Perfusion Fraction Research Articles

Role of intravoxel incoherent motion (IVIM) imaging in response assessment of hepatocellular carcinoma after transarterial chemoembolization (TACE) – A prospective study

IntroductionResponse evaluation of hepatocellular carcinoma (HCC) currently is based on arterial phase enhancement which doesn’t take into microstructural changes in the tumor after trans-arterial chemoembolization (TACE). AimThis prospective study was conducted to assess the feasibility and efficacy of intravascular incoherent motion imaging (IVIM) in response evaluation of HCC after TACE.39 cirrhotic patients with 48 HCC underwent MR imaging 1 week within and 6weeks after TACE. IVIM parameters like Dslow (true diffusion), Dfast (pseudodiffusion), perfusion fraction and ADC were measured prior to and postTACE. The pre and post TACE values in LR-TR (LIRADS – treatment response) nonviable and viable lesions were compared using paired t-tests. ROC curve analysis was done to calculate sensitivity and specificity and propose cut-off values. ResultNon-viable lesions showed a significant increase in Dslow (1.208 ± 0.581 vs 1.560 ± 0.494, P-value −.0207) and ADC (1.37 ± 0.53 vs 1.65 ± 0.4287, P value .016) after TACE. There was also significant decrease in Dfast (33.7 ± 10.4 vs 23.75 ± 12.13, P value .0005) and f (19.92 ± 10.54 vs 12.9 ± 10.41, P value .012) values after TACE in non-viable lesions compared to viable lesions. The change in true diffusion had the highest AUC (0.741) among IVIM parameters with greater than 0.075 increase between preTACE and postTACE values having a sensitivity and specificity of 81.8% and 60% respectively for complete response. ConclusionIVIM imaging is feasible to assess the response in HCC after TACE. True diffusion is more sensitive and specific than apparent diffusion in evaluating the response.

Histopathological graded liver lesions: what role does the IVIM analysis method have?

This study aims to investigate three different image processing methods on quantitative parameters of IVIM sequence, as well as apparent diffusion coefficients and simple perfusion fractions, for benign and malignant liver tumors. IVIM images with 8 b-values (0-1000s/mm2) and 1.5T MRI scanner in 16 patients and 3 healthy people were obtained. Next, the regions of interest were selected for malignant, benign, and healthy liver regions (50, 56, and 12, respectively). Then, the bi-exponential equation of the IVIM technique was fitted with two segmented fitting methods as well as one full fitting method (three methods in total). Using the segmented fitting method, diffusion coefficient (D) is fixed with a mono-exponential equation with b-values that are greater than 200s/mm2. The perfusion fraction (f) can then be calculated by extrapolating, as the first method, or fitting simultaneously with the pseudo-diffusion coefficient (D*) as the second method. In the full fitting method, as the third method, all IVIM parameters were obtained simultaneously. The mean values of parameters from different methods were compared in different grades of lesions. Our results indicate that the image processing method can change statistical comparisons between different groups for each parameter. The D value is the only quantity in this technique that does not depend on the fitting process and can be used as an indicator of comparison between studies (P < 0.05). The most effective method to distinguish liver lesions is the extrapolated f method (first method). This method created a significant difference (P < 0.05) between the perfusion parameters between benign and malignant lesions. Using extrapolated f is the most effective method of distinguishing liver lesions using IVIM parameters. The comparison between groups does not depend on the fitting method only for parameter D.

Intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging for neoadjuvant chemotherapy response evaluation in patients with osteosarcoma

ObjectivesThis study aims to explore the role of quantitative intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in characterizing changes in osteosarcoma (OS) patients receiving neoadjuvant chemotherapy (NACT). Material and methodsTwenty-seven patients with histologically proven OS were examined prospectively and divided into good-response group (n = 14) and poor-response group (n = 13). IVIM and DCE-MRI sequences were performed at baseline (pre-NACT) and after three cycles of NACT (post-NACT). Apparent diffusion coefficient (ADC) and IVIM bi-exponential model parameters, including diffusion coefficient (D-Bi), perfusion coefficient (D*-Bi), and perfusion fraction (f-Bi), were evaluated. DCE-MRI parameters, including quantitative parameters (volume transfer constant [Ktrans], elimination rate constant [Kep], and extravascular extracellular space volume ratio [Ve]) and semi-quantitative parameters (initial area under the gadolinium curve [IAUGC] and contrast enhancement rate [CER]), were also measured. ResultsD-Bi, D*-Bi, and f-Bi post-NACT and ΔD-Bi were statistically different between the good- and poor-response groups (Z1 = − 3.348, Z2 = − 2.572, Z3 = − 2.378, t = 2.235, P < 0.05). ADC, f-Bi, Ktrans, IAUGC, Kep, and CER post-NACT were statistically different from those at pre-NACT (P < 0.05). The receiver operating characteristic curve showed that f-Bi post-NACT had the best performance among all parameters, with area under the curve of 0.769, sensitivity of 1, and specificity of 0.538. The correlation analysis showed that the efficacy of NACT was negatively correlated with D-Bi, D*-Bi post-NACT, and ΔD-Bi (r1 = − 0.530, r2 = − 0.411, r3 = − 0.434, P1 = 0.008, P2 = 0.046, P3 = 0.034) and significantly positively correlated with f-Bi post-NACT (r = 0.482, P = 0.017). ConclusionsThe IVIM quantitative parameters D-Bi, D*-Bi, and f-Bi post-NACT and ΔD-Bi could be used as noninvasive imaging biomarkers for early response assessment of NACT in OS.

Image Improved Intravoxel Incoherent Motion MRI With Optimized Trigger Delays Based on Strain Curve Analysis to Evaluate Myocardial Microvascular Dysfunction of Exertional Heat Illness.

Intravoxel incoherent motion (IVIM) MRI has not been widely used and its role in evaluating exertional heat illness (EHI)-related myocardial involvement remains unknown. To investigate the feasibility of strain curve-derived trigger delay (TD) IVIM-MRI and its role in assessing myocardial diffusion and microvascular perfusion of EHI patients. Prospective. A total of 42 male EHI patients (median age: 21 years) and 22 age- and sex-matched healthy controls (HC). A 3-T, diffusion-weighted spin-echo echo-planar-imaging sequence. IVIM-MRI was acquired by conventional TD method (group A) or strain curve-based TD method (group B) in random order. IVIM image quality was evaluated on a 3-point Likert scale (1, nondiagnostic; 2, moderate; 3, good). Technical success was defined as image quality score=3. IVIM-MRI-derived parameters (pseudo diffusion in the capillaries [D*], perfusion fraction [f], and slow apparent diffusion coefficient [D]) were compared between EHI and HC. Student's t-tests, chi-square tests, one-way analysis of variance, receiver operating characteristic (ROC) curve analysis, Pearson's correlation coefficient (r). The statistical significance level was set at P < 0.05. IVIM-MRI image quality score (median [interquartile range]: 3 [2, 3] vs. 2 [1-3]) and technical success rate (61.9%[13/21] vs. 28.6%[6/21]) were significantly improved in group B. EHI patients showed significantly decreased D* (118.1 ± 23.3 × 10-3 mm2 /sec vs. 142.7 ± 42.6× 10-3 mm2 /sec) and f values (0.42 ± 0.12 vs. 0.51 ± 0.11) and significantly higher D values (3.0 ± 0.9× 10-3 mm2 /sec vs. 2.5 ± 0.6× 10-3 mm2 /sec) compared to HC. Relative to D and D*, f showed the most robust efficacy for detecting EHI-related myocardial injury with the highest area under the ROC curve (0.906: 95% confidence interval, 0.799, 0.967) and sensitivity of 88.5% and specificity of 85.6%. The strain curve-based TD method significantly improved image quality and technical success rate of IVIM-MRI, and f value may be an effective biomarker to assess myocardial microcirculation abnormalities of EHI patients. 2. Stage 3.

Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study.

To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas. Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades. Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r = - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006). IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas. • Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Microstructural and Microvascular Alterations in Psychotic Spectrum Disorders: A Three-Compartment Intravoxel Incoherent Imaging and Free Water Model.

Microvascular and inflammatory mechanisms have been hypothesized to be involved in the pathophysiology of psychotic spectrum disorders (PSDs). However, data evaluating these hypotheses remain limited. We applied a three-compartment intravoxel incoherent motion free water imaging (IVIM-FWI) technique that estimates the perfusion fraction (PF), free water fraction (FW), and anisotropic diffusion of tissue (FAt) to examine microvascular and microstructural changes in gray and white matter in 55 young adults with a PSD compared to 37 healthy controls (HCs). We found significantly increased PF, FW, and FAt in gray matter regions, and significantly increased PF, FW, and decreased FAt in white matter regions in the PSD group versus HC. Furthermore, in patients, but not in the HC group, increased PF, FW, and FAt in gray matter and increased PF in white matter were significantly associated with poor performance on several cognitive tests assessing memory and processing speed. We additionally report significant associations between IVIM-FWI metrics and myo-inositol, choline, and N-acetylaspartic acid magnetic resonance spectroscopy imaging metabolites in the posterior cingulate cortex, which further supports the validity of PF, FW, and FAt as microvascular and microstructural biomarkers of PSD. Finally, we found significant relationships between IVIM-FWI metrics and the duration of psychosis in gray and white matter regions. The three-compartment IVIM-FWI model provides metrics that are associated with cognitive deficits and may reflect disease progression.

Intravoxel incoherent motion imaging used to assess tumor microvascular changes after transarterial chemoembolization in a rabbit VX2 liver tumor model.

To evaluate the correlation between microvascular density (MVD) and intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) parameters and the effect of glycolytic flux after transarterial chemoembolization (TACE) in a rabbit VX2 liver tumor. VX2 liver tumor allografts in 15 New Zealand white rabbits were treated with sterile saline (control group, n = 5) or lipiodol-doxorubicin emulsion (experimental group, n = 10). MRI was performed 2 weeks after the procedure to evaluate IVIM parameters, including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF). All animal samples were taken of the tumor and surrounding liver. Immunostaining for CD31, CD34, CD105, and VEGF was used to evaluate MVD. The protein expression of Glut4, HK2, PKM2, LDHA, and MCT1 was determined using western blotting. Pearson correlation tests were used to analyze the relationship between MVD and IVIM parameters. D* value in the peritumoral region was negatively correlated with CD34 (r = -0.71, P = 0.01). PF value positively correlated with CD34 (r = 0.68, P = 0.015), CD105 (r = 0.76, P = 0.004) and VEGF (r = 0.72, P = 0.008) in the peritumoral region. Glut4, HK2, PKM2, and MCT1 in the peritumoral regions were higher in the experimental group than in the control group (all P < 0.05). IVIM parameters were correlated with MVD in the intratumoral and peritumoral regions after TACE in a rabbit liver tumor model. The angiogenesis reflected by MVD may be related to changes of glycolytic flux.

Utility of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) to predict prognosis and survival risk in laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy

PurposeTo investigate the predictive power of mono-exponential, bi-exponential, and stretched exponential signal models of intravoxel incoherent motion (IVIM) in prognosis and survival risk of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) patients after chemoradiotherapy.Materials and methodsForty-five patients with laryngeal or hypopharyngeal squamous cell carcinoma were retrospectively enrolled. All patients had undergone pretreatment IVIM examination, subsequently, mean apparent diffusion coefficient (ADCmean), maximum ADC (ADCmax), minimum ADC (ADCmin) and ADCrange (ADCmax − ADCmean) by mono-exponential model, true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f) by bi-exponential model, distributed diffusion coefficient (DDC), and diffusion heterogeneity index (α) by stretched exponential model were measured. Survival data were collected for 5 years.ResultsThirty-one cases were in the treatment failure group and fourteen cases were in the local control group. Significantly lower ADCmean, ADCmax, ADCmin, D, f, and higher D* values were observed in the treatment failure group than in the local control group (p < 0.05). D* had the greatest AUC of 0.802, with sensitivity and specificity of 77.4 and 85.7% when D* was 38.85 × 10–3 mm2/s. Kaplan–Meier survival analysis showed that the curves of N stage, ADCmean, ADCmax, ADCmin, D, D*, f, DDC, and α values were significant. Multivariate Cox regression analysis showed ADCmean and D* were independently correlated with progression-free survival (PFS) (hazard ratio [HR] = 0.125, p = 0.001; HR = 1.008, p = 0.002, respectively).ConclusionThe pretreatment parameters of mono-exponential and bi-exponential models were significantly correlated with prognosis of LHSCC, ADCmean and D* values were independent factors for survival risk prediction.

Placental perfusion using intravoxel incoherent motion MRI combined with Doppler findings in differentiating between very low birth weight infants and small for gestational age infants

IntroductionAbnormalities in placental morphology and function can lead to small for gestational age infants (SGA) and very low birth weight infants (VLBWI). In this study, we explored the value of intravoxel incoherent motion (IVIM) histogram parameters, MRI morphological parameters, and Doppler findings of the placenta in differentiating between VLBWI and SGA. MethodsThirty-three pregnant women who were diagnosed with SGA and met the inclusion criteria were enrolled in this retrospective study and divided into two groups: 22 with non-VLBWI and 11 with VLBWI. The IVIM histogram parameters (perfusion fraction (f), true diffusion coefficient (D), and pseudo-diffusion coefficient (D*)), MRI morphological parameters, and Doppler findings were compared between groups. The diagnostic efficiency was compared by receiver operating characteristic (ROC) curve analysis. ResultsThe Dmean, D90th, D*90th, fmax, and placental area of the VLBWI group were significantly lower than those of the non-VLBWI group (p < 0.05). The values of umbilical artery pulsatility index, resistance index (RI), and peak systolic velocity/end-diastolic velocity were significantly higher in the VLBWI group than in the non-VLBWI group (p < 0.05). D90th, placental area, and umbilical artery RI had the highest areas under the curve (AUCs) of the ROC curves, at 0.787, 0.785, and 0.762, respectively. A combined predictive model (D90th, placental area, and umbilical artery RI) improved the performance in differentiating between VLBWI and SGA compared with the single model (AUC = 0.942). DiscussionIVIM histogram (D90th) and MRI morphological (placental area) parameters and a Doppler finding (umbilical artery RI) may be sensitive indicators for differentiating between VLBWI and SGA.

Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology.

To investigate the diagnostic performance of a multiparametric magnetic resonance imaging (MRI) protocol comprising quantitative MRI (diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) perfusionMRI) and conventional MRI in the characterization of gallbladder wall thickening (GWT). This prospective study comprised consecutive adults with GWT who underwent multiparametric MRI between July 2020 and April 2022. Two radiologistsevaluated the MRI independently. The final diagnosis was based on surgical histopathology. The association of MRI parameters with malignant GWT was evaluated. The area under the curve (AUC) for the quantitative MRI parameters and diagnostic performance of conventional, and multiparametric MRI were compared. The interobserver agreementbetween two radiologists was calculated. Thirty-five patients (mean age, 56years, 23 females) with GWT (25 benign and ten malignant) were evaluated. The quantitative MRI parameters significantly associated with malignant GWT were apparent diffusion coefficient on DWI (p = 0.007) and mean diffusivity (MD) on DTI (p = 0.013), perfusion fraction (f) on IVIM (p = 0.033), time to peak enhancement (TTP, p = 0.008), and wash in rate (p = 0.049) on DCE-MRI. TTP had the highest AUC of 0.790, followed by MD (0.782) and f (0.742) (p = 0.213) for predicting malignant GWT. Multiparametric MRI had significantly higher sensitivity (90% vs. 80%, p = 0.045) than conventional MRI for diagnosing malignant GWT. The two radiologists' reading had substantial to near-perfect agreement (kappa = 0.639-1) and moderate to strong correlation (interclass correlation coefficient = 0.5-0.88). Multiparametric protocol incorporating advanced sequences improved the diagnostic performance of MRI for differentiating benign and malignant GWT. • Multiparametric MRI had 90% sensitivity and 88% specificity for diagnosing malignant GWT, compared to 80% sensitivity and 88% specificity for conventional CE-MRI. • Among the quantitative MRI parameters, TTP (perfusion-MRI) had the highest AUC of 0.790, followed by MD (0.782) and IVIM-f (0.742). • For most quantitative MRI parameters, there was moderate to strong agreement (ICC = 0.5-0.88).

The Use of Diffusion Weighted Imaging and Intravoxel Incoherent Motion to Assess Edema and Perfusion in Abdominal Organs during Endotoxemia

Diffusion weighted magnetic resonance imaging (DW-MRI) technique probing the microscopic movement of water molecules in tissue, allows for the quantification of water diffusivity and the contribution of perfusion to the total amount of water. In the last decades, its use in organs other than the brain has increased and it has successfully been applied to abdominal organs; otherwise the use of DWI for the assessment of perfusion (by measuring perfusion fraction and edema (by measuring the apparent diffusion coefficient—ADC) of entire organs is not a clinical standard. The aim of this paper is to assess whether DWI, specifically IVIM, can accurately assess edema (the amount of water contained in a tissue) and perfusion in abdominal organs (liver, spleen, intestine and kidneys) in an endotoxemic porcine model. Endotoxemia was induced in 20 piglets and they were divided into three groups: HM group (HighMAP, mean arterial pressure was maintained &gt;65 mmHg by noradrenaline infusion), LM group (LowMAP, MAP was maintained between 50 and 60 mmHg) and LTD (the thoracic duct was ligated to increase abdominal edema). In addition, a fourth group of healthy controls (four animals) underwent MRI. Edema was also assessed by wet–dry weight in liver, spleen and intestine; and perfusion was assessed by hemodynamics, lactate concentration and SvO2. ADC was found to be higher in the intestine of the LTD group compared with the other groups, in accordance with wet–dry weight. In addition, ADC in kidneys was found to be correlated to glomerular filtration rate. f was correlated with hemodynamics in kidneys and liver. The presented data show that there is a potential for clinical use of the technique in septic patients.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion in differentiating invasive placentas.

To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in distinguishing invasive placentas. A total of 53 patients with invasive placentas and 47 patients with noninvasive placentas undergoing conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were retrospectively enrolled. The mean, minimum, and maximum parameters including the apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, diffusion kurtosis (MK), and diffusion coefficient (MD) from DKI and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured and compared from the volumetric analysis. Receiver operating characteristics (ROC) curve and logistic regression analyses were conducted to evaluate the diagnostic efficiency of different diffusion parameters for distinguishing invasive placentas. Comparisons between accreta lesions in patients with invasive placentas (AL) and lower 1/3 part of the placenta in patients with noninvasive placentas (LP) demonstrated that MD mean, D mean, and D* mean were significantly lower while ADC max and D max were significantly higher in invasive placentas (all p < 0.05). Multivariate analysis demonstrated that D mean, D max and D* mean differed significantly among all the studied parameters for invasive placentas. A combined use of these three parameters yielded an AUC of 0.86 with sensitivity, specificity, and accuracy of 84.91%, 76.60%, and 80%, respectively. The combined use of different IVIM parameters is helpful in distinguishing invasive placentas.

IVIM-DKI with parametric reconstruction method for lymph node evaluation and characterization in lymphoma: A preliminary study comparison with FDG-PET/CT

To investigate IVIM-DKI in diagnosing benign and malignant lymph nodes in lymphoma and its subtypes in comparison to FDG-PET/CT. A total of twenty-one (n = 21) patients diagnosed with biopsy-proven Hodgkin lymphoma(HL: n = 13) and non-Hodgkin lymphoma (NHL: n = 8) were prospectively evaluated. All patients underwent MRI(T1-weighted, Short-Tau-Inversion-Recovery (STIR)), and IVIM-DKI was acquired with 9b-values (0–2000s/mm2) at 1.5T and whole-body FDG-PET/CT. The maximum and average standard uptake values (SUVmax and SUVmean) were calculated using PET images. IVIM-DKI parameters (diffusion coefficient(D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and kurtosis (k)) were estimated using IVIM-DKI model with total variation (TV) method (IDTV model). Area-under-curve from receiver-operating-curve (ROC) analysis was used to examine diagnostic value of IVIM-DKI parameters in differentiation between benign and malignant lymph nodes and HL from NHL. Machine learning-based classification of histogram features were performed using linear classifier model. For malignant vs. benign lymph nodes, apparent diffusion coefficient (ADC), f, and k were significantly (p < 0.05) lower in malignant vs. benign lymph nodes. f (AUC:0.88) and k (AUC:0.83) showed high AUC and histogram features combination of f (variance + skewness + kurtosis) showed highest accuracy of 97.2% and AUC of 1. ADC, D, D*, and f were significantly (p < 0.05) lower in NHL than HL. D* showed highest AUC of 0.85 than D (AUC:0.84), ADC (AUC:0.84), and f (AUC:0.74) in NHL vs. HL. SUVmax (spearman-rho = 0.85), ADC (spearman-rho = 0.50), D (spearman-rho = 0.48), and D* (spearman-rho = 0.49) showed significant (p < 0.05) positive correlation with SUVmean. Multi-b-values IVIM-DKI with histogram analysis helps characterize benign and malignant lymph nodes in lymphomas. IVIM-DKI parameters can differentiate malignant lymph nodes in HL and NHL superior to PET parameters.

Intravoxel incoherent motion and diffusion kurtosis imaging at 3T MRI: Application to ischemic stroke

Background and purposeThe DKI-IVIM model that incorporates DKI (diffusional kurtosis imaging) into the IVIM (Intravoxel Incoherent Motion) concept was investigated to assess its utility for both enhanced diffusion characterization and perfusion measurements in ischemic stroke at 3 T. MethodsFifteen stroke patients (71 ± 11 years old) were enrolled and DKI-IVIM analysis was performed using 9 b-values from 0 to 1500 s/mm2 chosen with the Cramer-Rao-Lower-Bound optimization approach. Pseudo-diffusion coefficient D*, perfusion fraction f, blood flow-related parameter fD*, the diffusion coefficient D and an additional parameter, the kurtosis, K were determined in the ischemic lesion and controlateral normal tissue based on a region of interest approach. The apparent diffusion coefficient (ADC) and arterial spin labelling (ASL) cerebral blood flow (CBF) parameters were also assessed and parametric maps were obtained for all parameters. ResultsSignificant differences were observed for all diffusion parameters with a significant decrease for D (p < 0.0001), ADC (p < 0.0001), and a significant increase for K (p < 0.0001) in the ischemic lesions of all patients. f decreased significantly in these regions (p = 0.0002). The fD* increase was not significant (p = 0.56). The same significant differences were found with a motion correction except for fD* (p = 0.47). CBF significantly decreased in the lesions. ADC was significantly positively correlated with D (p < 0.0001) and negatively with K (p = 0.0002); K was also negatively significantly correlated with D (p = 0.01). ConclusionsDKI-IVIM model enables for simultaneous cerebral perfusion and enhanced diffusion characterization in an acceptable clinically acquisition time for the ischemic stroke diagnosis with the additional kurtosis factor estimation, that may better reflect the microstructure heterogeneity.

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions.

Whole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions. To compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis. Fifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (Kapp) and kurtosis-corrected diffusion coefficient (Dapp) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance. The ADCmean, ADCmedian, D mean and D median values of lung cancer were significantly lower than those of inflammatory lesions, while the ADCskewness, Kapp mean, Kapp median, Kapp SD, Kapp kurtosis and Dapp skewness values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADCskewness (p = 0.019) and D median (p = 0.031) were identified as independent predictors of lung cancer. D median showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a D median of 1.091 × 10-3 mm2/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively. Whole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and D median shows the best performance in the differential diagnosis of solitary pulmonary lesions.

The quantification of IntraVoxel incoherent motion – MRI maps cannot preserve texture information: An evaluation based on simulated and in-vivo images

BackgroundRadiomics can be applied on parametric maps obtained from IntraVoxel Incoherent Motion (IVIM) MRI to characterize heterogeneity in diffusion and perfusion tissue properties. The purpose of this work is to assess the accuracy and reproducibility of radiomic features computed from IVIM maps using different fitting methods. Methods200 digitally simulated IVIM-MRI images with various SNR containing different combinations of texture patterns were generated from ground truth maps of true diffusion D, pseudo-diffusion D* and perfusion fraction f. Four different methods (segmented least-square LSQ, Bayesian, supervised and unsupervised deep learning DL) were adopted to quantify IVIM maps from simulations and from two real images of liver tumor. Radiomic features were computed from ground truth and estimated maps. Accuracy and reproducibility among quantification methods were assessed. ResultsAlmost 50% of radiomic features computed from D maps using DL approaches, 36% using Bayes and 27% using LSQ presented errors lower than 50%. Radiomic features from f and D* maps were accurate only if computed using DL methods from histogram. High reproducibility (ICC>0.8) was found only for D maps among DL and Bayes methods, whereas features from f and D* maps were less reproducible, with LSQ approach in lower agreement with the others. ConclusionsTexture patterns were preserved and correctly estimated only on D maps, except for LSQ approach. We suggest limiting radiomic analysis only to histogram and some texture features from D maps, to histogram features from f maps, and to avoid it on D* maps.

Synthetic-to-real domain adaptation with deep learning for fitting the intravoxel incoherent motion model of diffusion-weighted imaging.

Intravoxel incoherent motion (IVIM) is a type of diffusion-weighted imaging (DWI), and IVIM model parameters (water molecule diffusion rate Dt , pseudo-diffusion coefficient Dp , and tissue perfusion fraction Fp ) have been widely used in the diagnosis and characterization of malignant lesions. This study proposes a deep-learning model with synthetic-to-real domain adaptation to fit the IVIM model parameters of DWI. Ninety-eight consecutive patients diagnosed with hepatocellular carcinoma between January 2017 and September 2020 were included in the study, and routine IVIM-DWI serial examinations were performed using a 3.0T magnetic resonance imaging system in preoperative MR imaging. The proposed method is mainly composed of two modules: a convolutional neural network-based IVIM model fitting network to map b-value images to the IVIM parameter maps and a domain discriminator to improve the accuracy of the IVIM parameter maps in the real data. The proposed method was compared with previously reported fitting methods, including the nonlinear least squares (NLSs), IVIM-NEToptim , and self-supervised U-network methods. The IVIM parameter-fitting performance was assessed by measuring the DWI reconstruction performance and testing the robustness of each method against noise using noise-corrupted data. The DWI reconstruction performance demonstrates that the proposed method has better reconstruction accuracy for DWI with a low signal-to-noise ratio, which implies that the proposed method improves the fitting accuracy of the IVIM parameters. Noise-corrupt experiments show that the proposed method is more robust against noise-corrupted signals. With the proposed method, no outliers were found in Dt , and outliers were reduced for Fp in the abnormal regions (proposed method: 1.85%; NLS: 5.90%; IVIM-NEToptim : 6.61%; and self-U-net: 25.36%). Moreover, experiments show that the proposed method has a more stable parameter estimation performance than the existing methods in the absence of real data. IVIM parameters can be estimated using a synthetic-to-real domain-adaptation framework with deep learning, and the proposed method outperforms previously reported methods.

Effectiveness of functional magnetic resonance imaging for early identification of chronic kidney disease: A systematic review and network meta-analysis

PurposeThe commonly used clinical indicators are not sensitive enough on detecting early chronic kidney disease (CKD), whether functional magnetic resonance imaging (fMRI) can be regarded as a new noninvasive method to identify early stages of CKD and even different stages remains unknown. We performed a network meta-analysis to explore the question. MethodsFive databases were searched to identify eligible articles from 2000 to 2022. The outcome indicators were imaging biomarkers of fMRI techniques, including apparent diffusion coefficient (ADC) by diffusion-weighted imaging (DWI), fractional anisotropy (FA) by diffusion tensor imaging (DTI), diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) by intravoxel incoherent motion imaging (IVIM), and apparent relaxation rate (R2*) by blood oxygen level-dependent (BOLD). ResultsA total of 21 articles with 1472 patients were included for analysis. Cortical FA, f, and R2* values in CKD stages 1–2 were found statistically different with healthy controls (mean difference (MD), −0.03, 95% confidence interval (CI) −0.05, −0.01; MD, −0.04, 95% CI −0.06, −0.02; MD, 2.22, 95% CI 0.87, 3.57, respectively), and cortical ADC values were significantly different among different CKD stages (stages 3 and 1–2: MD, −0.15, 95% CI −0.23, −0.06; stages 4–5 and 3: MD −0.27, 95% CI −0.39, −0.14). ConclusionThe results indicated fMRI techniques had great efficacy in assessing early stages and different stages of CKD, among which DTI, IVIM, and BOLD exerted great superiority in differentiating early CKD patients from the general population, while DWI showed the advantage in distinguishing different CKD stages.

An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver &amp; Placenta in Fetal Growth Restriction

Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. The multiple aetiologies, coupled with the risk of fetal complications - encompassing neurodevelopmental delay, neonatal morbidity, and stillbirth - motivate the need to improve holistic assessment of the FGR fetus using MRI. We hypothesised that the fetal liver and placenta would provide insights into FGR biomarkers, unattainable through conventional methods. Therefore, we explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets (using a combined T2-diffusion scan) for 12 normally grown and 12 FGR gestational age (GA) matched pregnancies (Estimated Fetal Weight below 3rd centile, Median 28+/-3wks). We applied the Intravoxel Incoherent Motion Model, which describes circulatory properties of the fetal organs, and analysed the resulting features distinguishing both cohorts. We additionally used novel multi-compartment models for MRI fetal analysis, which exhibit potential to provide a multi-organ FGR assessment, overcoming the limitations of empirical indicators - such as abnormal artery Doppler findings - to evaluate placental dysfunction. The placenta and fetal liver presented key differentiators between FGR and normal controls, with significant decreased perfusion, abnormal fetal blood motion and reduced fetal blood oxygenation. This may be associated with the preferential shunting of the fetal blood towards the fetal brain, affecting supply to the liver. These features were further explored to determine their role in assessing FGR severity, by employing simple machine learning models to predict FGR diagnosis (100% accuracy in test data, n=5), GA at delivery, time from MRI scan to delivery, and baby weight. We additionally explored the use of deep learning to regress the latter three variables, training a convolutional neural network with our liver and placenta voxel-level parameter maps, obtained from our multi-compartment model fitting. Image texture analysis of the fetal organs demonstrated prominent textural variations in the placental perfusion fractions maps between the groups (p&lt;0.0009), and spatial differences in the incoherent fetal capillary blood motion in the liver (p&lt;0.009). This research serves as a proof-of-concept, investigating the effect of FGR on fetal organs, measuring differences in perfusion and oxygenation within the placenta and fetal liver, and their prognostic importance in automated diagnosis using simple machine learning models.

Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis.

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p &lt; 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.