Diffusion Kurtosis Imaging Research Articles

Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging.

To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer. A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI. The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery. Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer. Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study.

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia. Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed. Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment. The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Age-related alterations in human cortical microstructure across the lifespan: Insights from high-gradient diffusion MRI.

The human brain undergoes age-related microstructural alterations across the lifespan. Soma and Neurite Density Imaging (SANDI), a novel biophysical model of diffusion MRI, provides estimates of cell body (soma) radius and density, and neurite density in gray matter. The goal of this cross-sectional study was to assess the sensitivity of high-gradient diffusion MRI toward age-related alterations in cortical microstructure across the adult lifespan using SANDI. Seventy-two cognitively unimpaired healthy subjects (ages 19-85 years; 40 females) were scanned on the 3T Connectome MRI scanner with a maximum gradient strength of 300mT/m using a multi-shell diffusion MRI protocol incorporating 8 b-values and diffusion time of 19 ms. Intra-soma signal fraction obtained from SANDI model-fitting to the data was strongly correlated with age in all major cortical lobes (r = -0.69 to -0.60, FDR-p < 0.001). Intra-soma signal fraction (r = 0.48-0.63, FDR-p < 0.001) and soma radius (r = 0.28-0.40, FDR-p < 0.04) were significantly correlated with cortical volume in the prefrontal cortex, frontal, parietal, and temporal lobes. The strength of the relationship between SANDI metrics and age was greater than or comparable to the relationship between cortical volume and age across the cortical regions, particularly in the occipital lobe and anterior cingulate gyrus. In contrast to the SANDI metrics, all associations between diffusion tensor imaging (DTI) and diffusion kurtosis imaging metrics and age were low to moderate. These results suggest that high-gradient diffusion MRI may be more sensitive to underlying substrates of neurodegeneration in the aging brain than DTI and traditional macroscopic measures of neurodegeneration such as cortical volume and thickness.

Exploring white matter microstructural alterations in mild cognitive impairment: a multimodal diffusion MRI investigation utilizing diffusion kurtosis and free-water imaging.

Mild Cognitive Impairment (MCI) is a transitional stage from normal aging to dementia, characterized by noticeable changes in cognitive function that do not significantly impact daily life. Diffusion MRI (dMRI) plays a crucial role in understanding MCI by assessing white matter integrity and revealing early signs of axonal degeneration and myelin breakdown before cognitive symptoms appear. This study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to compare white matter microstructure in individuals with MCI to cognitively normal (CN) individuals, employing advanced dMRI techniques such as diffusion kurtosis imaging (DKI), mean signal diffusion kurtosis imaging (MSDKI), and free water imaging (FWI). Analyzing data from 55 CN subjects and 46 individuals with MCI, this study found significant differences in white matter integrity, particularly in free water levels and kurtosis values, suggesting neuroinflammatory responses and microstructural integrity disruption in MCI. Moreover, negative correlations between Mini-Mental State Examination (MMSE) scores and free water levels in the brain within the MCI group point to the potential of these measures as early biomarkers for cognitive impairment. In conclusion, this study demonstrates how a multimodal advanced diffusion imaging approach can uncover early microstructural changes in MCI, offering insights into the neurobiological mechanisms behind cognitive decline.

Restriction Spectrum Imaging and Diffusion Kurtosis Imaging for Assessing Proliferation Status in Rectal Carcinoma

To investigate the application of the three-compartment restriction spectrum imaging (RSI) model, diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in predicting Ki-67 status in rectal carcinoma. A total of 80 rectal carcinoma patients, including 47 high-proliferation (Ki-67> 50%) cases and 33 low-proliferation (Ki-67≤ 50%) cases, underwent pelvic MRI were enrolled. Parameters derived from RSI (f1, f2, and f3), DKI (MD and MK), and DWI (ADC) were calculated and compared between the two groups. Logistic regression (LR) analysis was conducted to identify independent predictors and assess combined diagnosis. Area under the receiver operating characteristic curve (AUC), DeLong analysis, and calibration curve analyses were performed to evaluate diagnostic performance. The patients with high-proliferation rectal carcinoma exhibited significantly higher f1 and MK values and significantly lower ADC, MD, f2, and f3 values than those with low-proliferation rectal carcinoma (P<0.05). LR analysis showed that MD, MK, and f2 were independent predictors for Ki-67 status in rectal carcinoma. Moreover, the combination of these three parameters achieved an optimal diagnostic efficacy (AUC = 0.877, sensitivity = 80.85%, specificity = 84.85%) that was significantly better than that obtained using ADC (AUC = 0.783, Z=2.347, P=0.019), f2 (AUC = 0.732, Z=2.762, P=0.006), and f3 (AUC = 0.700, Z=3.071, P=0.002). The combined diagnosis also showed good performance (AUC = 0.859) in the internal validation analysis based on 1000 bootstrap samples, while the calibration curve demonstrated that the combined diagnosis provided good stability. RSI, DKI, and DWI can effectively differentiate between patients with high- and low-proliferation rectal carcinoma. Furthermore, the MD, MK, and f2 imaging parameters may be a novel and promising combination biomarker for examining Ki-67 status in rectal carcinoma.

Monoexponential and advanced diffusion-weighted imaging for hepatic fibrosis staging based on high inter-examiner reliability.

To determine the diagnostic efficiencies of multiple diffusion-weighted imaging (DWI) techniques for hepatic fibrosis (HF) staging under the premise of high inter-examiner reliability. Participants with biopsy-confirmed HF were recruited and divided into the early HF (EHF) and advanced HF (AHF) groups; healthy volunteers (HVs) served as controls. Two examiners analyzed intravoxel incoherent motion (IVIM) using the IVIM-DWI and diffusion kurtosis imaging (DKI) models. Intravoxel incoherent motion-DWI, DKI, and diffusion tensor imaging parameters with intraclass correlation coefficients (ICCs) of ≥0.6 were used to create regression models: HVs vs. EHF and EHF vs. AHF. We enrolled 48 HVs, 59 EHF patients, and 38 AHF patients. Mean, radial, and axial kurtosis; fractional anisotropy; mean, radial, and axial diffusivity; and α exhibited excellent reliability (ICCs: 0.80-0.98). Fractional anisotropy of kurtosis, f, and apparent diffusion coefficient showed good reliability (ICCs: 0.69-0.92). The real (0.58-0.67), pseudo- (0.27-0.76), and distributed diffusion coefficients (0.58-0.67) showed low reliability. In the HVs versus (vs.) EHF model, α (p=0.008) and ADC (p=0.011) presented statistical differences (area under curve [AUC]: 0.710). In the EHF vs. AHF model, α (p=0.04) and distributed diffusion coefficient (p=0.02) presented significant differences (AUC: 0.758). Under the premise of high inter-examiner reliability, DWI and IVIM-derived stretched-exponential model parameters may help stage HF.

Unveiling the diagnostic potential of diffusion kurtosis imaging and intravoxel incoherent motion for detecting and characterizing prostate cancer: a meta-analysis.

This study aims to assess the diagnostic capabilities of Diffusion Kurtosis Imaging (DKI) and Intravoxel Incoherent Motion (IVIM) in prostate cancer (PCa) detection and characterization. A comprehensive search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for articles published up to September 10, 2023, that evaluated the diagnostic efficacy of MD, MK, Dt, f, and Dp parameters. Data were pooled using a bivariate mixed-effects regression model and analyzed with R software. In total, 27 studies were included. The analysis revealed distinct diagnostic efficacies for DKI and IVIM. In the overall model, sensitivity and specificity were 0.807 and 0.797, respectively, with prospective studies showing higher specificity (0.858, p = 0.024). The detection model yielded increased sensitivity (0.845) and specificity (0.812), with DKI outperforming IVIM in both metrics (sensitivity: 0.87, p = 0.043; specificity: 0.837, p = 0.26); MD had high sensitivity (0.88) and specificity (0.82), while MK's specificity was significantly higher (0.854, p = 0.04); Dp's sensitivity was significantly lower (0.64, p = 0.016). In characterization, sensitivity and specificity were 0.708 and 0.735, respectively, with no significant differences between DKI and IVIM or Gleason Scores; MK had higher sensitivity (0.78, p = 0.039), and f's sensitivity was significantly lower (0.51, p = 0.019). In summary, the study underscores DKI's enhanced diagnostic accuracy over IVIM in detecting PCa, with MK standing out for its precision. Conversely, Dp and f lag in diagnostic performance. Despite these promising results, the study highlights the imperative for standardized protocols and study designs to achieve reliable and consistent outcomes.

The value of diffusion kurtosis imaging and dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid gland tumors.

Parotid gland tumors (PGTs) are the most common benign tumors of salivary gland tumors. However, the diagnostic value of relative values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) parameters for PGTs has not been extensively studied. Therefore, this study aimed to evaluate the diagnostic performance of combined DKI and DCE-MRI for differentiating PGTs by introducing the concept of relative value. The DCE-MRI and DKI imaging data of 142 patients with PGTs between June 2018 and August 2022 were collected. Patients were divided into four groups by histopathology: malignant tumors (MTs), pleomorphic adenomas (PAs), Warthin tumors (WTs), and basal cell adenomas (BCAs). All MRI examinations were conducted using a 3 T MRI scanner with a 20-channel head and neck coil. Quantitative parameters of DCE-MRI and DKI and their relative values were determined. Kruskal-Wallis H test, post-hoc test with Bonferroni correction, one-way analysis of variance (ANOVA) and post-hoc test with least significant difference (LSD) method, and the receiver operating characteristic (ROC) curve were used for statistical analysis. Statistical significance was set at P<0.05. Only the combination of DKI and DCE-MRI parameters could reliably distinguish BCAs from other PGTs. PAs demonstrated the lowest transfer constant from plasma to extravascular extracellular space (Ktrans) value [0.09 (0.06, 0.20) min-1], relative Ktrans (rKtrans) [-0.24 (-0.64, 1.00)], rate constant from extravascular extracellular space to plasma (Kep) value [0.32 (0.22, 0.53) min-1], relative Kep (rKep) [0.32 (0.22, 0.53) min-1], and initial area under curve (iAUC) value [0.15 (0.09, 0.26) mmol·s/kg] compared with WTs, BCAs, and MTs (all P<0.05). The Ktrans values for MTs were substantially lower [0.17 (0.10, 0.31) min-1] than those for WTs (P=0.01). The Kep values for MTs [0.71 (0.52, 1.28) min-1] were substantially lower (all P<0.05) than those for WTs and BCAs. PAs and BCAs had higher diffusion coefficient (D) values and lower diffusion kurtosis (K) values and relative K (rK) values than MTs and WTs. However, the D and K values did not differ significantly even in their relative values of PAs and BCAs (all P>0.05). By using logistic regression, the combination of K value and rKep value further enhanced their discriminatory power between PAs and WTs [area under the ROC curve (AUC), 0.986], the combination of K and rKep value further enhanced their discriminatory power between PAs and MTs (AUC, 0.915), and the combination of D and Kep value further enhanced their discriminatory power between BCAs and MTs (AUC, 0.909). DKI and DCE-MRI can be used to differentiate PGTs quantitatively and can complement each other. The combined use of DKI and DCE-MRI parameters can improve the diagnostic accuracy of distinguishing PGTs.

Diffusion kurtosis imaging for different brain masses characterization

BackgroundDiffusion 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.ResultsMean 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.ConclusionsDiffusion 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.

Glioma Type Prediction with Dynamic Contrast-Enhanced MR Imaging and Diffusion Kurtosis Imaging-A Standardized Multicenter Study.

The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification.

T2* cartilage mapping in early axial spondyloarthritis: diagnostic accuracy and correlation with clinical characteristics, sacroiliitis MRI scorings, and diffusion metrics.

To determine the performance of T2* cartilage mapping in diagnosing and assessing disease activity in early axial spondyloarthritis (axSpA), and to investigate the interaction of cartilage damage with clinical characteristics, sacroiliitis MRI scorings, and diffusion metrics. This prospective study included 83 axSpA patients and 37 no-axSpA patients. Clinical characteristics, the Assessment of SpondyloArthritis International Society-defined active sacroiliitis on MRI, and T2* SIJs values were recorded. In axSpA, disease activity was evaluated using the ankylosing spondylitis disease activity score-C-reactive protein; active sacroiliitis was evaluated using Spondyloarthritis Research Consortium of Canada, intravoxel incoherent motion, and diffusion kurtosis imaging; chronic sacroiliitis was assessed using composite structural damage score (CSDS) and structural score fat. Mann-Whitney U-test, Kruskal-Wallis test with false discovery rate (FDR), ROC curve, and linear regression were used for statistical analysis. AxSpA patients had significantly higher T2*SIJs values than no-axSpA patients. (22.86 ± 2.42 ms vs 20.36 ± 1.30 ms, p < 0.001). The combination of T2*SIJs values and active sacroiliitis on MRI had the highest AUC for identifying axSpA. T2*SIJs values were significantly different between the inactive and very high, moderate and very high, high and very high, as well as inactive and high disease activity groups (all pFDR < 0.05). Dk (β = 0.48) and CSDS (β = 0.48) were independently associated with T2*SIJs values. T2* values may be a promising biomarker for diagnosing and differentiating disease activity in early axSpA. Both acute and chronic sacroiliitis influence cartilage properties. Sacroiliac joint cartilage abnormalities can be quantified with T2* relaxation time and allow better characterization of early axSpA. T2* mapping may have value in evaluating axSpA. The combination of T2* values and active sacroiliitis on MRI enhances diagnostic performance for axSpA. Abnormalities measured with T2* values correlate with disease activity, acute sacroiliitis, and degree of structural damage.

Abnormally glymphatic system functional in patients with migraine: a diffusion kurtosis imaging study

BackgroundThe diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) method has been used to evaluate glymphatic system function in patients with migraine. However, since the diffusion tensor model cannot accurately describe the diffusion coefficient of the nerve fibre crossing region, we proposed a diffusion kurtosis imaging ALPS (DKI-ALPS) method to evaluate glymphatic system function in patients with migraine.MethodsThe study included 29 healthy controls and 37 patients with migraine. We used diffusion imaging data from a 3T MRI scanner to calculate DTI-ALPS and DKI-ALPS indices of the two groups. We compared the DTI-ALPS and DKI-ALPS indices between the two groups using a two-sample t-test and performed correlation analyses with clinical variables.ResultsThere was no significant difference in DTI-ALPS index between the two groups. Patients with migraine showed a significantly increased right DKI-ALPS index compared to healthy controls (1.6858 vs. 1.5729; p = 0.0301). There was no significant correlation between ALPS indices and clinical variables.ConclusionsDKI-ALPS is a potential method to assess glymphatic system function and patients with migraine do not have impaired glymphatic system function.

High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain

Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.

In vivo mapping of cellular resolution neuropathology in brain ischemia with diffusion MRI.

Noninvasive mapping of cellular pathology can provide critical diagnostic and prognostic information. Recent advances in diffusion magnetic resonance imaging enabled in vivo examination of tissue microstructures well beyond the imaging resolution. Here, we proposed to use diffusion time-dependent diffusion kurtosis imaging (tDKI) to simultaneously assess cellular morphology and transmembrane permeability in hypoxic-ischemic (HI) brain injury. Through numerical simulations and organoid imaging, we demonstrated the feasibility of capturing effective size and permeability changes using tDKI. In vivo MRI of HI-injured mouse brains detected a shift of the tDKI peak to longer diffusion times, suggesting swelling of the cellular processes. Furthermore, we observed a faster decrease of the tDKI tail, reflecting increased transmembrane permeability associated with up-regulated water exchange or necrosis. Such information, unavailable from a single diffusion time, can predict salvageable tissues. Preliminary applications of tDKI in patients with ischemic stroke suggested increased transmembrane permeability in stroke regions, illustrating tDKI's potential for detecting pathological changes in the clinics.

Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study.

Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.

Associations between diffusion kurtosis imaging metrics and neurodevelopmental outcomes in neonates with low-grade germinal matrix and intraventricular hemorrhage

Diffusion Kurtosis Imaging (DKI)-derived metrics are recognized as indicators of maturation in neonates with low-grade germinal matrix and intraventricular hemorrhage (GMH-IVH). However, it is not yet known if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire DKI-derived metrics in neonates with low-grade GMH-IVH, and to demonstrate their association with later neurodevelopmental outcomes. In this prospective study, neonates with low-grade GMH-IVH and control neonates were recruited, and DKI were performed between January 2020 and March 2021. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age. Mean kurtosis (MK), radial kurtosis (RK) and gray matter values were measured. Spearman correlation analyses were conducted for the measured values and neurodevelopmental outcome scores. Forty controls (18 males, average gestational age (GA) 30 weeks ± 1.3, corrected GA at MRI scan 38 weeks ± 1) and thirty neonates with low-grade GMH-IVH (13 males, average GA 30 weeks ± 1.5, corrected GA at MRI scan 38 weeks ± 1). Neonates with low-grade GMH-IVH exhibited lower MK and RK values in the PLIC and the thalamus (P < 0.05). The MK value in the thalamus was associated with Mental Development Index (MDI) (r = 0.810, 95% CI 0.695–0.13; P < 0.001) and Psychomotor Development Index (PDI) (r = 0.852, 95% CI 0.722–0.912; P < 0.001) scores. RK value in the caudate nucleus significantly and positively correlated with MDI (r = 0.496, 95% CI 0.657–0.933; P < 0.001) and PDI (r = 0.545, 95% CI 0.712–0.942; P < 0.001) scores. The area under the curve (AUC) were used to assess diagnostic performance of MK and RK in thalamus (AUC = 0.866, 0.787) and caudate nucleus (AUC = 0.833, 0.671) for predicting neurodevelopmental outcomes. As quantitative neuroimaging markers, MK in thalamus and RK in caudate nucleus may help predict neurodevelopmental outcomes in neonates with low-grade GMH-IVH.

Paracetamol-induced Liver Injury in An Experimental Rat Model: Noninvasive Assessment using DKI and the Effect of Gadoxetate on DKI Parameters.

To explore the potential of diffusion kurtosis imaging (DKI) for assessing the degree of liver injury in a paracetamol-induced rat model and to simultaneously investigate the effect of intravenous gadoxetate on DKI parameters. Paracetamol was used to induce hepatoxicity in 39 rats. The rats were pathologically classified into 3 groups: normal (n=11), mild necrosis (n=18), and moderate necrosis (n=10). DKI was performed before and, 15 min, 25 min, and 45 min after gadoxetate administration. Repeated-measures ANOVA with Tukey's multiple comparison test was used to investigate the effect of gadoxetate on mean diffusivity (MD) and mean diffusion kurtosis (MK) and to assess the differences in MD and MK among the three groups. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of the MD values when discriminating between the necrotic groups. Gadoxetate had no significant effect on either the MD or the MK, and the effect size was small. The MD in the moderate necrosis group was significantly lower than that in the other two groups (F = 13.502, p < 0.001; η2 = 0.428 [95% CI: 0.082-0.637]), while the MK did not significantly differ among the three groups (F = 2.702, p = 0.081; η2 = 0.131 [95% CI: 0.001-0.4003]). The AUCs of MD for discriminating the moderate necrosis or normal group from the other groups were 0.921 (95% CI: 0.832-1.000) and 0.831 (95% CI: 0.701-0.961), respectively. It would be better to measure the MD and MK before gadoxetate injection. MD showed potential for assessing the degree of liver necrosis in a paracetamol-induced liver injury rat model.

Combining multimodal diffusion-weighted imaging and morphological parameters for detecting lymph node metastasis in cervical cancer.

Accurate detection of lymph node metastasis (LNM) is crucial for determining the tumor stage, selecting optimal treatment, and estimating the prognosis for cervical cancer. This study aimed to assess the diagnostic efficacy of multimodal diffusion-weighted imaging (DWI) and morphological parameters alone or in combination, for detecting LNM in cervical cancer. In this prospective study, we enrolled consecutive cervical cancer patients who received multimodal DWI (conventional DWI, intravoxel incoherent motion DWI, and diffusion kurtosis imaging) before treatment from June 2022 to June 2023. The largest lymph node (LN) observed on each side on imaging was matched with that detected on pathology to improve the accuracy of LN matching. Comparison of the diffusion and morphological parameters of LNs and the primary tumor between the positive and negative LN groups. A combined diagnostic model was constructed using multivariate logistic regression, and the diagnostic performance was evaluated using receiver operating characteristic curves. A total of 93 cervical cancer patients were enrolled: 35 with LNM (48 positive LNs were collected), and 58 without LNM (116 negative LNs were collected). The area under the curve (AUC) values for the apparent diffusion coefficient, diffusion coefficient, mean diffusivity, mean kurtosis, long-axis diameter, short-axis diameter of LNs, and the largest primary tumor diameter were 0.716, 0.720, 0.716, 0.723, 0.726, 0.798, and 0.744, respectively. Independent risk factors included the diffusion coefficient, mean kurtosis, short-axis diameter of LNs, and the largest primary tumor diameter. The AUC value of the combined model based on the independent risk factors was 0.920, superior to the AUC values of all the parameters mentioned above. Combining multimodal DWI and morphological parameters improved the diagnostic efficacy for detecting cervical cancer LNM than using either alone.

Evaluation of microstructural changes in the brain in transfusion dependent thalassemia patients with advanced magnetic resonance imaging techniques

PurposeTransfusion-dependent thalassemia (TDT) is associated with iron accumulation in the body and an increased tendency for thrombosis. With the increased life expectancy in these patients, the detection of neurocognitive complications has gained importance. This study investigates the microstructural changes in TDT patients using advanced diffusion MRI techniques and their relationship with laboratory parameters.MethodsThe study included 14 TDT patients and 14 control subjects. Tract-based spatial statistics (TBSS) were used to examine differences in DTI parameters such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in thalassemia patients using multi-shell DWI images. The mean kurtosis (MK) difference was investigated using diffusion kurtosis imaging. Fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC) differences were examined using fixel-based analysis. In the patient group, correlative tractography was used to investigate the relationship between DTI parameters and platelet (PLT) and ferritin levels.ResultsIncrease in RD and MD was observed, particularly in the white matter tracts of the corona radiata in patient group. Additionally, an increase in AD was detected in a limited area. Correlative tractography in thalasemia patients showed a positive correlation between increases in RD, MD, and AD with PLT and ferritin. Fixel-based analysis demonstrated a dispersed distribution in white matter fibers, with a more pronounced decrease in FD, FC, and FDC in the internal capsule.ConclusionThere is widespread involvement in the white matter and fiber tracts in thalassemia patients, which is highly correlated with thrombotic parameters.

Multiple diffusion models for predicting pathologic response of esophageal squamous cell carcinoma to neoadjuvant chemotherapy.

To assess the feasibility and diagnostic performance of the fractional order calculus (FROC), continuous-time random-walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), mono-exponential (MEM) and stretched exponential models (SEM) for predicting response to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous cell carcinoma (ESCC). This study prospectively included consecutive ESCC patients with baseline and follow up MR imaging and pathologically confirmed cT1-4aN + M0 or T3-4aN0M0 and underwent radical resection after neoadjuvant chemotherapy (NACT) between July 2019 and January 2023. Patients were divided into pCR (TRG 0) and non-pCR (TRG1 + 2 + 3) groups according to tumor regression grading (TRG). The Pre-, Post- and Delta-treatment models were built. 18 predictive models were generated according to different feature categories, based on six models by five-fold cross-validation. Areas under the curve (AUCs) of the models were compared by using DeLong method. Overall, 90 patients (71 men, 19 women; mean age, 64years ± 6 [SD]) received NACT and underwent baseline and Post-NACT esophageal MRI, with 29 patients in the pCR group and 61 patients in the non-pCR group. Among18 predictive models, The Pre-, Post-, and Delta-CTRW model showed good predictive efficacy (AUC = 0.722, 0.833 and 0.790). Additionally, the Post-FROC model (AUC = 0.907) also exhibited good diagnostic performance. Our study indicates that the CTRW model, along with the Post-FROC model, holds significant promise for the future of NACT efficacy prediction in ESCC patients.