IVIM Model Research Articles

Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers.

BackgroundDespite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the “fast” compartment, non-rigid registration, and exclusion voxels with poor fit quality. MethodsFrom June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence (b-values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm2). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f; diffusion coefficient, D; and pseudo-diffusion coefficient, D*), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations. ResultsAll results are reported for f, D, D*, and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (−0.01 ± 0.03) × 10−3 mm2/s, (0.70 ± 10.40) × 10−3 mm2/s, and (−0.02 ± 0.04) × 10−3 mm2/s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10−3 mm2/s, (−0.01 ± 0.21) × 10−3 mm2/s, (−13.37 ± 56.19) × 10−3 mm2/s, and (−0.01 ± 0.16) × 10−3 mm2/s respectively. Except for parameter D* that was associated with between-subjects parameter variability (P = 0.009), there was no significant variability between subjects, examinations, or readers. ConclusionWith our approach, IVIM parameters f, D, D*, and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.

Placental multimodal MRI prior to spontaneous preterm birth

To utilise combined diffusion-relaxation MRI techniques to interrogate antenatal changes in the placenta prior to extreme preterm birth among both women with PPROM and membranes intact, and compare this to a control group who subsequently delivered at term. Observational study. Tertiary Obstetric Unit, London, UK. Cases: pregnant women who subsequently spontaneously delivered a singleton pregnancy prior to 32 weeks' gestation without any other obstetric complications. pregnant women who delivered an uncomplicated pregnancy at term. All women consented to an MRI examination. A combined diffusion-relaxation MRI of the placenta was undertaken and analysed using fractional anisotropy, a combined T2*-apparent diffusion coefficient model and a combined T2*-intravoxel incoherent motion model, in order to provide a detailed placental phenotype associated with preterm birth. Subgroup analyses based on whether women in the case group had PPROM or intact membranes at time of scan, and on latency to delivery were performed. Fractional anisotropy, apparent diffusion coefficients and T2* placental values, from two models including a combined T2*-IVIM model separating fast- and slow-flowing (perfusing and diffusing) compartments. This study included 23 women who delivered preterm and 52 women who delivered at term. Placental T2* was lower in the T2*-apparent diffusion coefficient model (p < 0.001) and in the fast- and slow-flowing compartments (p = 0.001 and p < 0.001) of the T2*-IVIM model. This reached a higher level of significance in the preterm prelabour rupture of the membranes group than in the membranes intact group. There was a reduced perfusion fraction among the cases with impending delivery. Placental diffusion-relaxation reveals significant changes in the placenta prior to preterm birth with greater effect noted in cases of preterm prelabour rupture of the membranes. Application of this technique may allow clinically valuable interrogation of histopathological changes before preterm birth. In turn, this could facilitate more accurate antenatal prediction of preterm chorioamnionitis and so aid decisions around the safest time of delivery. Furthermore, this technique provides a research tool to improve understanding of the pathological mechanisms associated with preterm birth invivo.

Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis

Background: The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis. Materials and Methods: This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles. Results: Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model’s curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%–91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%–97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%–91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%–90.91%], perfusion-related diffusion [D*] 65% [48.47%–81.53%] and perfusion fraction [f] 80.36% [64.23%–96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92–0.97), for DKI parameters (DK 0.94 [0.89–0.99] and K 0.93 [0.90–0.96]) and for IVIM parameters (DIVIM 0.85 [0.80–0.91], D* 0.60 [0.43–0.77] and f 0.73 [0.63–0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM–kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%–93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%–79.48%), respectively. Conclusion: Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC.

The influence of b-values, noise levels, range of parameters, and ROI characteristics on the bi-exponential IVIM model and its fitting methods

The influence of b-values, noise levels, range of parameters, and ROI characteristics on the bi-exponential IVIM model and its fitting methods

Two-Compartment Perfusion MR IVIM Model to Investigate Normal and Pathological Placental Tissue.

Perfusion and diffusion coexist in the placenta and can be altered by pathologies. The two-perfusion model, where f1 and, f2 are the perfusion-fraction of the fastest and slowest perfusion compartment, respectively, and D is the diffusion coefficient, may help differentiate between normal and impaired placentas. Investigate the potential of the two-perfusion IVIM model in differentiating between normal and abnormal placentas. Retrospective, case-control. 43 normal pregnancy, 9 fetal-growth-restriction (FGR), 6 small-for-gestational-age (SGA), 4 accreta, 1 increta and 2 percreta placentas. Diffusion-weighted-echo planar imaging sequence at 1.5 T. Voxel-wise signal-correction and fitting-controls were used to avoid overfitting obtaining that two-perfusion model fitted the observed data better than the IVIM model (Akaike weight: 0.94). The two-perfusion parametric-maps were quantified from ROIs in the fetal and maternal placenta and in the accretion zone of accreta placentas. The diffusion coefficient D was evaluated using a b ≥ 200 sec/mm2 -mono-exponential decay fit. IVIM metrics were quantified to fix f1 + f2 = fIVIM . ANOVA with Dunn-Sidák's post-hoc correction and Cohen's d test were used to compare parameters between groups. Spearman's coefficient was evaluated to study the correlation between variables. A P-value<0.05 indicated a statistically significant difference. There was a significant difference in f1 between FGR and SGA, and significant differences in f2 and fIVIM between normal and FGR. The percreta + increta group showed the highest f1 values (Cohen's d = -2.66). The f2 between normal and percreta + increta groups showed Cohen's d = 1.12. Conversely, fIVIM had a small effective size (Cohen's d = 0.32). In the accretion zone, a significant correlation was found between f2 and GA (ρ = 0.90) whereas a significant negative correlation was found between fIVIM and D (ρ = -0.37 in fetal and ρ = -0.56 in maternal side) and f2 and D (ρ = -0.38 in fetal and ρ = -0.51 in maternal side) in normal placentas. The two-perfusion model provides complementary information to IVIM parameters that may be useful in identifying placenta impairment. 2 TECHNICAL EFFICACY STAGE: 1.

Improving microstructural integrity, interstitial fluid, and blood microcirculation images from multi-b-value diffusion MRI using physics-informed neural networks in cerebrovascular disease.

To obtain better microstructural integrity, interstitial fluid, and microvascular images from multi-b-value diffusion MRI data by using a physics-informed neural network (PINN) fitting approach. Test-retest whole-brain inversion recovery diffusion-weighted images with multiple b-values (IVIM: intravoxel incoherent motion) were acquired on separate days for 16 patients with cerebrovascular disease on a 3.0T MRI system. The performance of the PINN three-component IVIM (3C-IVIM) model fitting approach was compared with conventional fitting approaches (i.e., non-negative least squares and two-step least squares) in terms of (1) parameter map quality, (2) test-retest repeatability, and (3) voxel-wise accuracy. Using the in vivo data, the parameter map quality was assessed by the parameter contrast-to-noise ratio (PCNR) between normal-appearing white matter and white matter hyperintensities, and test-retest repeatability was expressed by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The voxel-wise accuracy of the 3C-IVIM parameters was determined by 10,000 computer simulations mimicking our in vivo data. Differences in PCNR and CV values obtained with the PINN approach versus conventional fitting approaches were assessed using paired Wilcoxon signed-rank tests. The PINN-derived 3C-IVIM parameter maps were of higher quality and more repeatable than those of conventional fitting approaches, while also achieving higher voxel-wise accuracy. Physics-informed neural networks enable robust voxel-wise estimation of three diffusion components from the diffusion-weighted signal. The repeatable and high-quality biological parameter maps generated with PINNs allow for visual evaluation of pathophysiological processes in cerebrovascular disease.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T.

The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value. Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm2 ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm2 ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times. Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2p = 100 ms) and 60 % (T2p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm2 was around 8% lower than at b = 0 s/mm2 (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01). The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

Diagnostic Efficiency of Diffusion Sequences and a Clinical Nomogram for Detecting Lymph Node Metastases from Rectal Cancer

First, to evaluate and compare three different diffusion sequences (i.e., standard DWI, IVIM, and DKI) for nodal staging. Second, to combine the DWI, and anatomic information to assess metastatic lymph node (LN). We retrospectively identified 136 patients of rectal adenocarcinoma who met the inclusion criteria. Three diffusion sequences (i.e., standard DWI, IVIM, and DKI) were performed, and quantitative parameters were evaluated. Univariate and multivariate analyses were used to assess the associations between the anatomic and DWI information and LN pathology. Multivariate logistic regression was used to identify independent risk factors. A nomogram model was established, and the model performance was evaluated by the concordance index (c-index) and calibration curve. There was a statistical difference in variables (LN long diameter, LN short diameter, LN boundary, LN signal, peri-LN signal intensity, ADC-1000, ADC-1400, ADC-2000, Kapp and D) between metastatic and non-metastatic LN for training and validation cohorts (p < 0.05). The ADC value derived from b=1000 mm/s (ADC-1000) showed the relative higher AUC (AUC=0.780) than the ADC value derived from b=1400 mm/s (ADC-1400) (AUC=0.703). The predictive accuracy of the nomogram measured by the c-index was 0.854 and 0.812 in the training and validation cohort, respectively. The IVIM and DKI model's diagnostic efficiency was not significantly improved compared to conventional DWI. The diagnostic accuracy of metastatic LN can be enhanced using the nomogram model, leading to a rational therapeutic choice.

An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging

The intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging (IVIM-DWI) with a series of images with different b-values has great potential as a tool for detecting, diagnosing, staging, and monitoring disease progression or the response to treatment. The current clinical tumour characterisation using IVIM-DWI is based on the parameter values derived from the IVIM model. On the one hand, the calculation accuracy of such parameter values is susceptible to deviations due to noise and motion; on the other hand, the performance of the parameter values is rather limited with respect to tumour characterisation. In this article, we propose a deep learning approach to directly extract spatiotemporal features from a series of b-value images of IVIM-DWI using a deep learning network for lesion characterisation. Specifically, we introduce an attention mechanism to select dominant features from specific b-values, channels, and spatial areas of the multiple b-value images for better lesion characterisation. The experimental results for clinical hepatocellular carcinoma (HCC) when using IVIM-DWI demonstrate the superiority of the proposed deep learning model for predicting the microvascular invasion (MVI) of HCC. In addition, the ablation study reflects the effectiveness of the attention mechanism for improving MVI prediction. We believe that the proposed model may be a useful tool for the lesion characterisation of IVIM-DWI in clinical practice.

Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging.

ObjectivesTo explore the differences between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion-weighted imaging (DWI) in evaluating the histopathological characters of pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective study enrolled 50 patients with PDAC confirmed by pathology from December 2018 to May 2020. All patients underwent DWI and IVIM-DWI before surgeries. Patients were classified into low- and high-fibrosis groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), false diffusion coefficient (D*), and perfusion fraction (f) were measured by two radiologists, respectively in GE AW 4.7 post-processing station, wherein ADC values were derived by mono-exponential fits and f, D, D* values were derived by biexponential fits. The tumor tissue was stained with Sirius red, CD34, and CK19 to evaluate fibrosis, microvascular density (MVD), and tumor cell density. Furthermore, the correlation between ADC, D, D*, and f values and histopathological results was analyzed.ResultsThe D values were lower in the high-fibrosis group than in the low-fibrosis group, while the f values were opposite. Further, no statistically significant differences were detected in ADC and D* values between the high- and low-fibrosis groups. The AUC of D and f values had higher evaluation efficacy in the high- and low-fibrosis groups than ADC values. A significant negative correlation was established between D values, and fibrosis and a significant positive correlation were observed between f values and fibrosis. No statistical difference was detected between DWI/IVIM parameters values and MVD or tumor cell density except for the positive correlation between D* values and tumor cell density.ConclusionsD and f values derived from the IVIM model had higher sensitivity and diagnostic performance for grading fibrosis in PDAC compared to the conventional DWI model. IVIM-DWI may have the potential as an imaging biomarker for predicting the fibrosis grade of PDAC.

Development of imaging biomarker for esophageal cancer using intravoxel incoherent motion MRI.

Intravoxel incoherent motion MRI (IVIM-MRI) can quantify micro-perfusion at the capillary level in the tissue. The purpose of this study is to measure tumor perfusion using IVIM-MRI, and evaluate its value as a biomarker to predict prognosis in esophageal squamous cell carcinoma (ESCC) patients. 109 ESCC patients (93 men and 16 women; median age: 72) who underwent IVIM-MRI prior to treatment between February 2018 and August 2020 were retrospectively investigated. Both mean apparent diffusion coefficient (ADC) value and mean perfusion-related parameter (PP) value of the primary tumor were measured using three b values of 0, 400, and 1000s/mm2 based on the IVIM model. We analyzed associations of these parameters with clinical stage and disease-specific survival (DSS). Lower ADC and PP values of the tumor were significantly associated with the higher clinical T stage (p < 0.0001, p < 0.0001, respectively). In Kaplan-Meier analyses, patients with lower PP value tumors (< 18.94, median) had significantly worse DSS (p < 0.0001), while tumor ADC value did not show a significant correlation with DSS. In a multivariate analysis, PP value of the tumor was an independent prognostic factor for DSS (p = 0.0027). Quantification of tumor perfusion using IVIM-MRI can be a non-invasive prognostic biomarker of ESCC, reflecting clinical stage and survival.

Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model.

PurposeTo compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC).MethodsFifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed.ResultsThere were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05).ConclusionsThe mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC.

Renal MRI Diffusion: Experimental Protocol.

Renal diffusion-weighted imaging (DWI) can be used to obtain information on the microstructure of kidney tissue, and has the potential to provide MR-biomarkers for functional renal imaging. Here we describe in a step-by-step experimental protocol the MRI method for measuring renal diffusion coefficients in rodents using ADC or IVIM models. Both methods provide quantification of renal diffusion coefficients; however, IVIM, a more complex model, allows for the calculation of the pseudodiffusion and fraction introduced by tissue vascular and tubular components. DWI provides information of renal microstructure contributing to the understanding of the physiology and the underlying processes that precede the beginning of pathologies.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.

The value of multiparametric histogram features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for the differential diagnosis of liver lesions.

BackgroundThe present study analyzed whole-lesion histogram parameters from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to explore the clinical value of IVIM histogram features in the differentiation of liver lesions.MethodsIn this retrospective study, 33 cases of hepatic hemangioma (HH), 22 cases of hepatic cysts (HC), and 34 cases of hepatocellular carcinoma (HCC) were underwent IVIM-DWI (b =0–600 s/mm2), which were confirmed pathologically and clinically. The data were processed by IVIM model to obtain the following quantitative indicators: perfusion fraction (f), slow diffusion coefficient (D), and pseudo-diffusion coefficient (or fast diffusion coefficient, D*). The region of interest in the largest solid part of the lesion was delineated for histogram analysis of the correlation between tissue image and lesion type. The relevant histogram parameters were obtained and statistically analyzed. The characteristic histogram parameters for HH, HC, and HCC were compared to find significantly different parameters. The diagnostic efficacies of these parameters for HH, liver cysts, and HCC were assessed using the receiver operating characteristic (ROC) curves.ResultsThere were significant differences in the maximum diameter, maximum value, minimum value, mean, median, standard deviation, uniformity, skewness, kurtosis, volume, 10th percentile (P10) of D, and 90th percentile (P90) of D between the three groups (P<0.05). The maximum diameter, minimum value, entropy, and volume of D* differed significantly between the three groups (P<0.05). The maximum diameter, minimum value, mean, median, skewness, kurtosis, volume, P10, and P90 of f differed significantly between the three groups (P<0.05). The largest area under the ROC curve (AUC) for both D* and f was that of volume (AUC =0.883 for both). When 1438.802 was used as the volume cut-off, the sensitivity and specificity of volume in differentiating between HH and HC were 87.88 and 77.27, respectively, and the sensitivity and specificity of volume in differentiating between HC and HCC were 77.27 and 85.29.ConclusionsA multiparametric histogram from IVIM-DWI magnetic resonance imaging (MRI) is an effective means of identifying HH, HC, and HCC that provides valuable reference information for clinical diagnosis.

OP10.11: Placental perfusion assessed by MR IVIM model and its correlation with placental volume in pregnancies at term complicated by fetal growth restriction

OP10.11: Placental perfusion assessed by MR IVIM model and its correlation with placental volume in pregnancies at term complicated by fetal growth restriction

Diffusion tensor imaging of the abdominal organs: Influence of oriented intravoxel flow compartments.

Water flow in partially oriented intravoxel compartments mimics an anisotropic fast-diffusion regime, which contributes to the signal attenuation in diffusion-weighted images. In the abdominal organs, this flow may reflect physiological fluid movements (eg, tubular urine flow in kidneys, or bile flow through the liver) and have a clinical relevance. This study investigated the influence of anisotropic intravoxel water flow on diffusion tensor imaging (DTI) of the abdominal organs. Diffusion-weighted images were acquired in five healthy volunteers using an EPI sequence with diffusion preparation (TR/TE: 1000ms/71ms; b-values: 0, 10, 20, 40, 70, 120, 250, 450, 700, 1000s/mm2 ; 12 noncollinear diffusion-encoding directions). DTI of liver and kidneys was performed assuming (i) monoexponential decay of the diffusion-weighted signal, and (ii) accounting for potential anisotropy of the fast-diffusion compartments using a tensorial generalization of the IVIM model. Additionally, potential dependency of the metrics of the tensors from the anatomical location was evaluated. Significant differences in the metrics of the diffusion tensor (DT) were found in both liver and kidneys when comparing the two models. In both organs, the trace and the fractional anisotropy of the DT were significantly higher in the monoexponential model than when accounting for perfusion. The comparison of areas of the liver proximal to the hilum with distal regions and of renal cortex with the medulla also proved a location dependency of the size of the fast-diffusion compartments. Pseudo-diffusion correction in DTI enables the assessment of the solid parenchyma regardless of the organ perfusion or other pseudo-diffusive fluid movements. This may have a clinical relevance in the assessment of parenchymal pathologies (eg, liver fibrosis). The fast pseudo-diffusion components present a detectable anisotropy, which may reflect the hepatic microcirculation or other sources of mesoscopic fluid movement in the abdominal organs.

Fetal brain development at 25-39 weeks gestational age: A preliminary study using intravoxel incoherent motion diffusion-weighted imaging.

The fetal brain developmental changes of water diffusivity and perfusion has not been extensively explored. To evaluate the fetal brain developmental changes of water diffusivity and perfusion using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). Prospective. Seventy-nine normal singleton fetuses were scanned without sedation of healthy pregnant women. 5 T MRI/T1 /2 -weighted image and IVIM-DWI. Pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values were calculated in the frontal (FWM), temporal (TWM), parietal (PWM), and occipital white matter (OWM) as well as cerebellar hemisphere (CH), basal ganglia region (BGR), thalamus (TH), and pons using an IVIM model. One-way analysis of variable (ANOVA) followed by Bonferroni post-hoc multiple comparison was employed to reveal the difference of IVIM parameters among the investigated brain regions. The linear and the nonlinear polynomial regression analyses were utilized to reveal the correlation between gestational age (GA) and IVIM parameters. There were significant differences in both D (F(7,623) = 96.64, P = 0.000) and f values (F(7,623) = 2.361, P = 0.0219), but not D* values among the varied brain regions. D values from TWM (r2 = 0.1402, P = 0.0002), PWM (r2 = 0.2245, P = 0.0002), OWM (r2 = 0.2519, P = 0.0002), CH (r2 = 0.2245, P = 0.0002), BGR (r2 = 0.3393, P = 0.0001), TH (r2 = 0.1259, P = 0.0001), and D* value from pons (r2 = 0.2206, P = 0.0002) were significantly correlated with GA using linear regression analysis. Quadratic regression analysis led to results similar to those using the linear regression model. IVIM-DWI parameters may indicate fetal brain developmental alterations but the conclusion is far from reached due to the not as high-powered correlation between IVIM parameters and GA. 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:899-909.

Determination of Hepatocellular Carcinoma and Characterization of Hepatic Focal Lesions with Adaptive Multi-Exponential Intravoxel Incoherent Motion Model

PURPOSE: To distinguish hepatocellular carcinoma (HCC) from other types of hepatic lesions with the adaptive multi-exponential IVIM model. METHODS: 94 hepatic focal lesions, including 38 HCC, 16 metastasis, 12 focal nodular hyperplasia, 13 cholangiocarcinoma, and 15 hemangioma, were examined in this study. Diffusion-weighted images were acquired with 13 b values (b = 0, 3, …, 500 s/mm2) to measure the adaptive multi-exponential IVIM parameters, namely, pure diffusion coefficient (D), diffusion fraction (fd), pseudo-diffusion coefficient (Di*) and perfusion-related diffusion fraction (fi) of the ith perfusion component. Comparison of the parameters of and their diagnostic performance was determined using Mann-Whitney U test, independent-sample t test, one-way analysis of variance, Z test and receiver-operating characteristic analysis. RESULTS: D, D1* and D2* presented significantly difference between HCCs and other hepatic lesions, whereas fd, f1 and f2 did not show statistical differences. In the differential diagnosis of HCCs from other hepatic lesions, D2* (AUC, 0.927) provided best diagnostic performance among all parameters. Additionally, the number of exponential terms in the model was also an important indicator for distinguishing HCCs from other hepatic lesions. In the benign and malignant analysis, D gave the greatest AUC values, 0.895 or 0.853, for differentiation between malignant and benign lesions with three or two exponential terms. Most parameters were not significantly different between hypovascular and hypervascular lesions. For multiple comparisons, significant differences of D, D1* or D2* were found between certain lesion types. CONCLUSION: The adaptive multi-exponential IVIM model was useful and reliable to distinguish HCC from other hepatic lesions.

Comparison of methods for estimation of the intravoxel incoherent motion (IVIM) diffusion coefficient (D) and perfusion fraction (f)

ObjectiveIntravoxel incoherent motion (IVIM) shows great potential in many applications, e.g., tumor tissue characterization. To reduce image-quality demands, various IVIM analysis approaches restricted to the diffusion coefficient (D) and the perfusion fraction (f) are increasingly being employed. In this work, the impact of estimation approach for D and f is studied.Materials and methodsFour approaches for estimating D and f were studied: segmented IVIM fitting, least-squares fitting of a simplified IVIM model (sIVIM), and Bayesian fitting of the sIVIM model using marginal posterior modes or posterior means. The estimation approaches were evaluated in terms of bias and variability as well as ability for differentiation between tumor and healthy liver tissue using simulated and in vivo data.ResultsAll estimation approaches had similar variability and ability for differentiation and negligible bias, except for the Bayesian posterior mean of f, which was substantially biased. Combined use of D and f improved tumor-to-liver tissue differentiation compared with using D or f separately.DiscussionThe similar performance between estimation approaches renders the segmented one preferable due to lower numerical complexity and shorter computational time. Superior tissue differentiation when combining D and f suggests complementary biologically relevant information.

Depiction of the perfusion components’ volume fraction distribution in generalized intravoxel incoherent motion by using Gaussian mixture model

AbstractGaussian mixture model (GMM) was proposed to depict the perfusion volume fraction distribution in the generalized intravoxel incoherent motion model (GIVIM) to improve GIVIM's ability of describing complex perfusion conditions and their changes. Different hepatic perfusion conditions were accounted for by performing different combinations of imaging sequence and diffusion time on six normal livers. In order to evaluate GIVIM‐GMM's reliability in perfusion condition analysis, the fitting to diffusion‐weighted (DW) data and the consistency between diffusion‐related parameters’ change and the data's change were tested and the recent GIVIM and the triexponential models were chosen for comparison. The difference of the fitting results was evaluated by performing the extra‐sum‐of‐squares F test and information criteria on normal human DW data. The difference of the consistency was assessed by using two‐tailed paired Student's t test. In the extra‐sum‐of‐squares F test, the relative difference ratio F values derived from the GIVIM and GIVIM‐GMM and that derived from the triexponential model and the GIVIM‐GMM are respectively 25.334 and 27.976, which indicates that significant difference existed and that the GIVIM‐GMM provides better fit to the normal human liver DW data. In information criteria test, the evidence ratio values were determined by dividing the GIVIM's or triexponential model's correct probability by the GIVIM‐GMM's. Both evidence ratio values (2.3942 × 10−10, 8.6167 × 10−9, respectively) are much smaller than 1, which also expresses that the best model used to fit the normal human liver DW data was the GIVIM‐GMM. In two‐tailed paired student's t test, the GIVIM‐GMM provides more parameters to give a finer description of perfusion than the triexponential model or GIVIM. In short, all the results demonstrated that the GIVIM‐GMM provides better performance than the existing IVIM models for depicting the signal attenuation in DW imaging.