Water Molecular Diffusion Heterogeneity Index Research Articles

Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer.

Unfortunately, the morphologic magnetic resonance imaging (MRI) is unable to determine perineural invasion (PNI) status. This study applied histogram analysis of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the assessment of PNI status of rectal cancer (RC). The retrospective analysis enrolled 175 patients with RC confirmed by postoperative pathology in The First Affiliated Hospital of Shandong First Medical University from January 2019 to December 2021. All patients underwent preoperative rectal MRI. Whole-tumor volume histogram features from IVIM-DWI were extracted using open-source software. Univariate analysis and multivariate logistic regression analysis were used to compare the differences in histogram parameters and clinical features between the PNI-positive group and PNI-negative group. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance, while the Delong test was used to compare the area under the curve of the models. The interobserver agreement of the histogram features derived from DWI, including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC) were good to excellent. A total of eight histogram features including DWI_maximum, DWI_skewness, D_kurtosis, D_minimum, D_skewness, D*_energy, D*_skewness, and f_minimum were significantly different between the PNI-positive and PNI-negative groups in the univariate analysis (P<0.05); among the clinicoradiologic factors, percentage of rectal wall circumference invasion (PCI) was significantly different between the two groups (P<0.05). Multivariate analysis demonstrated that the values of D*_energy, D*_skewness, and f_minimum differed significantly between the PNI-positive patients and PNI-negative patients (P<0.05), with the independent risk factors being D*_skewness [odds ratio (OR) =1.157; 95% confidence interval (CI): 1.050-1.276; P=0.003] and PCI (OR =11.108, 95% CI: 1.767-69.838; P=0.0002). The area under the curve of the model combining the three histogram features and PCI to assess PNI status in RC was 0.807 (95% CI: 0.741-0.863). The results of the Delong test showed that the combined model was significantly different from each single-parameter model (P<0.05). The combined model constructed on the basis of IVIM-DWI histogram features may help to assess the status of RC PNI.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions

PurposeThe purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs).MethodsPatients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency.ResultsA total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively.ConclusionAdvanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Intravoxel incoherent motion DWI with different mathematical models in predicting rectal adenoma with and without canceration

PurposeTo evaluate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) with mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models for predicting rectal adenomas with canceration. Material and methodsSixty patients with postoperative pathology-confirmed rectal adenoma (n = 31) and adenoma with canceration (n = 29) were enrolled and underwent IVIM-DWI scanning. The ME-derived apparent diffusion coefficient (ADC), BE-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), SE-derived distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured. The differences in each parameter between adenoma and canceration were compared. Multivariate binary logistic regression analysis was used to establish models for predicting rectal adenomas with canceration. Receiver operating characteristic curve analysis was applied to evaluate diagnostic performances of each model in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). ResultsThe AUCs of ADC, D, D*, f, DDC and α were 0.851 (95 % confidence interval, CI, 0.735–0.930), 0.895 (95 % CI, 0.789–0.960), 0.720 (95 % CI, 0.589–0.828), 0.791 (95 % CI, 0.667–0.886), 0.841 (95 % CI, 0.724–0.923) and 0.738 (95 % CI, 0.608–0.834), respectively. The AUCs of BE and SE models were 0.927 (95 % CI, 0.829–0.978) and 0.874 (95 % CI, 0.763–0.946), respectively. The AUC, sensitivity, specificity, and accuracy of the derived four values (ADC, D, f, and DDC) from the combination of three models were 0.950, 96.6 % (95 % CI, 95.3–97.6 %), 80.6 % (95 % CI, 78.0–82.9 %), and 88.3 % (95 % CI, 86.2–90.2 %), respectively. ConclusionADC can easily and effectively predict rectal adenomas with canceration. The BE model has a better combination of sensitivity and specificity for the diagnosis of rectal adenoma canceration.

Predicting Grade of Esophageal Squamous Carcinoma: Can Stretched Exponential Model-Based DWI Perform Better Than Bi-Exponential and Mono-Exponential Model?

BackgroundTo evaluate and compare the potential performance of various diffusion parameters obtained from mono-exponential model (MEM)-, bi-exponential model (BEM)-, and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in grading of esophageal squamous carcinoma (ESC).MethodsEighty-two patients with pathologically confirmed ESC without treatment underwent multi-b-value DWI scan with 13 b values (0~12,00 s/mm2). The apparent diffusion coefficient (ADC) deriving from the MEM; the pure molecular diffusion (ADCslow), pseudo-diffusion coefficient (ADCfast), perfusion, and fraction (f) deriving from the BEM; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) deriving from the SEM were calculated and compared between poorly differentiated and well/moderately differentiated ESC, respectively. The prediction parameters and diagnostic efficiency were compared by drawing receiver operating characteristic (ROC) curves.ResultsThe ADC, ADCslow, ADCfast, and DDC in poorly ESC were significantly lower than those in well/moderately differentiated ones. By using only one parameter, ADCslow, DDC had the moderate diagnostic efficiency and the areas under the curve (AUC) were 0.758 and 0.813 in differentiating ESC. The DDC had the maximum AUC with sensitivity (88.00%) and specificity (68.42%). Combining ADC with ADCfast, ADCslow, and DDC and combining ADCslow with ADCfast can provide a higher diagnostic accuracy with AUC ranging from 0.756, 0.771, 0.816, and 0.793, respectively.ConclusionVarious parameters derived from different DWI models including MEM, BEM, and SEM were potentially helpful in grading ESC. DDC obtained from SEM was the most promising diffusion parameter for predicting the grade of ESC.

Differentiation of Prostate Cancer and Stromal Hyperplasia in the Transition Zone With Monoexponential, Stretched-Exponential Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging in a Reduced Number of b Values: Correlation With Whole-Mount Pathology.

The aims of the study were to explore the feasibility of generating a monoexponential model (MEM), stretched-exponential model (SEM) based diffusion-weighted imaging (DWI), and diffusion kurtosis imaging (DKI) by applying the same set of reduced b values and to compare their effectiveness in distinguishing prostate cancer from stromal hyperplasia (SH) in the transition zone (TZ) area. An analysis of 75 patients who underwent preoperative DWI ( b values of 0, 700, 1400, 2000 s/mm 2 ) was performed. All lesions were localized on magnetic resonance images according to whole-mount histopathological correlations. The apparent diffusion coefficient (ADC), water molecular diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), mean diffusivity (MD), and mean kurtosis (MK) values were calculated and compared between the TZ cancer and SH groups. Receiver operating characteristic analysis and areas under the receiver operating characteristic curve (AUCs) were carried out for all parameters. Compared with the SH group, the ADC, DDC, α, and MD values of the TZ cancer group were significantly reduced, while the MK value was significantly increased (all P < 0.05). The AUCs of the ADC, DDC, α, MD, and MK were 0.828, 0.801, 0.813, 0.822, and 0.882, respectively. The AUC of MK was significantly higher than that of the other parameters (all P < 0.05). When using the reduced b -value set, all parameters from MEM, SEM, based DWI, and DKI can effectively distinguish TZ cancer from SH. Among them, DKI demonstrated potential clinical superiority over the others in TZ cancer diagnosis.

Predicting grade of esophageal squamous carcinoma: Can stretched exponential model-based DWI perform better than bi-exponential and mono-exponential model?

Background: To evaluate and compare the potential performance of various diffusion parameters obtained from Mono-Exponential Model (MEM), Bi-Exponential Model (BEM), and Stretched Exponential Model (SEM) Diffusion-Weighted Imaging (DWI) models in grading of Esophageal Squamous Carcinoma (ESC). Methods: Eighty-two patients with pathologically confirmed ESC without treatment underwent multi-b-value DWI scan with 13 b values (0~1200 s/mm2 ). The Apparent Diffusion Coefficient (ADC) deriving from the MEM; The Pure Molecular Diffusion (ADCslow), Pseudo-Diffusion Coefficient (ADCfast), Perfusion And Fraction (f) deriving from the BEM; the Distributed Diffusion Coefficient (DDC) and water molecular diffusion heterogeneity index (α) deriving from the SEM obtained were calculated and compared between poorly differentiated and well / moderately differentiated ESC respectively. The prediction parameters and diagnostic efficiency were compared by drawing Receiver Operating Characteristic (ROC) curve. Results: ADC, ADCslow, ADCfast, DDC in poorly ESC were significantly lower than those in well/moderately differentiated ones. By using only one parameter, ADCslow, DDC had the moderate diagnostic efficiency and the Areas Under the Curve (AUC) were 0.758 and 0.813 in grading ESC. DDC had the maximum AUC with sensitivity (88.00%) and specificity (68.42%). Combining ADC with ADCfast, ADCslow, DDC and combining ADCslow with ADCfast can provide a higher diagnostic accuracy with AUC ranging from 0.756, 0.771, 0.816 and 0.793 respectively. Conclusion: Various parameters derived from different DWI models including MEM, BEM and SEM, are potentially helpful in grading ESC. Compared with MEM model, ADCslow and DDC have better diagnostic performances. DDC obtained from SEM based model is the most promising diffusion parameter for predicting the grade of ESC. Keywords: Esophageal carcinoma; Diffusion magnetic resonance imaging; Stretched exponential model; Intravoxel incoherent motion.

Identification of histological features of endometrioid adenocarcinoma based on amide proton transfer-weighted imaging and multimodel diffusion-weighted imaging.

Noninvasive identification of the histological features of endometrioid adenocarcinoma is necessary. This study aimed to investigate whether amide proton transfer-weighted imaging (APTWI) and multimodel (monoexponential, biexponential, and stretched exponential) diffusion-weighted imaging (DWI) could predict the histological grade of endometrial adenocarcinoma (EA). In addition, we analyzed the correlation between each parameter and the Ki-67 index. A total of 90 EA patients who received pelvic magnetic resonance imaging (MRI) were enrolled. The magnetization transfer ratio asymmetry [MTRasym (3.5 ppm)], apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured and compared. Correlation coefficients between each parameter and histological grade and the Ki-67 index were calculated. Statistical methods included the independent samples t test, Spearman's correlation, and logistic regression. MTRasym (3.5 ppm) [(3.72%±0.31%) vs. (3.27%±0.48%)], f [(3.15%±0.36%) vs. (2.69%±0.83%)], and α [(0.89±0.05) vs. (0.81±0.09)] were higher and ADC [(0.82±0.08) vs. (0.89±0.10) ×10-3 mm2/s], D [(0.67±0.09) vs. (0.81±0.11) ×10-3 mm2/s], and DDC [(1.04±0.09) vs. (1.13±0.13) ×10-3 mm2/s] were lower in high-grade EA than in low-grade EA (P<0.05). MTRasym (3.5 ppm) and D were independent predictors for the histological grade of EA. The combination of MTRasym (3.5 ppm) and D were better able to identify high- and low-grade EA than was each parameter. MTRasym (3.5 ppm) and α were moderately and weakly positively correlated, respectively, with histological grade and the Ki-67 index (r=0.528, r=0.514, r=0.395, and r=0.367; P<0.05). D was moderately negatively correlated with histological grade and the Ki-67 index (r=-0.540 and r=-0.529; P<0.05). DDC was weakly and moderately negatively correlated with histological grade and the Ki-67 index, respectively (r=-0.473 and r=-0.515; P<0.05). ADC was weakly negatively correlated with histological grade and the Ki-67 index (r=-0.417 and r=-0.427; P<0.05). f was weakly positively correlated with histological grade and the Ki-67 index (r=0.294 and r=0.355; P<0.05). Our study found that both multimodel DWI and APTWI could be used to estimate the histological grade and Ki-67 index of EA, and the combination of high MTRasym (3.5 ppm) and low D may be an effective imaging marker for predicting the grade of EA.

Comparison of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted MR imaging models in differentiating hepatic hemangiomas from liver metastases

PurposeThis study aims to compare the diagnostic values of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) in differentiating hepatic hemangiomas and liver metastases. MethodThis prospective study was approved by our institutional review board, and written informed consent was obtained from all patients. In this study, 244 patients with known or suspected liver disease underwent magnetic resonance imaging. Among them, 37 patients who had focal hepatic lesions with a maximum diameter of ≥10 mm were evaluated. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for the three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from a stretched exponential model. The parameters were compared between hepatic hemangiomas and liver metastases. ResultsIn total, 64 focal hepatic lesions were evaluated, of which 22 were identified to be hepatic hemangiomas and 42 were liver metastases. ADC, D, f, and DDC values were significantly lower in liver metastases than in hepatic hemangiomas (P < 0.0001, < 0.0001, 0.015, and < 0.0001, respectively); whereas, the α value was significantly higher in liver metastases than in hepatic hemangiomas (P = 0.028). The areas under the ROC curve (AUCs) for differentiating hepatic hemangiomas and liver metastases in ADC, D, D*, f, DDC, and α were 0.940, 0.908, 0.608, 0.686, 0.952, and 0.667, respectively. The AUC values of ADC and DDC were significantly greater than those of D* (P < 0.0001), f (P = 0.0001), and α values (P = 0.0001). ConclusionADC and DDC values from the mono-exponential and stretched exponential models could be considered as quantitative imaging biomarkers for differentiating hepatic hemangiomas and liver metastases.

Amide Proton Transfer-Weighted Imaging and Multiple Models Diffusion-Weighted Imaging Facilitates Preoperative Risk Stratification of Early-Stage Endometrial Carcinoma.

Endometrial carcinoma (EC) risk stratification is generally based on histological assessment. It would be beneficial to perform risk stratification noninvasively by MRI. To investigate the application of amide proton transfer-weighted imaging (APTWI), monoexponential, biexponential, and stretched exponential intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for the evaluation of risk stratification in early-stage EC. Prospective. Eighty patients with early-stage EC (47 classified as low risk, 20 as medium risk, and 13 as high risk by histological grade and International Federation of Gynecology and Obstetrics stage). T1-weighted imaging, T2-weighted imaging, IVIM, APTWI, and DKI MRI at 3 T. The magnetization transfer ratio asymmetry (MTRasym [3.5 ppm]), apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index (α), mean kurtosis (MK), and mean diffusivity (MD) were calculated and compared between low-risk and non-low-risk groups. Individual sample t test, analysis of variance, and logistic regression. A P-value <0.05 was considered statistically significant. The α, ADC, D, DDC, and MD were significantly higher and the f, MK, and MTRasym (3.5 ppm) were significantly lower in the low-risk group than in the non-low-risk group. The difference in D* between the two groups was not significant (P=0.289). MTRasym (3.5 ppm), D, and MK were independent predictors of risk stratification. The combination of these three parameters was better able to identify low- and non-low-risk groups than each individual parameter. The IVIM, DKI, and APTWI parameters have potential as imaging markers for risk stratification in early-stage EC. 2 TECHNICAL EFFICACY: Stage 3.

Prediction of false-negative extramural venous invasion in patients with rectal cancer using multiple mathematical models of diffusion-weighted imaging

PurposeTo investigate the parameters from mono-exponential, stretched-exponential, and intravoxel incoherent motion diffusion-weighted imaging (DWI) models for evaluating false-negative extramural venous invasion (EMVI) on conventional magnetic resonance imaging (MRI) in rectal cancer patients. Material and methodsSeventy-two rectal cancer patients with negative EMVI on conventional MRI who underwent direct surgical resection were enrolled in this prospective study. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) values within the whole tumor were obtained to identify the patients with false-negative EMVI. Receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic performance. Multivariate binary logistic regression analysis was conducted to determine the independent risk factors. ResultsThe DDC, D*, f, and α values were significantly different in the EMVI-positive and EMVI-negative groups (P = 0.018, and P < 0.001, respectively). The D*, f, and α values demonstrated good diagnostic performance with area under the ROC curve (AUC) of 0.861, 0.824, and 0.854, respectively. The combined model, including D*, α, and tumor location, proved superior diagnostic performance with the AUC, sensitivity, specificity, and accuracy of 0.971, 0.917, 0.967, and 0.931, respectively. The AUC of the combined model was significantly higher than that of the D*, f, and DDC (P = 0.004, 0.045, and 0.002, respectively). ConclusionMulti-b-value DWI may be a potential tool for identifying micro-EMVI in rectal cancer. The combination of DWI parameters and tumor location leads to superior diagnostic performance.

Comparing mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted MR imaging for stratifying non-alcoholic fatty liver disease in a rabbit model

To compare diffusion parameters obtained from mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) in stratifying non-alcoholic fatty liver disease (NAFLD). Thirty-two New Zealand rabbits were fed a high-fat/cholesterol or standard diet to obtain different stages of NAFLD before 12 b-values (0-800s/mm2) DWI. The apparent diffusion coefficient (ADC) from the mono-exponential model; pure water diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f) from bi-exponential DWI; and distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from stretched-exponential DWI were calculated for hepatic parenchyma. The goodness of fit of the three models was compared. NAFLD severity was pathologically graded as normal, simple steatosis, borderline, and non-alcoholic steatohepatitis (NASH). Spearman rank correlation analysis and receiver operating characteristic curves were used to assess NAFLD severity. Upon comparison, the goodness of fit chi-square from stretched-exponential fitting (0.077 ± 0.012) was significantly lower than that for the bi-exponential (0.110 ± 0.090) and mono-exponential (0.181 ± 0.131) models (p < 0.05). Seven normal, 8 simple steatosis, 6 borderline, and 11 NASH livers were pathologically confirmed from 32 rabbits. Both α and D increased with increasing NAFLD severity (r = 0.811 and 0.373, respectively; p < 0.05). ADC, f, and DDC decreased as NAFLD severity increased (r = - 0.529, - 0.717, and - 0.541, respectively; p < 0.05). Both α (area under the curve [AUC] = 0.952) and f (AUC = 0.931) had significantly greater AUCs than ADC (AUC = 0.727) in the differentiation of NASH from borderline or less severe groups (p < 0.05). Stretched-exponential DWI with higher fitting efficiency performed, as well as bi-exponential DWI, better than mono-exponential DWI in the stratification of NAFLD severity. • Stretched-exponential diffusion model fitting was more reliable than the bi-exponential and mono-exponential diffusion models (p = 0.039 and p < 0.001, respectively). • As NAFLD severity increased, the diffusion heterogeneity index (α) increased, while the perfusion fraction (f) decreased (r = 0.811, - 0.717, p < 0.05). • Both α and f showed superior NASH diagnostic performance (AUC = 0.952, 0.931) compared with ADC (AUC = 0.727, p < 0.05).

Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading. Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance. The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (α) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between α and the tumor grades (p < 0.01). The parameter of α yielded a diagnostic accuracy of 85.3%, the combination of MK and α yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and α were more accurate (94.2%) in predicting tumor grade. The most accurate parameters were rCBV, MK, and α in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.

Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging

To prospectively evaluate and compare the potential of various diffusion metrics obtained from mono-exponential model (MEM), bi-exponential model (BEM), and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in the grading of meningiomas. Consecutive 93 patients with histopathologically confirmed meningiomas received DWI of multiple b-values. Apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (alpha), and distributed diffusion coefficient (DDC) were calculated and compared between low-grade and high-grade meningiomas. Receiver operating characteristic and multivariable stepwise logistic regression analyses were performed to evaluate the diagnostic performance of different parameters. The mean and normalised ADC, D, f, and DDC values were significantly lower in high-grade meningiomas than those in low-grade meningiomas (all p<0.05). The AUCs of D and DDC were significantly higher than that of f in the differentiation (all p<0.05). D was the only variable that could be used to independently differentiate high-grade and low-grade meningiomas (p<0.001). Different models of DWI, including MEM, BEM, and SEM, are useful in the differentiating high-grade and low-grade meningiomas; however, D obtained from BEM is the most promising diffusion parameter for predicting the grade of meningiomas.

Monoexponential, Biexponential, and stretched-exponential models using diffusion-weighted imaging: A quantitative differentiation of breast lesions at 3.0T.

Diffusion-weighted imaging (DWI) plays an important role in the differentiation of malignant and benign breast lesions. To investigate the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched-exponential DWI models in the differential diagnosis of breast lesions. Prospective. Sixty-one patients (age range: 25-68 years old; mean age: 46 years old) with 31 malignant lesions, 42 benign lesions, and 28 normal breast tissues diagnosed initially by clinical palpation, ultrasonography, or conventional mammography were enrolled in the study from January to September 2016. 3.0T MR scanner, T1 WI, T2 WI, DWI (conventional and multi-b values), dynamic contrast-enhanced. The apparent diffusion coefficient (ADC) was calculated by monoexponential analysis. The diffusion coefficient (ADCslow ), pseudodiffusion coefficient (ADCfast ), and perfusion fraction (f) were calculated using the biexponential model. The distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were obtained using a stretched-exponential model. All parameters were compared for malignant tumors, benign tumors, and normal breast tissues. A receiver operating characteristic curve was used to compare the ability of these parameters, in order to differentiate benign and malignant breast lesions. All statistical analyses were performed using statistical software (SPSS). ADC, ADCslow , f, DDC, and α values were significantly lower in malignant tumors when compared with normal breast tissues and benign tumors (P < 0.05). However, ADC and f had higher area under the receiver operating characteristic curve (AUC) values (0.889 and 0.919, respectively). The parameters derived from the biexponential and stretched-exponential DWI could provide additional information for differentiating between benign and malignant breast tumors when compared with conventional diffusion parameters. 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:1461-1467.

Application of stretched exponential diffusion-weighted imaging model in quantitative diagnosis of nonalcoholic fatty liver disease: in a rabbit model

Objective: To evaluate the feasibility of diffusion parameters obtained from mono-exponential and stretched exponential diffusion-weighted imaging models in staging of nonalcoholic fatty liver disease and compare the diagnosis ability of nonalcoholic steatohepatitis. Methods: Thirty-two 12 -week-old male New Zealand rabbits, about 2.5 kg of each, were randomly divided into five groups. To obtained different severity groups of NAFLD, a variety of diet (from standard to high-fat, high cholesterol chow) were feed for different periods before liver diffusion imaging was performed by using 3.0 T MR imaging system (Discovery 750W GE health care), the value of ADC, stretched exponential model parameters distributed diffusion coefficient (DDC) and α (water molecular diffusion heterogeneity index) were measured. Liver specimens were obtained for pathological grading (NAFLD activity scoring system). The diffusion parameters of each group of NAFLD were compared by ANOVA, LSD-t test was utilized to pairwise comparison between different grades of NAFLD. Spearman rank correlation analysis was used to evaluate the correlation between ADC, DDC, α and different severity groups of NAFLD. ROC curve was compared to evaluate the diagnostic efficiency of MR parameters for NASH. Results: α was significant different between borderline and NASH groups (0.65±0.05 vs 0.72±0.07; P<0.05); ADC and DDC showed no statistical difference between borderline and NASH groups (ADC:(1.09±0.14)×10(-3) vs (1.04±0.24)×10(-3) mm(2)/s; DDC: (0.73±0.08)×10(-3) vs (0.66±0.19)×10(-3) mm(2)/s; P>0.05); ADC and DDC were negatively correlated with NAFLD, and the correlation coefficients were -0.552, -0.596, respectively (P<0.05). α was found to be positively correlated with advancement of NAFLD, the correlation coefficient was 0.729 (P<0.05).In terms of the diagnostic efficiency of NASH, the AUC of ADC, DDC and α were 0.736, 0.784, 0.900, respectively, α was significantly greater than ADC in diagnosis of NASH (P<0.05). Conclusion: Stretched exponential model parameter α (water molecular diffusion heterogeneity index) could be utilized to identify NAFLD. α may provide more information and improve the staging of NASH compared with conventional diffusion parameters.

Comparison of the Diagnostic Value of Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MRI in Differentiating Tumor Stage and Histological Grade of Bladder Cancer

We aimed to determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging (DWI) models in differentiating tumor stage and grade of bladder cancer. Forty-five patients with pathologically confirmed bladder cancer underwent multi-b-value DWI. An apparent diffusion coefficient (ADC) was calculated from DWI by using a monoexponential model. A true diffusion coefficient (D), perfusion-related pseudo-diffusion coefficient (D*), and perfusion fraction (f) were calculated from DWI by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from DWI by using a stretched exponential model. All parameters were compared between different stages and grades by using the Mann-Whitney U test. Receiver operating characteristic and intrareader correlation coefficient analysis were used for statistical evaluations. ADC, D, f, and DDC values were significantly higher in the non-muscle-invasive vs muscle-invasive bladder cancers (P = .000, .000, .002, and .000, respectively) and in low-grade vs high-grade ones (P = .000, .000, .018, and .000, respectively). D* value was significantly lower in the low-grade bladder cancers compared to high-grade ones (P = .012). The areas under the receiver operating characteristic curve of ADC, D, and DDC values were 0.945, 0.912, and 0.946 in staging bladder cancers; 0.866, 0.862, and 0.856 in grading bladder cancers, respectively. Biexponential and stretched exponential DWI models may provide more parameters in staging and grading bladder cancers and show a slight difference between DDC and ADC values in staging bladder cancers. These two DWI models, as well as the monoexponential models, were very helpful in staging and grading bladder cancers.

Monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models: Quantitative biomarkers for differentiating renal clear cell carcinoma and minimal fat angiomyolipoma.

To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC). One hundred thirty-one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi-b value DWI (0∼1700 s/mm2 ) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion-weighted images by using a monoexponential model. A pseudo-ADC (Dp ), true ADC (Dt ), and perfusion fraction (fp ) were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion-weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations. ADC, Dt , and α values were significantly lower in the MFAML group than in the ccRCC group (P < 0.001). Dp , fp , and DDC values were slightly higher in the MFAML group than in the ccRCC group; however, the difference was not significant (P = 0.136, 0.090, and 0.424, respectively). The AUC values for both α (0.953) and Dt (0.964) were significantly higher than those for ADC (0860), Dp (0.605), fp (0.596), and DDC (0.477) in the differentiation of MFAML from ccRCC (P < 0.001). Water molecular diffusion heterogeneity index (α) and Dt may provide additional information and could lead to improved differentiation with better sensitivity and specificity between MFAML and ccRCC compared with conventional diffusion parameters. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:240-247.

Monoexponential, biexponential and stretched-exponential models based diffusion weighted imaging: a comparative study in the differential diagnosis of benign and malignant breast lesions

Objective To investgate the value of various parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models in the differential diagnosis of breast lesions. Methods A retrospective study performed in 54 patients with pathologically confirmed malignant tumors(n=30), benign lesions(n=34) and normal fibroglandular tissues (n=30). All patients underwent T1WI, T2WI, dynamic enhancement and diffusion weighted MRI with multi-b values at a 3.0 T magnetic resonance imaging unit. All parameters were measured at a workstation. ADC was calculated by using monoexponential analysis(b=0, 800 s/mm2). Slow apparent diffusion coefficient (ADC- slow), fast apparent diffusion coefficient (ADC- fast), and perfusion fraction (f) were calculated using the biexponential model. Distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were obtained from diffusion-weighted images using the stretched exponential model. All parameters were statistically compared among normal fibroglandular tissues, benign lesions and malignant tumors using Kruskal- Wallis rank sum test. Mann- Whitney U test were used for further comparisons between specific group pairs. ADC values were compared with ADC-slow and DDC for different groups by paired Wilcoxon test. Correlations between ADC-value, ADC-slow and DDC were assessed by using Spearman rank correlation coefficient. Receiver operating characteristic curve was used to analyze and compare the ability of these parameters in differentiation of benign and malignant breast lesions. Results The ADC, ADC-slow, f, DDC and α values were significantly different among the normal fibroglandular tissues, benign tumors and malignant tumors (P<0.05). Further differential comparisons of the four parameters between each pair showed that the ADC, ADC-slow, f, DDC and α values were significantly lower in malignant tumors than both in normal fibroglandular tissues and benign tumors(P<0.016 7). The ADC values of normal fibroglandular tissues, benign tumors and malignant tumors were significantly higher than ADC- slow and DDC (P<0.05). The f and DDC had higher area under the receiver operating characteristic curve (0.688 and 0.657 respectively). The optimal cutoff values for ADC, ADC- slow, f, DDC and α were 1.235 × 10-3mm2/s, 0.428×10-3mm2/s, 57.8%, 1.175×10-3mm2/s and 0.721, respectively. The α value showed higher specificity (65.5% ) and the f value had higher sensitivity (82.9%). Conclusion The parameters derived from biexponential and stretched exponential DWI could be helpful for differentiation between benign and malignant breast tumors. Key words: Breast neoplasms; Magnetic resonance imaging; Comparative study

Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging.

To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas. This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations. ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P < .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P < .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P < .05). Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.