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Abstract Background Diffusion kurtosis imaging is an advanced magnetic resonance imaging technique that reveals additional information on the microstructure and micro-dynamics of different brain masses without the need for contrast agents. The aim of this study was to provide a comprehensive analysis of the role of MRI diffusion kurtosis and to compare it with magnetic resonance spectroscopy (MRS) and dynamic susceptibility contrast perfusion (DSC) in characterizing different brain masses, including gliomas, recurrent tumors, radiation necrosis, abscesses, and infarctions. Sixty-six patients with intracranial brain masses were enrolled in this prospective study. All patients were examined by conventional MRI sequences, DSC perfusion, MRS, and diffusion kurtosis imaging, with implemented b values which were 200, 500, 1000, 1500 and 2000s/mm2. Results Mean kurtosis (MK) was higher (P

Background: Diffusion kurtosis imaging is an advanced magnetic resonance imaging technique that reveals additional information on the microstructure and micro-dynamics of different brain masses without the need for contrast agents. The aim of this study was to provide a comprehensive analysis of the role of MRI diffusion kurtosis and to compare it with magnetic resonance spectroscopy (MRS) and dynamic susceptibility contrast perfusion (DSC) in characterizing different brain masses, including gliomas, recurrent tumors, radiation necrosis, abscesses, and infarctions. Sixty-six patients with intracranial brain masses were enrolled in this prospective study. All patients were examined by conventional MRI sequences, DSC perfusion, MRS, and diffusion kurtosis imaging, with implemented b values which were 200, 500, 1000, 1500 and 2000s/mm2. Results: Mean kurtosis (MK) was higher (P < 0.001) in recurrent brain tumors than in radiation-induced necrosis; the optimal MK cutoff value for differentiation between them was 642 with 91.3% sensitivity and 85.7% specificity. Mean kurtosis was also higher (P < 0.001) in high-grade gliomas than in low-grade gliomas; the optimal MK cutoff value for differentiation between them was 639 with 91.6% sensitivity and 85.71% specificity. There was a good level of agreement between ADC and MD within the studied cases, with a correlation coefficient r = 0.815. MK had more sensitivity and specificity in differentiation between high- and low-grade gliomas, as well as RIN and tumoral recurrence, than MRS and DSC. Conclusions: Diffusion kurtosis imaging stands as an integral, noninvasive, and noncontrast tool for the characterization of various brain masses. It augments the capabilities of traditional and advanced MRI techniques, providing a deeper understanding of the microstructural changes in brain tissues. [ABSTRACT FROM AUTHOR]

PURPOSE: Structure-guided total variation is a recently introduced prior that allows reconstruction of images using knowledge of the location and orientation of edges in a reference image. In this work, we demonstrate the advantages of a variant of structure-guided total variation known as directional total variation (DTV), over traditional total variation (TV), in the context of compressed-sensing reconstruction and super-resolution. METHODS: We compared TV and DTV in retrospectively undersampled ex vivo diffusion tensor imaging and diffusion spectrum imaging data from healthy, sham, and hypertrophic rat hearts. RESULTS: In compressed sensing at an undersampling factor of 8, the RMS error of mean diffusivity and fractional anisotropy relative to the fully sampled ground truth were 44% and 20% lower in DTV compared with TV. In super-resolution, these values were 29% and 14%, respectively. Similarly, we observed improvements in helix angle, transverse angle, sheetlet elevation, and sheetlet azimuth. The RMS error of the diffusion kurtosis in the undersampled data relative to the ground truth was uniformly lower (22% on average) with DTV compared to TV. CONCLUSION: Acquiring one fully sampled non-diffusion-weighted image and 10 diffusion-weighted images at 8× undersampling would result in an 80% net reduction in data needed. We demonstrate efficacy of the DTV algorithm over TV in reducing data sampling requirements, which can be translated into higher apparent resolution and potentially shorter scan times. This method would be equally applicable in diffusion MRI applications outside the heart.

The brain has a unique macroscopic waste clearance system, termed the glymphatic system which utilises perivascular tunnels surrounded by astroglia to promote cerebrospinal-interstitial fluid exchange. Rodent studies have demonstrated a marked increase in glymphatic clearance during sleep which has been linked to a sleep-induced expansion of the extracellular space and concomitant reduction in intracellular volume. However, despite being implicated in the pathophysiology of multiple human neurodegenerative disorders, non-invasive techniques for imaging glymphatic clearance in humans are currently limited.Here we acquired multi-shell diffusion weighted MRI (dwMRI) in twenty-one healthy young participants (6 female, 22.3 ± 3.2 years) each scanned twice, once during wakefulness and once during sleep induced by a combination of one night of sleep deprivation and 10 mg of the hypnotic zolpidem 30 min before scanning. To capture hypothesised sleep-associated changes in intra/extracellular space, dwMRI were analysed using higher order diffusion modelling with the prediction that sleep-associated increases in interstitial (extracellular) fluid volume would result in a decrease in diffusion kurtosis, particularly in areas associated with slow wave generation at the onset of sleep.In line with our hypothesis, we observed a global reduction in diffusion kurtosis (t15=2.82, p = 0.006) during sleep as well as regional reductions in brain areas associated with slow wave generation during early sleep and default mode network areas that are highly metabolically active during wakefulness. Analysis with a higher-order representation of diffusion (MAP-MRI) further indicated that changes within the intra/extracellular domain rather than membrane permeability likely underpin the observed sleep-associated decrease in kurtosis. These findings identify higher-order modelling of dwMRI as a potential new non-invasive method for imaging glymphatic clearance and extend rodent findings to suggest that sleep is also associated with an increase in interstitial fluid volume in humans.

Background: Several functional imaging techniques, including monoexponential diffusion‐weighted imaging (m‐DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis (DK) imaging, have been used in differentiating benign and malignant musculoskeletal tumors. Combining all three techniques in the same study population may improve differentiation. Purpose To compare the diagnostic performance of m‐DWI, IVIM, and DK models and their combinations in differentiating benign and malignant musculoskeletal tumors. Study Type: Prospective. Population: Fifty patients with benign and malignant musculoskeletal tumors divided into nonmyxoid and nonchondroid and myxoid and/or chondroid subgroups. Field strength/Sequence: A 1.5 T/m‐DWI, IVIM, and DK single‐shot spin‐echo echo‐planar sequences. Assessment Minimum and volumetric values of apparent diffusion coefficient (ADC), pure molecular diffusion (Divim), pseudodiffusion (D*), perfusion fraction (f), diffusion coefficient for kurtosis model (DK), and Kurtosis (K) were compared between all benign and malignant tumors. Subgroup analysis was also performed for nonmyxoid and nonchondroid and myxoid and/or chondroid tumors. Statistical tests: Independent samples t‐test, Mann–Whitney U test, intraclass correlation coefficient, ROC analysis, and logistic regression analysis. A P value < 0.05 was considered statistically significant. Results: ADCmin, Divim‐min, D*vol, DK‐min, Kvol, and Kmin values showed statistically significant differences between all benign and malignant tumors and nonmyxoid and nonchondroid tumor subgroup. Kmin showed the highest diagnostic performance in differentiating benign and malignant tumors with AUCs of 0.760 for "all tumors" and 0.825 for the nonmyxoid and nonchondroid tumor subgroup. No significant differences were detected in m‐DWI‐, IVIM‐, and DK‐derived parameters for differentiating benign and malignant myxoid and/or chondroid tumors. Only three of 63 combinations of prediction models demonstrated a higher diagnostic performance than Kmin; however, improvements were not significantly different. Data conclusion: ADCmin, Divim‐min, D*vol, DK‐min, Kvol, and Kmin values can be used to differentiate benign and malignant musculoskeletal tumors. Our findings suggest that the added value of multiparametric approach in such differentiation is not significant. Evidence Level 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Since its first description and development in the late 20th century, diffusion magnetic resonance imaging (dMRI) has proven useful in describing the microstructural details of biological tissues. Signal generated from the protons of water molecules undergoing Brownian motion produces contrast based on the varied diffusivity of tissue types. Images employing diffusion contrast were first used to describe the diffusion characteristics of tissues, later used to describe the fiber orientations of white matter through tractography, and most recently proposed as a functional contrast method capable of delineating neuronal firing in the active brain. Thanks to the molecular origins of its signal source, diffusion contrast is inherently useful at describing features of the microenvironment; however, limitations in achievable resolution in magnetic resonance imaging (MRI) scans precluded direct visualization of tissue microstructure for decades following MRI’s inception as an imaging modality. Even after advancements in MRI hardware had permitted the visualization of mammalian cells, these specialized systems could only accommodate fixed specimens that prohibited the observation and characterization of physiological processes. The goal of the current study was to visualize cellular structure and investigate the subcellular origins of the functional diffusion contrast mechanism (DfMRI) in living, mammalian tissue explants. Using a combination of ultra-high field spectrometers, micro radio frequency (RF) coils, and an MRI-compatible superfusion device, we are able to report the first live, mammalian cells—α-motor neurons—visualized with magnetic resonance microscopy (MRM). We are also able to report changes in the apparent diffusion of the stratum oriens within the hippocampus—a layer comprised primarily of pyramidal cell axons and basal dendrites—and the spinal cord’s ventral horn following exposure to kainate.

This commentary serves as an introduction to the magnetic resonance imaging (MRI) technique called diffusion kurtosis imaging (DKI) employed in the study by Arab et al. in the present issue of Neurotoxicology Research. In their study, DKI is employed for longitudinal investigation of a methamphetamine intoxication model of Parkinson's disease. The study employs an impressive number of animals and combines DKI with behavioral analysis at multiple time points. The commentary discusses some aspects of the study design especially the strength of combining behavioral analysis with MRI in an effort to provide as thorough a characterization and validity assessment of the animal model and cohort as possible. The potential clinical value of combining multiple MRI techniques (multimodal MRI) in PD is discussed as well as the benefit of multimodal MRI combined with behavioral analysis and subsequent histological analysis for in-depth characterization of animal models. [ABSTRACT FROM AUTHOR]

To evaluate the feasibility of a multimodal approach involving dynamic contrast-enhanced (DCE) perfusion imaging and diffusion kurtosis imaging (DKI) in the preoperative imaging of brain tumors in a multicenter setting, and to evaluate the effect on diagnostic confidence and accuracy for tumor grade and type prediction. One hundred and thirty-three patients with brain tumors were imaged in six hospitals with a standardized multimodal protocol. Standard imaging and six parameter maps derived from DCE and DKI sequences were reviewed off-site by two independent readers. Image quality and diagnostic confidence were evaluated in qualitative analyses. Quantitative analyses were performed to assess diagnostic accuracy and the performance of DKI and DCE parameters for tumor grade differentiation and molecular tumor type determination. Standardized acquisition of DCE and DKI maps was feasible with excellent image quality. Diagnostic confidence was significantly improved from 85 % to 96 % (p = 0.0005) by additional review of the DCE and DKI maps. The combination of mean kurtosis and CBV was particularly advantageous for differentiating low-grade and high-grade glioma, oligodendroglial vs. astrocytic, and IDH1/2 wild type vs. mutated tumors. A multimodal imaging approach with DCE and DKI improves diagnostic confidence and yields higher diagnostic accuracy for predicting tumor grade and type in adult-type glioma. [ABSTRACT FROM AUTHOR]

Objectives: In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects., Methods: Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T 1 -weghted (T 1 w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T 1 w of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter., Results: The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, R 2 = 0.93; FA-RK, R 2 = 0.89) and a strong negative correlation (FA-AK, R 2 = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA., Conclusions: WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures., Advances in Knowledge: Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images., Competing Interests: M.H is supported by the research funding from FUJIFILM Healthcare Corporation. Y.T., K.I., and Y.B. are employees of FUJIFILM Healthcare Corporation; the other authors declare that they have no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Purpose: To evaluate the role of diffusion kurtosis and diffusivity as potential imaging biomarkers to predict response to neoadjuvant chemoradiation therapy (CRT) from baseline staging magnetic resonance imaging (MRI) in locally advanced rectal cancer (LARC). Materials and methods: This retrospective study included 45 consecutive patients (31 male/14 female) who underwent baseline MRI with high b-value sequences (up to 1500 mm/s2) for LARC followed by neoadjuvant chemoradiation and surgical resection. The mean age was 57.4 years (range 34.2–72.9). An abdominal radiologist using open source software manually segmented T2-weighted images. Segmentations were used to derive diffusion kurtosis and diffusivity from diffusion-weighted images as well as volumetric data. These data were analyzed with regard to tumor regression grade (TRG) using the four-tier American Joint Committee on Cancer (AJCC) classification, TRG 0–3. Proportional odds regression was used to analyze the four-level ordinal outcome. A sensitivity analysis was performed using univariable logistic regression for binary TRG groups, TRG 0/1 (> 90% response), or TRG 2/3 (< 90% response). p < 0.05 was considered significant throughout. Results: In the univariable proportional odds regression analysis, higher diffusivity summary (Dsum) values were observed to be significantly associated with higher odds of being in one or more favorable TRG group (TRG 0 or 1). In other words, on average, patients with higher Dsum values were more likely to be in a more favorable TRG group. These results are mostly consistent with the sensitivity analysis, in which higher values for most Dsum values [all but region of interest (ROI)-max D median (p = 0.08)] were observed to be significantly associated with higher odds of being TRG 0 or 1. Tumor volume of interest (VOI) and ROI volume, ROI kurtosis mean and median, and VOI kurtosis mean and median were not significantly associated with TRG. Conclusion: Diffusivity derived from the baseline staging MRI, but not diffusion kurtosis or volumetric data, is associated with TRG and therefore shows promise as a potential imaging biomarker to predict the response to neoadjuvant chemotherapy in LARC. Clinical relevance statement: Diffusivity shows promise as a potential imaging biomarker to predict AJCC TRG following neoadjuvant CRT, which has implications for risk stratification. Patients with TRG 0/1 have 5-year disease-free survival (DFS) of 90–98%, as opposed to those who are TRG 2/3 with 5-year DFS of 68–73%. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively assess the feasibility of diffusional kurtosis (DK) imaging for distinguishing prostate cancer(PCa) from benign prostate hyperplasia (BPH) in comparison with standard diffusion-weighted (DW) imaging, as well as low-from high-grade malignant regions.Materials and Methods: 147 consecutive patients with suspected PCa underwent multi-parametric 1.5-TMR. Diffusion kurtosis imaging was acquired with with 5 b values (0,600,800,1600,and 2400sec/mm2).Region of interest (ROI)-based measurements were performed on ADC, D, and K map by two radiologists. Data were analyzed by using mixed-model analysis of variance and receiver operating characteristic curves. Correlations among the three parameters (ADC,D and K) in all patients, and correlations between three parameters with the tumor Gleason score (GS) in PCa group were analyzed using Pearson's correlation coefficient in peripheral zone(PZ) and transiton zone(TZ).Results: 58 patients were proved with PCa (9 GS 3 + 3[PZ/TZ = 4/5], 49 GS ≥ 7 [PZ/TZ = 26/23]), and 89 patients were with BPH. ADC,D and K were able to distinguish benignance from tumor tissue both in PZ and TZ(P＜0.01), but performed poorly in neither differentiating low-(GS 3 + 3) from high-grade (GS≥3 + 4) disease, nor GS(3 + 4) from GS(4 + 3).There was a weak correlation between the GS and ADC, D (PZ:ADC r=-0.113, D r=-0.139; TZ:ADC r=-0.104,D r=-0.103), while a moderate correlation between the GS and K(PZ:K r = 0.492; TZ:K r = 0.433, P＜0.01).K had significantly greater area under the curve for differentiating PCa from BHP than ADC both in PZ and TZ.Conclusion: DK model may add value in PCa detection and diagnosis, but none can differentiate low-from high-grade PCas (including GS=3+4 from GS=4+3). [ABSTRACT FROM AUTHOR]

Objectives: To explore the feasibility of diffusion kurtosis imaging (DKI) for evaluating inflammatory activity in Crohn's disease (CD). Materials and methods: In all, 51 CD patients were included, who were performed with consecutive enteroscopy, MR and DKI (b values = 0–2000 mm2/s). The lesions of bowel segments were graded as inactive (0–2), mild (3–6), and moderate–severe group (> 6) based on simplified endoscopic activity score for Crohn's disease (SES-CD), The abilities of the parameters of DKI and DWI in grading different activity lesions were compared. Results: One hundred and twenty-seven bowel segments including inactive (15), mild (45) and moderate–severe (67) were analyzed. ADC (r = − 0.627, p < 0.001), Dapp (r = − 0.381, p < 0.001) and Kapp (r = 0.641, p < 0.001) were correlated with SES-CD. These parameters were significantly different among the three groups (all p < 0.001). ROC analysis found ADC had the highest accuracy (AUC = 0.884, p < 0.001) to differentiate inactive from active group with the threshold at 0.865 × 10−3 mm2/s, which was slightly higher than Kapp (AUC = 0.867, p < 0.001) with the threshold at 0.645, and was obviously higher than Dapp (AUC = 0.726, p = 0.005). Similarly, ADC also had the highest accuracy (AUC = 0.846, p < 0.001) to differentiate inactive–mild from moderate–severe group with the threshold at 0.825 × 10−3 mm2/s, and minimally higher than Kapp (AUC = 0.843, p < 0.001) with the threshold at 0.695, and obviously higher than Dapp (AUC = 0.690, p < 0.001). Conclusion: DKI is feasible and comparable to conventional DWI for the evaluation of inflammatory activity in CD. [ABSTRACT FROM AUTHOR]

Purpose: Microscopic fractional anisotropy (µFA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate µFA, it has only recently been proposed that powder‐averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for µFA estimation. This study aims to evaluate µFA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder‐averaged SM signals. Methods: SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Δ/δ = 12/1.5 ms). The µFA maps obtained from powder‐averaged SDE signals were then compared to maps obtained from DDE‐MRI experiments (Δ/τ/δ = 12/12/1.5 ms), which allow a model‐free estimation of µFA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. Results: µFA, as well as other estimates derived from powder‐averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). Conclusion: Powder‐averaged SMT and (2‐component) SM are unable to accurately report µFA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research. [ABSTRACT FROM AUTHOR]

Background: Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.Purpose: To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (1 H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.Study Type: Prospective.Subjects: Seventy-one patients with breast lesions (50 malignant, 26 benign).Sequence: DKI, DWI, DCE-MRI, and 1 H-MRS were performed at 3.0T.Assessment: Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.Statistical Tests: Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.Results: MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.Data Conclusion: MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856. [ABSTRACT FROM AUTHOR]

Background Although several studies have been reported on evaluating the performance of Gaussian and different non-Gaussian diffusion models on prostate cancer, few studies have been reported on the comparison of different models on differential diagnosis for prostate cancer. Purpose To compare the utility of various metrics derived from monoexponential model (MEM), biexponential model (BEM), stretched-exponential model (SEM) based diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differential diagnosis of prostate cancer. Material and Methods Thirty-three patients underwent magnetic resonance imaging (MRI) examination. Multi-b value and multi-direction DWIs were performed. In-bore MR-guided biopsy was performed. Apparent diffusion coefficient (ADC), pure molecular diffusion (ADCslow), pseudo-diffusion coefficient (ADCfast), perfusion fraction (f), water molecular diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), non-Gaussian diffusion coefficient (MD), and mean kurtosis (MK) values were calculated and compared between cancerous and non-cancerous groups. Receiver operating characteristic (ROC) analysis was performed for all parameters and models. Results ADC, ADCslow, DDC, and MD values were significantly lower while MK value was significantly higher in prostate cancer than those of prostatitis and benign prostatic hyperplasia. ADC, ADCslow, DDC, MD, and MK could discriminate between tumor and non-tumorous lesions (area under the curve, 0.856, 0.835, 0.866, 0.918, and 0.937, respectively). MK was superior to ADC in the discrimination of prostate cancer. DKI was superior to MEM in the discrimination of prostate cancer. Conclusions Parameters derived from both Gaussian and non-Gaussian models could characterize prostate cancer. DKI may be advantageous than DWI for detection of prostate cancer. [ABSTRACT FROM AUTHOR]

Background: Children with fetal alcohol spectrum disorders (FASD) often have deficits associated with multisensory processing. Because ethanol (EtOH) disrupts activity‐dependent neuronal plasticity, a process that is essential for refining connections during cortical development, we hypothesize that early alcohol exposure results in alterations in multisensory cortical networks, which could explain the multisensory processing deficits seen in FASD. Here, we use a gyrencephalic animal model to test the prediction that early alcohol exposure alters the functional connectivity and microstructural features of the rostral posterior parietal cortex (PPr), a visual‐tactile integrative area. Methods: Ferrets were exposed to moderate doses of EtOH during the brain growth spurt period. Functional connectivity and microstructural features were assessed using resting‐state functional magnetic resonance imaging and ex vivo diffusion kurtosis imaging (DKI), respectively, when the animals reached juvenile age and adulthood, respectively. Results: While the whole brain volume was smaller in alcohol‐treated animals, the relative size of the frontal brain area was larger when compared to control animals. Altered functional connectivity was observed in alcohol‐treated animals, where increased connectivity was observed between PPr and the region that provides its major visual inputs (the caudal portion of the parietal cortex), but not with the region that provides its major somatosensory inputs (tertiary somatosensory cortex). DKI revealed reduced microstructural tissue complexity in all investigated sensory areas of alcohol‐treated animals. Conclusions: In this study, we observed alterations in cortical functional connectivity and microstructural integrity in a cortical area involved in multisensory processing in a ferret FASD model. These findings indicate an alteration in cortical networks that may be related to the multisensory processing deficiencies observed in FASD. [ABSTRACT FROM AUTHOR]

• Diffusion weighted MRI images were acquired in awake and sleeping participants. • Sleep was associated with a significant decrease in diffusion kurtosis. • Decrease in kurtosis was observed in default mode network areas. • Higher-order diffusion representation supports changes in intra/extracellular space. • Observed changes in diffusion kurtosis are in line with the glymphatic hypothesis. The brain has a unique macroscopic waste clearance system, termed the glymphatic system which utilises perivascular tunnels surrounded by astroglia to promote cerebrospinal-interstitial fluid exchange. Rodent studies have demonstrated a marked increase in glymphatic clearance during sleep which has been linked to a sleep-induced expansion of the extracellular space and concomitant reduction in intracellular volume. However, despite being implicated in the pathophysiology of multiple human neurodegenerative disorders, non-invasive techniques for imaging glymphatic clearance in humans are currently limited. Here we acquired multi-shell diffusion weighted MRI (dwMRI) in twenty-one healthy young participants (6 female, 22.3 ± 3.2 years) each scanned twice, once during wakefulness and once during sleep induced by a combination of one night of sleep deprivation and 10 mg of the hypnotic zolpidem 30 min before scanning. To capture hypothesised sleep-associated changes in intra/extracellular space, dwMRI were analysed using higher order diffusion modelling with the prediction that sleep-associated increases in interstitial (extracellular) fluid volume would result in a decrease in diffusion kurtosis, particularly in areas associated with slow wave generation at the onset of sleep. In line with our hypothesis, we observed a global reduction in diffusion kurtosis (t 15 =2.82, p = 0.006) during sleep as well as regional reductions in brain areas associated with slow wave generation during early sleep and default mode network areas that are highly metabolically active during wakefulness. Analysis with a higher-order representation of diffusion (MAP-MRI) further indicated that changes within the intra/extracellular domain rather than membrane permeability likely underpin the observed sleep-associated decrease in kurtosis. These findings identify higher-order modelling of dwMRI as a potential new non-invasive method for imaging glymphatic clearance and extend rodent findings to suggest that sleep is also associated with an increase in interstitial fluid volume in humans. [ABSTRACT FROM AUTHOR]

The aim of this study is to present and evaluate a multiparametric and multi-modality imaging protocol applied to brain tumours and investigate correlations between these different imaging measures. In particular, we describe a method for rapid, non-invasive, quantitative imaging of water content of brain tissue, based on a single multiple-echo gradient-echo (mGRE) acquisition. We include in the processing a method for noise reduction of the multi-contrast data based on Principal Component Analysis (PCA). Noise reduction is a key ingredient to obtaining high-precision water content and transverse relaxation T 2 ∗ values. The quantitative method is applied to brain tumour patients in a hybrid MR-PET environment. Active tumour tissue is identified by means of FET-PET; oedema, white and grey-matter segmentation is performed based on MRI contrasts. Water content information is not only relevant by itself, but also as a basis for correlations with other quantitative measures of water behaviour in tissue and interpreting the microenvironment of water. Water content in active tumour tissue (84%) and oedema (79%) regions is found to be higher than that of normal WM (69%) and close to that of normal GM (83%). Consistent with literature reports, mean kurtosis is measured to be lower in tumour and oedema regions than in normal WM and GM, whereas mean diffusivity is increased. Voxel-based correlations between water content and diffusion indices obtained with diffusion kurtosis tensor imaging, and between quantitative MRI and FET-PET are reported for 8 brain tumour patients. The effective transverse relaxation time T 2 ∗ is found to be the MR parameter showing the strongest correlations with other MR indices derived here and with FET-PET. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether there is imaging evidence of hypothalamic injury in patients with mild traumatic brain injury (MTBI), which is a major public health problem due to the high prevalence and difficulty in diagnosis and treatment.Materials and Methods: Twenty-four patients (mean age 34.2, range, 18-56 years) with symptomatic MTBI and 22 age-matched healthy controls (mean age 37.0, range 20-61 years) participated in the study. Diffusion kurtosis imaging was performed with diffusion-weighted images acquired along 30 gradient directions and three b-values (b = 0, 1000, 2000 s/mm2 ) based on a twice-refocused spin-echo sequence with a 3T magnetic resonance imaging (MRI) scanner. Resting-state functional (f)MRI with standard echo planar imaging (EPI) were performed to localize the resting-state networks (RSN) and hypothalamic functional connectivity.Results: There were significantly reduced mean kurtosis (P = 0.0092) and radial kurtosis (P = 0.0078) in patients as compared to controls in the hypothalamus. Furthermore, there was a significant negative correlation (r = -0.675, P = 0.0007) between radial kurtosis in the hypothalamus and fatigue severity scale in patients. The MTBI group also showed disrupted hypothalamic RSNs, with significantly decreased positive connectivity in medial prefrontal cortex, inferior posterior parietal, and cingulate regions but increased connectivity in the peri-hypothalamic regions and cerebellum, together with significantly decreased negative RSNs in visual and bilateral premotor areas (cluster corrected P < 0.05).Conclusion: Our results show disruption of functional and structural hypothalamic connectivity in patients with MTBI, and might further the understanding of an array of clinical symptoms in MTBI such as sleep disturbance and fatigue.Level Of Evidence: 2 J. Magn. Reson. Imaging 2017;45:1105-1112. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the reliability of 3D isotropic MRI and quantitative multi-parametric MRI characterization on an orthotopic pancreatic head tumor model in rats. 3D isotropic T2-weighted MRI was performed as a routine for tumor longitudinal follow-up and volume estimation. Common bile duct diameter was measured from 3D multiplanar reconstruction. Quantitative multi-parametric measurements including pixel-wise T2, T1 relaxivity, apparent diffusion coefficient (ADC) and apparent diffusion kurtosis mapping were performed twice throughout tumor growth. Semi-quantitative and quantitative analyses based on an extended Tofts model were applied to region-of-interest-based dynamic contrast-enhanced imaging, followed by contrast ratio measurement on standard contrast-enhanced imaging. Moreover, low-level texture-based analysis was inspected for T2, T1, ADC and contrast ratio measurements. Results indicated that multi-parametric MRI showed good reproducibility for tumor characterization; the measurements were not affected by tumor growth. Tumor growth was further confirmed with histology examinations. To conclude, state-of-the-art clinical MRI techniques were translated to this preclinical tumor model with high reliability, and have paved the way for translational oncology studies on this tumor model. [ABSTRACT FROM AUTHOR]

Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g. intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2-3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e. produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.

Purpose: An increasing number of studies have aimed to compare diffusion tensor imaging (DTI)-related parameters [e.g., mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD)] to complementary new indexes [e.g., mean kurtosis (MK)/radial kurtosis (RK)/axial kurtosis (AK)] derived through diffusion kurtosis imaging (DKI) in terms of their discriminative potential about tissue disease-related microstructural alterations. Given that the DTI and DKI models provide conceptually and quantitatively different estimates of the diffusion tensor, which can also depend on fitting routine, the aim of this study was to investigate modeland algorithm-dependent differences in MD/FA/RD/AD and anisotropy mode (MO) estimates in diffusion-weighted imaging of human brain white matter. Methods: The authors employed (a) data collected from 33 healthy subjects (20-59 yr, F: 15, M: 18) within the Human Connectome Project (HCP) on a customized 3 T scanner, and (b) data from 34 healthy subjects (26-61 yr, F: 5, M: 29) acquired on a clinical 3 T scanner. The DTI model was fitted to b-value = 0 and b-value = 1000 s/mm2 data while the DKI model was fitted to data comprising b-value = 0, 1000 and 3000/2500 s/mm2 [for dataset (a)/(b), respectively] through nonlinear and weighted linear least squares algorithms. In addition toMK/RK/AK maps,MD/FA/MO/RD/AD maps were estimated from both models and both algorithms. Using tract-based spatial statistics, the authors tested the null hypothesis of zero difference between the two MD/FA/MO/RD/AD estimates in brain white matter for both datasets and both algorithms. Results: DKI-derivedMD/FA/RD/AD andMOestimates were significantly higher and lower, respectively, than corresponding DTI-derived estimates. All voxelwise differences extended over most of the white matter skeleton. Fractional differences between the two estimates [(DKI - DTI)/DTI] of most invariants were seen to vary with the invariant value itself as well as with MK/RK/AK values, indicating substantial anatomical variability of these discrepancies. In the HCP dataset, the median voxelwise percentage differences across the whole white matter skeleton were (nonlinear least squares algorithm) 14.5% (8.2%-23.1%) for MD, 4.3% (1.4%-17.3%) for FA, -5.2% (-48.7% to -0.8%) for MO, 12.5% (6.4%-21.2%) for RD, and 16.1% (9.9%-25.6%) for AD (all ranges computed as 0.01 and 0.99 quantiles). All differences/trends were consistent between the discovery (HCP) and replication (local) datasets and between estimation algorithms. However, the relationships between such trends, estimated diffusion tensor invariants, and kurtosis estimates were impacted by the choice of fitting routine. Conclusions: Model-dependent differences in the estimation of conventional indexes of MD/FA/MO/RD/AD can be well beyond commonly seen disease-related alterations. While estimating diffusion tensor-derived indexes using the DKI model may be advantageous in terms of mitigating b-value dependence of diffusivity estimates, such estimates should not be referred to as conventional DTI-derived indexes in order to avoid confusion in interpretation as well as multicenter comparisons. In order to assess the potential and advantages of DKI with respect to DTI as well as to standardize diffusion-weighted imaging methods between centers, both conventional DTI-derived indexes and diffusion tensor invariants derived by fitting the non-Gaussian DKI model should be separately estimated and analyzed using the same combination of fitting routines. [ABSTRACT FROM AUTHOR]

An area of rapid advancement in abdominal MRI is diffusion-weighted imaging (DWI). By measuring diffusion properties of water molecules, DWI is capable of non-invasively probing tissue properties and physiology at cellular and macromolecular level. The integration of DWI as part of abdominal MRI exam allows better lesion characterization and therefore more accurate initial diagnosis and treatment monitoring. One of the most technical challenging, but also most useful abdominal DWI applications is in liver and therefore requires special attention and careful optimization. In this article, the latest technical developments of DWI and its liver applications are reviewed with the explanations of the technical principles, recommendations of the imaging parameters, and examples of clinical applications. More advanced DWI techniques, including Intra-Voxel Incoherent Motion (IVIM) diffusion imaging, anomalous diffusion imaging, and Diffusion Kurtosis Imaging (DKI) are discussed. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging (DWI) is a powerful tool to investigate the microscopic structure of the central nervous system (CNS). Diffusion tensor imaging (DTI), a common model of the DWI signal, has a demonstrated sensitivity to detect microscopic changes as a result of injury or disease. However, DTI and other similar models have inherent limitations that reduce their specificity for certain pathological features, particularly in tissues with complex fiber arrangements. Methods such as double pulsed field gradient (dPFG) and q-vector magic angle spinning (qMAS) have been proposed to specifically probe the underlying microscopic anisotropy without interference from the macroscopic tissue organization. This is particularly important for the study of acute injury, where abrupt changes in the microscopic morphology of axons and dendrites manifest as focal enlargements known as beading. The purpose of this work was to assess the relative sensitivity of DWI measures to beading in the context of macroscopic fiber organization and edema. Computational simulations of DWI experiments in normal and beaded axons demonstrated that, although DWI models can be highly specific for the simulated pathologies of beading and volume fraction changes in coherent fiber pathways, their sensitivity to a single idealized pathology is considerably reduced in crossing and dispersed fibers. However, dPFG and qMAS have a high sensitivity for beading, even in complex fiber tracts. Moreover, in tissues with coherent arrangements, such as the spinal cord or nerve fibers in which tract orientation is known a priori, a specific dPFG sequence variant decreases the effects of edema and improves specificity for beading. Collectively, the simulation results demonstrate that advanced DWI methods, particularly those which sample diffusion along multiple directions within a single acquisition, have improved sensitivity to acute axonal injury over conventional DTI metrics and hold promise for more informative clinical diagnostic use in CNS injury evaluation. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2-3mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to examine sex dimorphism in water diffusion in the brain throughout the normal aging process by magnetic resonance imaging. Methods: Diffusion-weighted images covering the majority of the brain were acquired from 77 healthy participants. Both the mean water diffusivity and diffusion kurtosis were calculated from the cortical regions and parcellated according to the template in anatomical automatic labeling. The mean water diffusivity and diffusion kurtosis from both sexes were examined and subsequently correlated with age. Statistical significance was set at a threshold of p < 0.01 after correction for multiple comparisons. In regions that reached statistical significance, a linear regression model was performed. Analysis of variance was conducted to determine the interaction between aging and sex. Results: Sex differences were observed for three aspects. First, compared to females, males presented increased mean water diffusivity and a decreased diffusion kurtosis in the frontal and temporal lobes. Second, a widespread age-related increase in mean water diffusivity was observed, which was more significant in the frontal, occipital, and temporal areas and in the cingulum in females. Third, the diffusion kurtosis decreased with aging but only in restricted areas for both sexes. For the interaction of aging and sex, the most significant change was observed with regards to mean diffusivity, mostly in the right amygdala. Conclusions: A sex-related dimorphism in water diffusion throughout the aging process was observed in the cortex using magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Abstract: In this work we provide an up-to-date short review of computational magnetic resonance imaging (MRI) and software tools that are widely used to process and analyze diffusion-weighted MRI data. A review of different methods used to acquire, model and analyze diffusion-weighted imaging data (DWI) is first provided with focus on diffusion tensor imaging (DTI). The major preprocessing, processing and post-processing procedures applied to DTI data are discussed. A list of freely available software packages to analyze diffusion MRI data is also provided. [Copyright &y& Elsevier]

Abstract: Diffusion kurtosis imaging (DKI) is a new model in medical engineering, where a diffusion kurtosis (DK) tensor is involved. A DK tensor is a fourth-order three-dimensional fully symmetric tensor. In this paper, we introduce -eigenvalues for a DK tensor. The largest, the smallest and the average -eigenvalues of a DK tensor correspond with the largest, the smallest and the average apparent kurtosis coefficients (AKC) of a water molecule in the space, respectively. We present their computational methods and discuss related anisotropy value of AKC. [Copyright &y& Elsevier]

Abstract: MR diffusion kurtosis imaging (DKI) was proposed recently to study the deviation of water diffusion from Gaussian distribution. Mean kurtosis, the directionally averaged kurtosis, has been shown to be useful in assessing pathophysiological changes, thus yielding another dimension of information to characterize water diffusion in biological tissues. In this study, orthogonal transformation of the 4th order diffusion kurtosis tensor was introduced to compute the diffusion kurtoses along the three eigenvector directions of the 2nd order diffusion tensor. Such axial (K //) and radial (K ┴) kurtoses measured the kurtoses along the directions parallel and perpendicular, respectively, to the principal diffusion direction. DKI experiments were performed in normal adult (N =7) and formalin-fixed rat brains (N =5). DKI estimates were documented for various white matter (WM) and gray matter (GM) tissues, and compared with the conventional diffusion tensor estimates. The results showed that kurtosis estimates revealed different information for tissue characterization. For example, K // and K ┴ under formalin fixation condition exhibited large and moderate increases in WM while they showed little change in GM despite the overall dramatic decrease of axial and radial diffusivities in both WM and GM. These findings indicate that directional kurtosis analysis can provide additional microstructural information in characterizing neural tissues. [Copyright &y& Elsevier]

Since its first description and development in the late 20th century, diffusion magnetic resonance imaging (dMRI) has proven useful in describing the microstructural details of biological tissues. Signal generated from the protons of water molecules undergoing Brownian motion produces contrast based on the varied diffusivity of tissue types. Images employing diffusion contrast were first used to describe the diffusion characteristics of tissues, later used to describe the fiber orientations of white matter through tractography, and most recently proposed as a functional contrast method capable of delineating neuronal firing in the active brain. Thanks to the molecular origins of its signal source, diffusion contrast is inherently useful at describing features of the microenvironment; however, limitations in achievable resolution in magnetic resonance imaging (MRI) scans precluded direct visualization of tissue microstructure for decades following MRI's inception as an imaging modality. Even after advancements in MRI hardware had permitted the visualization of mammalian cells, these specialized systems could only accommodate fixed specimens that prohibited the observation and characterization of physiological processes. The goal of the current study was to visualize cellular structure and investigate the subcellular origins of the functional diffusion contrast mechanism (DfMRI) in living, mammalian tissue explants. Using a combination of ultra-high field spectrometers, micro radio frequency (RF) coils, and an MRI-compatible superfusion device, we are able to report the first live, mammalian cells—α-motor neurons—visualized with magnetic resonance microscopy (MRM). We are also able to report changes in the apparent diffusion of the stratum oriens within the hippocampus—a layer comprised primarily of pyramidal cell axons and basal dendrites—and the spinal cord's ventral horn following exposure to kainate. • First images of live mammalian cells—α-motor neurons—visualized using MRI. • Diffusion contrast reveals faster diffusivity in live cells compared to neuropil. • Micro-superfusion setup for MRM allows chemical perturbation of acute slices. [ABSTRACT FROM AUTHOR]

This work offers a short up-to-date review of diffusion-weighted MR imaging (dMRI) and software tools that are used widely to process and analyze clinical dMRI. A consolidated dMRI protocol for clinical applications that enables the mapping of tissue microstructural attributes is presented., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Quantitative kurtosis phantoms are sought by multicenter clinical trials to establish accuracy and precision of quantitative imaging biomarkers on the basis of diffusion kurtosis imaging (DKI) parameters. We designed and evaluated precision, reproducibility, and long-term stability of a novel isotropic (i)DKI phantom fabricated using four families of chemicals based on vesicular and lamellar mesophases of liquid crystal materials. The constructed iDKI phantoms included negative control monoexponential diffusion materials to independently characterize noise and model-induced bias in quantitative kurtosis parameters. Ten test-retest DKI studies were performed on four scanners at three imaging centers over a six-month period. The tested prototype phantoms exhibited physiologically relevant apparent diffusion, D app , and kurtosis, K app , parameters ranging between 0.4 and 1.1 (×10 -3 mm 2 /s) and 0.8 and 1.7 (unitless), respectively. Measured kurtosis phantom K app exceeded maximum fit model bias (0.1) detected for negative control (zero kurtosis) materials. The material-specific parameter precision [95% CI for D app : 0.013-0.022(×10 -3 mm 2 /s) and for K app : 0.009-0.076] derived from the test-retest analysis was sufficient to characterize thermal and temporal stability of the prototype DKI phantom through correlation analysis of inter-scan variability. The present study confirms a promising chemical design for stable quantitative DKI phantom based on vesicular mesophase of liquid crystal materials. Improvements to phantom preparation and temperature monitoring procedures have potential to enhance precision and reproducibility for future multicenter iDKI phantom studies.

Background and Purpose: In patients with neuromyelitis optica (NMO), damage to extensive regions of normal-appearing WM has been observed. To investigate the possibility that microstructural alterations are present in these WM tracts, DTI and diffusion kurtosis imaging (DKI) techniques were applied and compared., Material and Methods: Thirteen patients with NMO and 13 demographically and gender-matched controls underwent MRI using a 3T MR scanner, with DTI/DKI sequence acquired jointly fitted. Parametric fractional anisotropy maps were derived from diffusion tensor (FADTI) values using b-values of 0s/mm(2) and 1000s/mm(2). Parametric fractional anisotropy maps derived from diffusion kurtosis tensor (FADKI) values were also acquired using b-values of 0, 1000, and 2000s/mm(2). Mean FADTI and FADKI values were also calculated. A ROI analysis of the genu and splenium of the corpus callosum (CC), cerebral peduncle (CP), and optic radiation (OR) was also performed. Student's t-test and corrections for multiple comparisons were used to evaluate the data obtained., Results: A significant decrease in the FADTI values obtained for NMO patients versus controls was observed for the splenium of the CC and the left OR (p<0.05). However, just a positive trend was observed for the FADKI values associated with the same WM tracts., Conclusions: To our knowledge, this is the first study to analyze WM tracts of NMO patients using DTI and DKI. These data indicate that DKI could have limitations in evaluating the WM integrity in NMO patients. Furthermore, the results obtained are consistent with the hypothesis that diffuse brain involvement characterizes NMO., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: The purpose of this study was to examine sex dimorphism in water diffusion in the brain throughout the normal aging process by magnetic resonance imaging., Methods: Diffusion-weighted images covering the majority of the brain were acquired from 77 healthy participants. Both the mean water diffusivity and diffusion kurtosis were calculated from the cortical regions and parcellated according to the template in anatomical automatic labeling. The mean water diffusivity and diffusion kurtosis from both sexes were examined and subsequently correlated with age. Statistical significance was set at a threshold of p < 0.01 after correction for multiple comparisons. In regions that reached statistical significance, a linear regression model was performed. Analysis of variance was conducted to determine the interaction between aging and sex., Results: Sex differences were observed for three aspects. First, compared to females, males presented increased mean water diffusivity and a decreased diffusion kurtosis in the frontal and temporal lobes. Second, a widespread age-related increase in mean water diffusivity was observed, which was more significant in the frontal, occipital, and temporal areas and in the cingulum in females. Third, the diffusion kurtosis decreased with aging but only in restricted areas for both sexes. For the interaction of aging and sex, the most significant change was observed with regards to mean diffusivity, mostly in the right amygdala., Conclusions: A sex-related dimorphism in water diffusion throughout the aging process was observed in the cortex using magnetic resonance imaging.