Presentation of microstructural diffusion components by color schemes in abdominal organs

PURPOSE

Development of a color scheme representation to facilitate the interpretation of tri-exponential DWI data from abdominal organs, where multi-exponential behavior is more pronounced.

METHODS

Multi-exponential analysis of DWI data provides information about the microstructure of the tissue under study. The tri-exponential signal analysis generates numerous parameter images that are difficult to analyze individually. Summarized color images can simplify at-a-glance analysis. A color scheme was developed in which the slow, intermediate, and fast diffusion components were each assigned to a different red, green, and blue color channel. To improve the appearance of the image, histogram equalization, gamma correction, and white balance were used, and the processing parameters were adjusted. Examples of the resulting color maps of the diffusion fractions of healthy and pathological kidney and prostate are shown.

RESULTS

The color maps obtained by the presented method show the merged information of the slow, intermediate, and fast diffusion components in a single view. A differentiation of the different fractions becomes clearly visible. Fast diffusion regimes, such as in the renal hilus, can be clearly distinguished from slow fractions, such as in dense tumor tissue.

CONCLUSION

Combining the diffusion information from tri-exponential DWI analysis into a single color image allows for simplified interpretation of the diffusion fractions. In the future, such color images may provide additional information about the microstructural nature of the tissue under study.

Tri- and bi-exponential diffusion analyses of the kidney: effect of respiratory-controlled acquisition on diffusion parameters

This study examined whether respiratory-controlled acquisition influences diffusion parameters obtained with intravoxel incoherent motion (IVIM) analysis using tri-exponential and bi-exponential models. Ten healthy volunteers were examined on a 3.0 T MRI system to obtain coronal diffusion-weighted images of both kidneys. The participants were scanned twice using respiratory-triggering (RT) and free-breathing (FB) acquisition to assess the repeatability of the measurements. We determined mean signal intensities in the renal cortex at each b value. Then, perfusion-related diffusion coefficient (Dp), fast-free diffusion coefficient (Df), slow-restricted diffusion coefficient (Ds), and their corresponding fractions (Fp, Ff, and Fs, respectively) were calculated using tri-exponential function. Moreover, perfusion-related diffusion coefficient (D*), the fraction (F), and perfusion-independent diffusion coefficient (D) were calculated using bi-exponential function. Normalized root-mean-square errors for the tri- and bi-exponential analyses (nRMSEtri and nRMSEbi, respectively) were determined to assess the deviation of the fitted to measured data, i.e., the fitting accuracy. Additionally, repeatability coefficients (RCs) were calculated from Bland-Altman plots to evaluate the repeatability of each diffusion parameter. These values were compared between the RT and FB groups. Dp and D* in the RT group were significantly lower than those in the FB group (P < 0.05). In addition, the RT group showed significantly lower nRMSEtri and nRMSEbi values than those in the FB group (P < 0.05). Moreover, Dp, Ds, Fs, and D* at RT showed lower RC values than those at FB. Respiratory-controlled acquisition affects perfusion-related diffusion parameters of the kidney obtained using tri-exponential and bi-exponential analyses.

Triexponential Diffusion Analysis of Diffusion-weighted Imaging for Breast Ductal Carcinoma in Situ and Invasive Ductal Carcinoma

Purpose

To obtain detailed information in breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) using triexponential diffusion analysis.

Methods

Diffusion-weighted images (DWI) of the breast were obtained using single-shot diffusion echo-planar imaging with 15 b-values. Mean signal intensities at each b-value were measured in the DCIS and IDC lesions and fitted with the triexponential function based on a two-step approach: slow-restricted diffusion coefficient (D_s) was initially determined using a monoexponential function with b-values > 800 s/mm². The diffusion coefficient of free water at 37°C was assigned to the fast-free diffusion coefficient (D_f). Finally, the perfusion-related diffusion coefficient (D_p) was derived using all the b-values. Furthermore, biexponential analysis was performed to obtain the perfusion-related diffusion coefficient (D^*) and the perfusion-independent diffusion coefficient (D). Monoexponential analysis was performed to obtain the apparent diffusion coefficient (ADC). The sensitivity and specificity of the aforementioned diffusion coefficients for distinguishing between DCIS and IDC were evaluated using the pathological results.

Results

The D_s, D, and ADC of DCIS were significantly higher than those of IDC (P < 0.01 for all). There was no significant correlation between D_p and D_s, but there was a weak correlation between D^* and D. The combination of D_p and D_s showed higher sensitivity and specificity (85.9% and 71.4%, respectively), compared to the combination of D^* and D (81.5% and 33.3%, respectively).

Conclusion

Triexponential analysis can provide detailed diffusion information for breast tumors that can be used to differentiate between DCIS and IDC.

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

PURPOSE

To 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).

METHODS

Fifty-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.

RESULTS

There 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).

CONCLUSIONS

The 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.

Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations

Diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) have been explored to assess liver tumors and diffused liver diseases. IVIM reflects the microscopic translational motions that occur in voxels in magnetic resonance (MR) DWI. In biologic tissues, molecular diffusion of water and microcirculation of blood in the capillary network can be assessed using IVIM DWI. The most commonly applied model to describe the DWI signal is a bi-exponential model, with a slow compartment of diffusion linked to pure molecular diffusion (represented by the coefficient D_slow), and a fast compartment of diffusion, related to microperfusion (represented by the coefficient D_fast). However, high variance in D_fast estimates has been consistently shown in literature for liver IVIM, restricting its application in clinical practice. This variation could be explained by the presence of another very fast compartment of diffusion in the liver. Therefore, a tri-exponential model would be more suitable to describe the DWI signal. This article reviews the published evidence of the existence of this additional very fast diffusion compartment and discusses the performance and limitations of the tri-exponential model for liver IVIM in current clinical settings.

Spectral diffusion analysis of kidney intravoxel incoherent motion MRI in healthy volunteers and patients with renal pathologies

PURPOSE

To assess the feasibility of measuring tubular and vascular signal fractions in the human kidney using nonnegative least-square (NNLS) analysis of intravoxel incoherent motion data collected in healthy volunteers and patients with renal pathologies.

METHODS

MR imaging was performed at 3 Tesla in 12 healthy subjects and 3 patients with various kidney pathologies (fibrotic kidney disease, failed renal graft, and renal masses). Relative signal fractions f and mean diffusivities of the diffusion components in the cortex, medulla, and renal lesions were obtained using the regularized NNLS fitting of the intravoxel incoherent motion data. Test-retest repeatability of the NNLS approach was tested in 5 volunteers scanned twice.

RESULTS

In the healthy kidneys, the NNLS method yielded diffusion spectra with 3 distinguishable components that may be linked to the slow tissue water diffusion, intermediate tubular and vascular flow, and fast blood flow in larger vessels with the relative signal fractions, f_slow , f_interm and f_fast , respectively. In the pathological kidneys, the diffusion spectra varied substantially from those acquired in the healthy kidneys. Overall, the renal cyst showed substantially higher f_interm and lower f_slow , whereas the fibrotic kidney, failed renal graft, and renal cell carcinoma demonstrated the opposite trend.

CONCLUSION

NNLS-based intravoxel incoherent motion could potentially become a valuable tool in assessing changes in tubular and vascular volume fractions under pathophysiological conditions.

Advanced diffusion imaging of abdominal organs in different hydration states of the human body: stability of biomarkers

BACKGROUND

MR diffusion weighted imaging (DWI) may provide important information regarding the pathophysiology of parenchymal abdominal organs. The purpose of our study was to investigate the stability of imaging biomarkers of diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in abdominal parenchymal organs regarding two body hydration states.

METHODS

Ten healthy volunteers twice underwent DWI of abdominal organs using a double-refocused spin-echo echo-planar imaging sequences with 11 different b-values (ranging from 0 to 1,500 s/mm2): after 4 h of fluid deprivation; 45 min following 1000 ml of water intake. Four different diffusion models were evaluated and compared: standard DWI, DKI with mono-exponential fitting, multistep algorithm with variable b-value threshold for IVIM, combined IVIM-Kurtosis; in four abdominal organs: kidneys, liver, spleen and psoas muscle.

RESULTS

Diffusion parameters from all four models remained similar for the renal parenchyma before and after the water challenge. Significant differences were found for the liver, spleen, and psoas muscle. The largest effects were seen for: the liver parenchyma after the water challenge by means of IVIM model's true diffusion (p < 0.02); the spleen, for IVIM's perfusion fraction (p < 0.03), the psoas muscle for the ADC value (p < 0.02).

CONCLUSIONS

Herein, we showed that diffusion parameters of the kidney remain remarkably stable regarding the hydration status. This may be attributed to the kidney-specific compensatory mechanisms. For the liver, spleen and psoas muscle the diffusion parameters were sensitive to changes of the hydration. This phenomenon needs to be considered when evaluating diffusion data of these organs.

Diffusion-weighted imaging and texture analysis: current role for diffuse liver disease

Multiparametric MRI represents the primary imaging modality to assess diffuse liver disease, both in a qualitative and in a quantitative manner. Diffusion-weighted imaging (DWI) is among the imaging techniques that can be used to assess fibrosis due to its unique capability to assess microstructural changes at the tissue level. DWI is based on water mobility patterns and has the potential to become a non-invasive and non-destructive virtual biopsy to assess diffuse liver disease, overcoming sampling bias errors due to its three-dimensional imaging capabilities. Parallel to DWI, another quantitative method called texture analysis may be used to assess early and advanced diffused liver disease through quantifying spatial relationships in a global and local level, applying to any type of digital imaging technique like MRI or CT. Initial results using texture analysis hold great promise. In the current paper, we will review the role of DWI and texture analysis using MR images in assessing diffuse liver disease.

Progress of intravoxel incoherent motion diffusion-weighted imaging in liver diseases

Traditional magnetic resonance (MR) diffusion-weighted imaging (DWI) uses a single exponential model to obtain the apparent diffusion coefficient to quantitatively reflect the diffusion motion of water molecules in living tissues, but it is affected by blood perfusion. Intravoxel incoherent motion (IVIM)-DWI utilizes a double-exponential model to obtain information on pure water molecule diffusion and microcirculatory perfusion-related diffusion, which compensates for the insufficiency of traditional DWI. In recent years, research on the application of IVIM-DWI in the diagnosis and treatment of hepatic diseases has gradually increased and has achieved considerable progress. This study mainly reviews the basic principles of IVIM-DWI and related research progress in the diagnosis and treatment of hepatic diseases.

On the identification of the blood vessel confounding effect in intravoxel incoherent motion (IVIM) Diffusion-Weighted (DW)-MRI in liver: An efficient sparsity based algorithm

IntraVoxel Incoherent Motion (IVIM) Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is of great interest for evaluating tissue diffusion and perfusion and producing parametric maps in clinical applications for liver pathologies. However, the presence of macroscopic blood vessels (not capillaries) in a given Region of Interest (ROI) results in a confounding effect that bias the quantification of tissue perfusion. Therefore, it is necessary to identify those voxels affected by blood vessels. In this paper, an efficient algorithm for an automatic identification of blood vessels in a given ROI is proposed. It relies on the sparsity of the spatial distribution of blood vessels. This sparsity prior can be easily incorporated using the all-voxel IVIM-MRI model introduced in this paper. In addition to the identification of blood vessels, the proposed algorithm provides a quantification of blood vessels, tissue diffusion and tissue perfusion of all voxels in a given ROI, in one single step. Besides, two strategies are proposed in this paper to deal with the nonnegativity of the model parameters. The efficiency of the proposed algorithm compared to the Non-Negative Least Square (NNLS)-based method, recently introduced to deal with the confounding blood vessel effect in the IVIM-MRI model, is confirmed using both realistic and real DW-MR images.

Comparison of tri-exponential decay versus bi-exponential decay and full fitting versus segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI

OBJECTIVES

To determine whether bi- or tri-exponential models, and full or segmented fittings, better fit the intravoxel incoherent motion (IVIM) imaging signal of healthy livers.

METHODS

Diffusion-weighted images were acquired with a 3 T scanner using a respiratory-triggered echo-planar sequence and 16 b-values (0-800 s/mm² ). Eighteen healthy volunteers had their livers scanned twice in the same session, and then once in another session. Liver parenchyma region-of-interest-based measurements were processed with bi-exponential and tri-exponential models, with both full fitting and segmented fitting (threshold b-value = 200 s/mm² ).

RESULTS

With the signal of all scans averaged, bi-exponential model full fitting showed D_slow  = 1.14 × 10^-3  mm² /s, D_fast  = 193.6 × 10^-3  mm² /s, and perfusion fraction (PF) = 16.9%, and segmented fitting showed D_slow  = 0.98 × 10^-3  mm² /s, D_fast  = 42.2 × 10^-3  mm² /s, and PF = 23.3%. IVIM parameters derived from the tri-exponential model were similar for full fitting and segmented fitting, with slow (D'_slow  = 0.98 × 10^-3  mm² /s; F'_slow  = 76.4 or 76.6%), fast (D'_fast  = 15.1 or 15.4 × 10^-3  mm² /s; F'_fast  = 11.8 or 11.7%) and very fast (D'_Vfast  = 445.0 or 448.8 × 10^-3  mm² /s; F'_Vfast  = 11.8 or 11.7%) diffusion compartments. The tri-exponential model provided an overall better fit than the bi-exponential model. For the bi-exponential model, full fitting provided a better fit at very low and low b-values compared with segmented fitting, with the latter tending to underestimate D_fast ; however, the segmented method demonstrated lower error in signal prediction for high b-values. Compared with full fitting, tri-exponential segmented fitting offered better scan-rescan reproducibility.

CONCLUSION

For healthy liver, tri-exponential modeling is preferred to bi-exponential modeling. For the bi-exponential model, segmented fitting underestimates D_fast , but offers a more accurate estimation of D_slow .

Spectral Diffusion Analysis of Intravoxel Incoherent Motion MRI in Cerebral Small Vessel Disease

Background

Cerebral intravoxel incoherent motion (IVIM) imaging assumes two components. However, more compartments are likely present in pathologic tissue. We hypothesized that spectral analysis using a nonnegative least‐squares (NNLS) approach can detect an additional, intermediate diffusion component, distinct from the parenchymal and microvascular components, in lesion‐prone regions.

Purpose

To investigate the presence of this intermediate diffusion component and its relation with cerebral small vessel disease (cSVD)‐related lesions.

Study Type

Prospective cross‐sectional study.

Population

Patients with cSVD (n = 69, median age 69.8) and controls (n = 39, median age 68.9).

Field Strength/Sequence

Whole‐brain inversion recovery IVIM acquisition at 3.0T.

Assessment

Enlarged perivascular spaces (PVS) were rated by three raters. White matter hyperintensities (WMH) were identified on a fluid attenuated inversion recovery (FLAIR) image using a semiautomated algorithm.

Statistical Tests

Relations between IVIM measures and cSVD‐related lesions were studied using the Spearman's rank order correlation.

Results

NNLS yielded diffusion spectra from which the intermediate volume fraction f_int was apparent between parenchymal diffusion and microvasular pseudodiffusion. WMH volume and the extent of MRI‐visible enlarged PVS in the basal ganglia (BG) and centrum semiovale (CSO) were correlated with f_int in the WMHs, BG, and CSO, respectively. f_int was 4.2 ± 1.7%, 7.0 ± 4.1% and 13.6 ± 7.7% in BG and 3.9 ± 1.3%, 4.4 ± 1.4% and 4.5 ± 1.2% in CSO for the groups with low, moderate, and high number of enlarged PVS, respectively, and increased with the extent of enlarged PVS (BG: r = 0.49, P < 0.01; CSO: r = 0.23, P = 0.02). f_int in the WMHs was 27.1 ± 13.1%, and increased with the WMH volume (r = 0.57, P < 0.01).

Data Conclusion

We revealed the presence of an intermediate diffusion component in lesion‐prone regions of cSVD and demonstrated its relation with enlarged PVS and WMHs. In tissue with these lesions, tissue degeneration or perivascular edema can lead to more freely diffusing interstitial fluid contributing to f_int.

Level of Evidence: 2

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2020;51:1170–1180.

Intravoxel incoherent motion imaging has the possibility to detect liver abnormalities in young Fontan patients with good hemodynamics

INTRODUCTION

Liver fibrosis and cirrhosis are one of the critical complications in Fontan patients. However, there are no well-established non-invasive and quantitative techniques for evaluating liver abnormalities in Fontan patients. Intravoxel incoherent motion diffusion-weighted imaging with MRI is a non-invasive and quantitative method to evaluate capillary network perfusion and molecular diffusion. The objective of this study is to assess the feasibility of intravoxel incoherent motion imaging in evaluating liver abnormalities in Fontan children.

MATERIALS AND METHODS

Five consecutive Fontan patients and four age-matched healthy volunteers were included. Fontan patients were 12.8 ± 1.5 years old at the time of MRI scan. Intravoxel incoherent motion imaging parameters (D, D*, and f values) within the right hepatic lobe were compared. Laboratory test, ultrasonography, and cardiac MRI were also conducted in the Fontan patients. Results of cardiac catheterization conducted within one year of the intravoxel incoherent motion imaging were also examined.

RESULTS

In Fontan patients, laboratory test and liver ultrasonography showed almost normal liver condition. Cardiac catheter and MRI showed good Fontan circulation. Cardiac index was 2.61 ± 0.23 L/min/m2. Intravoxel incoherent motion imaging parameters D, D*, and f values were lower in Fontan patients compared with controls (D: 1.1 ± 0.0 versus 1.3 ± 0.2 × 10-3 mm2/second (p = 0.04), D*: 30.8 ± 24.8 versus 113.2 ± 25.6 × 10-3 mm2/second (p < 0.01), and f: 13.2 ± 3.1 versus 22.4 ± 2.4% (p < 0.01), respectively).

CONCLUSIONS

Intravoxel incoherent motion imaging is feasible for evaluating liver abnormalities in children with Fontan circulation.

Intravoxel Incoherent Motion (IVIM) Diffusion-Weighted Imaging (DWI) in Patients with Liver Dysfunction of Chronic Viral Hepatitis: Segmental Heterogeneity and Relationship with Child-Turcotte-Pugh Class at 3 Tesla

Background

Few studies focused on the region of interest- (ROI-) related heterogeneity of liver intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). The aim of the study was to evaluate the differences of liver IVIM parameters among liver segments in cirrhotic livers (chronic viral hepatitis).

Material and Methods

This was a retrospective study of 82 consecutive patients with chronic liver disease who underwent MRI examination at the Jinan Infectious Diseases Hospital between January 2015 and December 2016. IVIM DWI (seven different b values) was performed on a Siemens 3.0-T MRI scanner. Pure molecular diffusion (D), pseudodiffusion (D^∗), and perfusion fraction (f) in different liver segments were evaluated.

Results

f, D, and D^∗ were different among the liver segments (all p < 0.05), indicating heterogeneity in IVIM parameters among liver segments. f was consistently higher in Child-Turcotte-Pugh (CTP) class A compared with CTP class B + C (p < 0.01). D and D^∗ were higher in CTP class A compared with CTP class B + C (p < 0.05). In patients with mean f value of >0.29, the AUC was 0.88 (95% CI: 0.81-0.96), with 86.8% sensitivity and 81.8% specificity for predicting CTP class A from CTP class B + C.

Conclusion

Liver IVIM could be a promising method for classifying the severity of segmental liver dysfunction of chronic viral hepatitis as evaluated by the CTP class, which provides a noninvasive alternative for evaluating segmental liver dysfunction with accurate selection of ROIs. Potentially it can be used to monitor the progression of CLD and LC in the future.

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

We assessed parameters of advanced diffusion weighted imaging (DWI) models for the prediction of the tumor growth rate in 55 head and neck squamous cell carcinoma (HNSCC) patients. The DWI acquisition used single-shot spin-echo echo-planar imaging with 12 b-values (0−2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, tri-exponential, stretched exponential and diffusion kurtosis imaging models. We directly measured the tumor growth rate from two sets of different-date imaging data. We divided the patients into a discovery group (n = 40) and validation group (n = 15) based on their MR acquisition dates. In the discovery group, we performed univariate and multivariate regression analyses to establish the multiple regression equation for the prediction of the tumor growth rate using diffusion parameters. The equation obtained with the discovery group was applied to the validation group for the confirmation of the equation's accuracy. After the univariate and multivariate regression analyses in the discovery-group patients, the estimated tumor growth rate equation was established by using the significant parameters of intermediate diffusion coefficient D₂ and slow diffusion coefficient D₃ obtained by the tri-exponential model. The discovery group's correlation coefficient between the estimated and directly measured tumor growth rates was 0.74. In the validation group, the correlation coefficient (r = 0.66) and intra-class correlation coefficient (0.65) between the estimated and directly measured tumor growth rates were respectively good. In conclusion, advanced DWI model parameters can be a predictor for determining HNSCC patients’ tumor growth rate.

A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

Purpose: To present a new modified tri-exponential model for diffusion-weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models.

Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm² were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models.

Results: The mean f₀ values were ranging 11.9–18.7% in white matter ROIs and 1.2–2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p < 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADC_very−slow values were extremely low in white matter (1–7 × 10⁻⁶ mm²/s), but not in gray matter (251–445 × 10⁻⁶ mm²/s), indicating that the conventional tri-exponential model fails to represent a special compartment.

Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information.

The Effect of Feeding Behavior on Hypothalamus in Obese Type 2 Diabetic Rats with Glucagon-like Peptide-1 Receptor Agonist Intervention

OBJECTIVE

To investigate the utility of intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI) derived parameters in hypothalamus for monitoring the effect of Exendin-4 (Ex-4) intervention on the feeding behavior in obese diabetic rats within early feeding.

METHODS

21 obese and 19 non-obese rats which were treated with streptozotocin injections were initially divided into an obese diabetes group (OD, n = 10), a non-obese diabetes group (D, n = 8), an obese group (O, n = 9) and a non-obese group (N, n = 9). Then, the rats in the 4 groups received subcutaneous injections of Ex-4, and feeding behavior was examined at 5, 35, 65, 95, and 125 min. The hypothalamic function was evaluated by IVIM-DWI. Finally, the relationship between the hypothalamic function and the amount of food intake was analyzed.

RESULTS

In comparison with the N group, the food intake significantly decreased in the O , OD, and D groups in response to Ex-4. Furthermore, a significant positive correlation was found between food intake and D values at different times from 5 to 125 min after Ex-4 intervention in all 4 groups.

CONCLUSION

A direct correlation between the change of hypothalamic function and feeding behavior was detected in OD rats with Ex-4 intervention in the early feeding period. The hypothalamic D value derived from IVIM-DWI is promising to reflect the dynamic change of hypothalamic function due to intervention.

Is there evidence for more than two diffusion components in abdominal organs? - A magnetic resonance imaging study in healthy volunteers

The most commonly applied model for the description of diffusion-weighted imaging (DWI) data in perfused organs is bicompartmental intravoxel incoherent motion (IVIM) analysis. In this study, we assessed the ground truth of underlying diffusion components in healthy abdominal organs using an extensive DWI protocol and subsequent computation of apparent diffusion coefficient 'spectra', similar to the computation of previously described T₂ relaxation spectra. Diffusion datasets of eight healthy subjects were acquired in a 3-T magnetic resonance scanner using 68 different b values during free breathing (equidistantly placed in the range 0-1005 s/mm² ). Signal intensity curves as a function of the b value were analyzed in liver, spleen and kidneys using non-negative least-squares fitting to a distribution of decaying exponential functions with minimum amplitude energy regularization. In all assessed organs, the typical slow- and fast-diffusing components of the IVIM model were detected [liver: true diffusion D = (1.26 ± 0.01) × 10^-3 mm² /s, pseudodiffusion D* = (270 ± 44) × 10^-3 mm² /s; kidney cortex: D = (2.26 ± 0.07) × 10^-3 mm² /s, D* = (264 ± 78) × 10^-3 mm² /s; kidney medulla: D = (1.57 ± 0.28) × 10^-3 mm² /s, D* = (168 ± 18) × 10^-3 mm² /s; spleen: D = (0.91 ± 0.01) × 10^-3 mm² /s, D* = (69.8 ± 0.50) × 10^-3 mm² /s]. However, in the liver and kidney, a third component between D and D* was found [liver: D' = (43.8 ± 5.9) × 10^-3 mm² /s; kidney cortex: D' = (23.8 ± 11.5) × 10^-3 mm² /s; kidney medulla: D' = (5.23 ± 0.93) × 10^-3 mm² /s], whereas no third component was detected in the spleen. Fitting with a diffusion kurtosis model did not lead to a better fit of the resulting curves to the acquired data compared with apparent diffusion coefficient spectrum analysis. For a most accurate description of diffusion properties in the liver and the kidneys, a more sophisticated model seems to be required including three diffusion components.

Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging

OBJECTIVE

This study aimed to investigate the reliability of intravoxel incoherent motion (IVIM) model derived parameters D and f and their dependence on b value distributions with a rapid three b value acquisition protocol.

MATERIALS AND METHODS

Diffusion models for brain, kidney, and liver were assessed for bias, error, and reproducibility for the estimated IVIM parameters using b values 0 and 1000, and a b value between 200 and 900, at signal-to-noise ratios (SNR) 40, 55, and 80. Relative errors were used to estimate optimal b value distributions for each tissue scenario. Sixteen volunteers underwent brain DW-MRI, for which bias and coefficient of variation were determined in the grey matter.

RESULTS

Bias had a large influence in the estimation of D and f for the low-perfused brain model, particularly at lower b values, with the same trends being confirmed by in vivo imaging. Significant differences were demonstrated in vivo for estimation of D (P = 0.029) and f (P < 0.001) with [300,1000] and [500,1000] distributions. The effect of bias was considerably lower for the high-perfused models. The optimal b value distributions were estimated to be brain_500,1000, kidney_300,1000, and liver_200,1000.

CONCLUSION

IVIM parameters can be estimated using a rapid DW-MRI protocol, where the optimal b value distribution depends on tissue characteristics and compromise between bias and variability.

Visualization of kidney fibrosis in diabetic nephropathy by long diffusion tensor imaging MRI with spin-echo sequence

Renal fibrosis (RF) is an indicator for progression of chronic kidney disease (CKD). Although diabetic nephropathy (DN) is the leading cause of CKD and end-stage renal disease in Western populations, the ability of MRI to evaluate RF in DN patients has not been determined. As a first step to identify possible MRI methods for RF evaluation, we examined the use of diffusion tensor imaging (DTI) MRI to evaluate RF in a rat model of DN (SHR/NDmcr-cp(cp/cp): SHR/ND). The signal-to-noise ratio in DTI MRI was enhanced using a spin-echo sequence, and a special kidney attachment was developed for long-term stabilization. The changes in renal temperature and blood flow during measurement were minimal, suggesting the feasibility of this method. At 38 weeks of age, RF had aggressively accumulated in the outer stripe (OS) of the outer medulla. FA maps showed that this method was successful in visualizing and evaluating fibrosis in the OS of the SHR/ND rat kidney (r = 0.7697, P = 0.0126). Interestingly, in the FA color maps, the directions of water molecule diffusion in RF were random, but distinct from conventional water diffusion in brain neuron fibers. These findings indicate that DTI MRI may be able to evaluate RF in CKD by DN.

Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit

PURPOSE

To evaluate if a three-component model correctly describes the diffusion signal in the kidney and whether it can provide complementary anatomical or physiological information about the underlying tissue.

MATERIALS AND METHODS

Ten healthy volunteers were examined at 3T, with T2 -weighted imaging, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM). Diffusion tensor parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squares fitting of the DTI data and mono-, bi-, and triexponential fit parameters (D1 , D2 , D3 , ffast2 , ffast3 , and finterm ) using a nonlinear fit of the IVIM data. Average parameters were calculated for three regions of interest (ROIs) (cortex, medulla, and rest) and from fiber tractography. Goodness of fit was assessed with adjusted R2 ( Radj2) and the Shapiro-Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared.

RESULTS

Fitting the diffusion signal was feasible for all models. The three-component model was best able to describe fast signal decay at low b values (b < 50), which was most apparent in Radj2 of the ROI containing high diffusion signals (ROIrest ), which was 0.42 ± 0.14, 0.61 ± 0.11, 0.77 ± 0.09, and 0.81 ± 0.08 for DTI, one-, two-, and three-component models, respectively, and in visual comparison of the fitted and measured S0 . None of the models showed significant differences (P > 0.05) between the diffusion constant of the medulla and cortex, whereas the ffast component of the two and three-component models were significantly different (P < 0.001).

CONCLUSION

Triexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:228-239.

A Combined Use of Intravoxel Incoherent Motion MRI Parameters Can Differentiate Early-Stage Hepatitis-b Fibrotic Livers from Healthy Livers

This study investigated a combined use of intravoxel incoherent motion (IVIM) parameters, Dslow ( D), PF ( f), and Dfast ( D*), for liver fibrosis evaluation. Sixteen healthy volunteers (F0) and 33 hepatitis-b patients (stage F1 = 15, stage F2-4 = 18) were included. With a 1.5 T MR scanner and respiration gating, IVIM diffusion-weighted imaging was acquired using a single-shot echo-planar imaging sequence with 10 b values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm². Signal measurement was performed on right liver parenchyma. With a three-dimensional tool, Dslow, PF, and Dfast values were placed along the x axis, y axis, and z axis, and a plane was defined to separate healthy volunteers from patients. The three-dimensional tool demonstrated that healthy volunteers and all patients with liver fibrosis could be separated. Classification and regression tree showed that a combination of PF (PF < 12.55%), Dslow (Dslow < 1.152 × 10^-3 mm²/s), and Dfast (Dfast < 13.36 × 10^-3 mm²/s) could differentiate healthy subjects and all fibrotic livers (F1-4) with an area under the curve of logistic regression (AUC) of 0.986. The AUC for differentiation of healthy livers versus F2-4 livers was 1. PF offered the best diagnostic value, followed by Dslow; however, all three parameters of PF, Dslow, and Dfast contributed to liver fibrosis detection.

Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation

A comprehensive literature review was performed on liver intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) technique and its applications. Heterogeneous data have been reported. IVIM parameters are magnetic field strength dependent to a mild extent. A lower Dslow (D) value at 3 T than at 1.5 T and higher perfusion fraction (PF) value at 3 T than at 1.5 T were noted. An increased number of b values are associated with increased IVIM parameter measurement accuracy. With the current status of art, IVIM technique is not yet capable of detecting early stage liver fibrosis and diagnosing liver fibrosis grades, nor can it differentiate liver tumors. Though IVIM parameters show promise for tumor treatment monitoring, till now how PF and Dfast (D*) add diagnostic value to Dslow or apparent diffusion coefficient (ADC) remains unclear. This paper shows the state-of-art IVIM MR technique is still not able to offer reliable measurement for liver. More works on the measurement robustness are warranted as they are essential to justify follow-up clinical studies on patients.

Diffusion-weighted imaging of the liver: Current applications

Diffusion-weighted imaging (DWI) of the liver can be performed using most commercially available machines and is currently accepted in routine sequence. This sequence has some potential as an imaging biomarker for fibrosis, tumor detection/characterization, and following/predicting therapy. To improve reliability including accuracy and reproducibility, researchers have validated this new technique in terms of image acquisition, data sampling, and analysis. The added value of DWI in contrast-enhanced magnetic resonance imaging was established in the detection of malignant liver lesions. However, some limitations remain in terms of lesion characterization and fibrosis detection. Furthermore, the methodologies of image acquisition and data analysis have been inconsistent. Therefore, researchers should make every effort to not only improve accuracy and reproducibility but also standardize imaging parameters.

Intravoxel Incoherent Motion MR Imaging for Staging of Hepatic Fibrosis

Objectives

To determine the potential of intravoxel incoherent motion (IVIM) MR imaging for staging of hepatic fibrosis (HF).

Methods

We searched PubMed and EMBASE from their inception to 31 July 2015 to select studies reporting IVIM MR imaging and HF staging. We defined F1-2 as non-advanced HF, F3-4 as advanced HF, F0 as normal liver, F1 as very early HF, and F2-4 as significant HF. Then we compared stage F0 with F1, F0-1 with F2-3, and F1-2 with F3-4 using IVIM-derived parameters (pseudo-diffusion coefficient D*, perfusion fraction f, and pure molecular diffusion parameter D). The effect estimate was expressed as a pooled weighted mean difference (WMD) with 95% confidence interval (CI), using the fixed-effects model.

Results

Overall, we included six papers (406 patients) in this study. Significant differences in D* were observed between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 2.46, 95% CI 0.83–4.09, P = 0.006; WMD 13.10, 95% CI 9.53–16.67, P < 0.001; WMD 14.34, 95% CI 10.26–18.42, P < 0.001, respectively). Significant differences in f were also found between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 1.62, 95% CI 0.06–3.18, P = 0.027; WMD 5.63, 95% CI 2.74–8.52, P < 0.001; WMD 3.30, 95% CI 2.10–4.50, P < 0.001, respectively). However, D showed no differences between F0 and F1, F0-1 and F2-3, and F1-2 and F3-4 (WMD 0.05, 95% CI -0.01─0.11, P = 0.105; WMD 0.04, 95% CI -0.01─0.10, P = 0.230; WMD 0.02, 95% CI -0.02─0.06, P = 0.378, respectively).

Conclusions

IVIM MR imaging provides an effective method of staging HF and can distinguish early HF from normal liver, significant HF from normal liver or very early HF, and advanced HF from non-advanced HF.

Apparent Diffusion Coefficient Value Is Not Dependent on Magnetic Resonance Systems and Field Strength Under Fixed Imaging Parameters in Brain

OBJECTIVE

The aim of the study was to investigate the causes of apparent diffusion coefficient (ADC) measurement errors and to determine the optimal scanning parameters that are independent of the field strength and vendors of the magnetic resonance (MR) system.

MATERIALS AND METHODS

Brain MR images of 10 healthy volunteers were scanned using 6 MR scanners of different field strengths and vendors in 2 different institutions. Ethical review board approvals were obtained for this study, and all volunteers gave their informed consents. Coefficient of variation (CV) of ADC values were compared for their differences in various MR scanners and in the scanned subjects.

RESULTS

The CV of ADC values for 6 different scanners of 6 brains was 3.32%. The CV for repeated measurements in 1 day (10 scans per day) and in 10 days (scan per day for 10 days) for 1 subject was 1.72% and 2.96%, respectively (n = 5, P < 0.001). The CV of measurements for 10 healthy subjects was 5.22%. The measurement errors of the ADC values for 6 different MR units in 1 subject were higher than the intrascanner variance for the same subject but were lower than the intersubject variance for the same scanner.

CONCLUSIONS

The variance in the ADC values for different MR scanners is reasonably small if appropriate scanning parameters (repetition time, >3000 ms; echo time, minimum; and high enough signal-to-noise ratio of high-b diffusion-weighted image) are used.

Modified triexponential analysis of intravoxel incoherent motion for brain perfusion and diffusion

BACKGROUND

To noninvasively obtain more detailed information on brain perfusion and diffusion using modified triexponential analysis.

METHODS

On a 3.0 Tesla MRI, diffusion-weighted imaging of the brain with multiple b-values was performed in healthy volunteers (n = 12). We derived perfusion-related, fast-free, and slow-restricted diffusion coefficients (Dp , Df , and Ds , respectively) and fractions (Fp , Ff , and Fs , respectively) in the frontal and occipital white matter, caudate nucleus, and putamen calculated from triexponential function by a two-step approach. Ds was initially determined using monoexponential function in b-values over 1000 s/mm(2) and was applied to triexponential function. Additionally, the literature value of the diffusion coefficient of free water at 37 °C was assigned to Df . Finally, Dp and fractions were derived using all b-values. Moreover, biexponential analysis was performed and compared with triexponential analysis. We also determined regional cerebral blood flow (rCBF) using arterial spin labeling and assessed its relation with each diffusion parameter.

RESULTS

Significant positive correlations between Dp and rCBF were found in the caudate nucleus (R = 0.84; P = 0.01) and putamen (R = 0.86; P = 0.01), whereas no diffusion parameters were significantly correlated with rCBF on biexponential analysis (P > 0.05 for all).

CONCLUSION

Diffusion analysis with triexponential function enables noninvasive gathering of more detailed information on brain perfusion and diffusion.

Triexponential function analysis of diffusion-weighted MRI for diagnosing prostate cancer

BACKGROUND

To evaluate more detailed information noninvasively through on diffusion and perfusion in prostate cancer (PCa) using triexponential analysis of diffusion-weighted imaging (DWI).

METHODS

Sixty-three prostate cancer patients underwent preoperative 3.0 Tesla MRI including eight b-values DWI. Triexponential analysis was performed to obtain three diffusion coefficients (Dp , Df , Ds ), as well as fractions (Fp , Ff , Fs ). Each diffusion parameter for cancerous lesions and normal tissues was compared and the relationship between diffusion parameters and Gleason score (GS) was assessed. K(trans) , Ve , and the ratios of intracellular components measured in histopathological specimens were compared with diffusion parameters.

RESULTS

Dp was significantly greater for cancerous lesions than normal peripheral zone (PZ) (P < 0.001), whereas Dp in transition zone (TZ) showed no significant difference (P = 0.74, 95% confidence interval (CI) = -4.69-6.48). Ds was significantly smaller for each cancerous lesions in PZ and TZ (P < 0.001, respectively). There was no significant difference in Df between cancerous lesions and normal tissues in PZ and TZ (P = 0.07, 95% CI = -0.29-0.12 and P = 0.53, 95% CI = -3.51-2.29, respectively). D obtained with biexponential analysis were significantly smaller in cancerous lesions than in normal tissue in PZ and TZ (P < 0.001 for both), while D* in PZ and TZ showed no significant difference (P = 0.14, 95% CI = -1.60-0.24 and P = 0.31, 95% CI = -3.43-1.16, respectively). Dp in PZ and TZ showed significant correlation with K(trans) (R = 0.85, P < 0.001; R = 0.81, P < 0.001, respectively), while D(*) in PZ obtained with biexponential analysis showed no such correlation (P = 0.08, 95% CI = -0.14-0.30). Fs was significantly correlated with intracellular space fraction evaluated in histopathological specimens in PZ and TZ cancer (R = 0.41, P < 0.05; R = 0.59, P < 0.001, respectively). Ff and Fs correlated significantly with GS in PZ and TZ cancer (PZ: R = -0.44, P < 0.05; R = 0.37, P < 0.05, TZ: R = -0.59, P < 0.05; R = 0.57, P < 0.05, respectively).

CONCLUSION

Triexponential analysis is a noninvasive approach that can provide more detailed information regarding diffusion and perfusion of PCa than biexponential analysis.

Intravoxel incoherent motion diffusion-weighted imaging in the liver: comparison of mono-, bi- and tri-exponential modelling at 3.0-T

PURPOSE

To determine whether a mono-, bi- or tri-exponential model best fits the intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) signal of normal livers.

MATERIALS AND METHODS

The pilot and validation studies were conducted in 38 and 36 patients with normal livers, respectively. The DWI sequence was performed using single-shot echoplanar imaging with 11 (pilot study) and 16 (validation study) b values. In each study, data from all patients were used to model the IVIM signal of normal liver. Diffusion coefficients (Di ± standard deviations) and their fractions (fi ± standard deviations) were determined from each model. The models were compared using the extra sum-of-squares test and information criteria.

RESULTS

The tri-exponential model provided a better fit than both the bi- and mono-exponential models. The tri-exponential IVIM model determined three diffusion compartments: a slow (D1 = 1.35 ± 0.03 × 10(-3) mm(2)/s; f1 = 72.7 ± 0.9 %), a fast (D2 = 26.50 ± 2.49 × 10(-3) mm(2)/s; f2 = 13.7 ± 0.6 %) and a very fast (D3 = 404.00 ± 43.7 × 10(-3) mm(2)/s; f3 = 13.5 ± 0.8 %) diffusion compartment [results from the validation study]. The very fast compartment contributed to the IVIM signal only for b values ≤15 s/mm(2) CONCLUSION: The tri-exponential model provided the best fit for IVIM signal decay in the liver over the 0-800 s/mm(2) range. In IVIM analysis of normal liver, a third very fast (pseudo)diffusion component might be relevant.

KEY POINTS

• For normal liver, tri-exponential IVIM model might be superior to bi-exponential • A very fast compartment (D = 404.00 ± 43.7 × 10 (-3) mm (2) /s; f = 13.5 ± 0.8 %) is determined from the tri-exponential model • The compartment contributes to the IVIM signal only for b ≤ 15 s/mm(2).

Latest developments in the imaging of fibrotic liver disease

According to the World Health Organization, liver cirrhosis accounted for 1.8% of all deaths in Europe, causing about 170,000 deaths per year. Approximately 29 million persons in the EU suffer from chronic liver disease and this trend is on the rise. Liver disease is the EU's fifth most common cause of death accounting for at least one in six deaths. Early detection and monitoring of fibrosis has the potential to direct management of these chronic liver diseases and avert morbidity and mortality. Although the available techniques are in their infancy and the very early stages of fibrosis are difficult to detect, there have been significant advances in imaging over the last decade that has resulted in the use of these new imaging techniques being introduced into the patient pathway. This review explores the accuracies of these imaging techniques, their role in the management of patients, and the potential for the future.

Diffusion-weighted imaging of the liver: techniques and applications

Diffusion-weighted imaging (DWI) is a technique that assesses the cellularity, tortuosity of the extracellular/extravascular space, and cell membrane density based on differences in water proton mobility in tissues. The strength of the diffusion weighting is reflected by the b value. DWI using several b values enables the quantification of the apparent diffusion coefficient. DWI is increasingly used in liver imaging for multiple reasons: it can add useful qualitative and quantitative information to conventional imaging sequences; it is acquired relatively quickly; it is easily incorporated into existing clinical protocols; and it is a noncontrast technique.

Functional MR imaging of the abdomen

In this article, functional magnetic resonance (MR) imaging techniques in the abdomen are discussed. Diffusion-weighted imaging (DWI) increases the confidence in detecting and characterizing focal hepatic lesions. The potential uses of DWI in kidneys, adrenal glands, bowel, and pancreas are outlined. Studies have shown potential use of quantitative dynamic contrast-enhanced MR imaging parameters, such as K(trans), in predicting outcomes in cancer therapy. MR elastography is considered to be a useful tool in staging liver fibrosis. A major issue with all functional MR imaging techniques is the lack of standardization of the protocol.

[Triexponential diffusion analysis in invasive ductal carcinoma and fibroadenoma]

To simultaneously obtain information on diffusion and perfusion in breast lesions by diffusion-weighted magnetic resonance imaging (DWI), we analyzed three diffusion components using a triexponential function. Eighteen subjects [10 with invasive ductal carcinoma (IDC), 8 with fibroadenoma] were evaluated using DWI with multiple b-values. We derived perfusion-related diffusion, fast free diffusion, and slow restricted diffusion coefficients (Dp, Df, Ds) calculated from the triexponential function using the DWI data. Moreover, the triexponential analysis was compared with biexponential and monoexponential analyses. Each diffusion coefficient with a triexponential function was correlated to a relative enhancement ratio (RER) using dynamic contrast-enhanced MRI. In triexponential analysis, Dp and Ds in IDC were significantly higher than those for fibroadenoma. There was no correlation between each diffusion coefficient from the triexponential analysis in any of the groups (Dp, Df, and Ds), but biexponential analysis revealed a positive correlation between each diffusion coefficient in breast lesions. Strong correlations were found between Dp and RERs. Triexponential analysis thus makes it possible to obtain, in noninvasive fashion, more detailed diffusion and perfusion information in breast lesions.

Diffusion analysis with triexponential function in hepatic steatosis

Our purpose was to assess the influence of liver steatosis on diffusion by triexponential analysis. Thirty-three patients underwent diffusion-weighted magnetic resonance imaging with multiple b values for perfusion-related diffusion, fast free diffusion, and slow restricted diffusion coefficients (D p, D f, D s) and fractions (F p, F f, F s). They also underwent dual-echo gradient-echo imaging for measurement of the hepatic fat fraction (HFF). Of these, 13 patients were included in the control group and 20 in the fatty liver group with HFF >5 %. The parameters of the two groups were compared by use of the Mann-Whitney U test. The relationships between diffusion coefficients and HFFs were assessed by use of the Pearson correlation. D p and D f were reduced significantly in the steatotic liver group compared with those in the control group (D p = 27.72 ± 6.61 × 10(-3) vs. 33.33 ± 6.47 × 10(-3) mm(2)/s, P = 0.0072; D f = 1.70 ± 0.53 × 10(-3) vs. 2.06 ± 0.40 × 10(-3) mm(2)/s, P = 0.0224). There were no significant differences in the other parameters between the two groups. Furthermore, D p and D f were correlated with HFF (P < 0.0001, r = -0.64 and P = 0.0008, r = -0.56, respectively). Decreased liver perfusion in steatosis caused the reduction in D p, and extracellular fat accumulation and intracellular fat droplets in steatosis led to the reduction in D f. Thus, the influence of hepatic steatosis should be taken into consideration when triexponential function analysis is used for assessment of diffuse liver disease.