-derived parameters with histologic grade and MIB-1 (Ki-67 labeling) index (MI) in brain glioma. METHODS: Sixty-five patients with pathologically confirmed glioma, who underwent diffusion-weighted MRI with 2 b values (0, 1000 s/mm(2)) and 22 b values (≤5000 s/mm(2)), respectively, were divided into three groups of grade II (n = 35), grade III (n = 8), and grade IV (n = 22). Comparisons by two groups were made for apparent diffusion coefficient (ADC), slow diffusion coefficient (Dslow), distributed diffusion coefficient (DDC), and heterogeneity index α. Analyses of receiver operating characteristic (ROC) curve were performed to maximize the area under the curve (AUC) for differentiating grade III + IV (high-grade glioma, HGG) from grade II (low-grade glioma, LGG) and grade IV (glioblastoma multiforme, GBM) from grade II + III (other grade glioma, OGG). Correlations with MI were analyzed for the MRI parameters. RESULTS: On tumor regions, the values of ADC, Dslow, DDC, and α were significantly higher in grade II [(1.37 ± 0.29, 0.70 ± 0.11, 1.39 ± 0.34) (×10(-3) mm(2)/s) and 0.88 ± 0.05, respectively] than in grade III [(0.99 ± 0.13, 0.55 ± 0.07, 1.04 ± 0.20) (×10(-3) mm(2)/s) and 0.80 ± 0.03, respectively] and grade IV [(1.03 ± 0.14, 0.50 ± 0.05, 1.02 ± 0.16) (×10(-3) mm(2)/s) and 0.76 ± 0.04, respectively] (all P < 0.001). The parameter α showed the highest AUCs of 0.950 and 0.922 in discriminating HGG from LGG and GBM from OGG, respectively. Significant correlations with histologic grade and MI were observed for the MRI parameters. CONCLUSION: The non-Gaussian MRI-derived parameters α and Dslow are superior to ADC in glioma grading, which are comparable with ADC as reliable biomarkers in noninvasively predicting the proliferation level of glioma malignancy.

Double-pulsed diffusional kurtosis imaging for the in vivo assessment of human brain microstructure.

We have recently extended conventional single-pulsed-field-gradient (s-PFG) diffusional kurtosis imaging (DKI) to double-pulsed-field-gradient (d-PFG) diffusion MRI sequences, with a method known as double-pulsed DKI (DP-DKI). By virtue of a six-dimensional (6D) formulation for q-space, many of the results and insights of s-PFG DKI are generalized to those of DP-DKI. Owing to the fact that DP-DKI isolates the second order contributions to the d-PFG signal (i.e. second order in b-value), the 6D diffusional kurtosis encodes information beyond what is available from s-PFG sequences. Previously, we have demonstrated DP-DKI for in vivo mouse brain at 7 T, and it is the objective of this study to demonstrate the feasibility of DP-DKI at 3 T for the in vivo assessment of human brain microstructure. In addition, an example is given of how to utilize the additional information obtained from DP-DKI for the purpose of biophysical modeling. The relationship between a specific microscopic anisotropy metric estimated from DP-DKI and other recently proposed measures is also discussed.