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

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

Impact of velocity- and acceleration-compensated encodings on signal dropout and black-blood state in diffusion-weighted magnetic resonance liver imaging at clinical TEs

PURPOSE

The study aims to develop easy-to-implement concomitant field-compensated gradient waveforms with varying velocity-weighting (M1) and acceleration-weighting (M2) levels and to evaluate their efficacy in correcting signal dropouts and preserving the black-blood state in liver diffusion-weighted imaging. Additionally, we seek to determine an optimal degree of compensation that minimizes signal dropouts while maintaining blood signal suppression.

METHODS

Numerically optimized gradient waveforms were adapted using a novel method that allows for the simultaneous tuning of M1- and M2-weighting by changing only one timing variable. Seven healthy volunteers underwent diffusion-weighted magnetic resonance imaging (DWI) with five diffusion encoding schemes (monopolar, velocity-compensated (M1 = 0), acceleration-compensated (M1 = M2 = 0), 84%-M1-M2-compensated, 67%-M1-M2-compensated) at b-values of 50 and 800 s/mm2 at a constant echo time of 70 ms. Signal dropout correction and apparent diffusion coefficients (ADCs) were quantified using regions of interest in the left and right liver lobe. The blood appearance was evaluated using two five-point Likert scales.

RESULTS

Signal dropout was more pronounced in the left lobe (19%-42% less signal than in the right lobe with monopolar scheme) and best corrected by acceleration-compensation (8%-10% less signal than in the right lobe). The black-blood state was best with monopolar encodings and decreased significantly (p < 0.001) with velocity- and/or acceleration-compensation. The partially M1-M2-compensated encoding schemes could restore the black-blood state again. Strongest ADC bias occurred for monopolar encodings (difference between left/right lobe of 0.41 μm2/ms for monopolar vs. < 0.12 μm2/ms for the other encodings).

CONCLUSION

All of the diffusion encodings used in this study demonstrated suitability for routine DWI application. The results indicate that a perfect value for the level of M1-M2-compensation does not exist. However, among the examined encodings, the 84%-M1-M2-compensated encodings provided a suitable tradeoff.

The Feasibility of Using Tri-Exponential Intra-Voxel Incoherent Motion DWI for Identifying the Microvascular Invasion in Hepatocellular Carcinoma

Purpose

To assess the effectiveness of tri-exponential Intra-Voxel Incoherent Motion (tri-IVIM) MRI in preoperatively identifying microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Patients and Methods

In this prospective study, 67 patients with HCC were included. Metrics from bi-exponential IVIM (bi-IVIM) and tri-IVIM were calculated. Subgroup comparisons were analyzed using the independent Student's t-test or Mann-Whitney U-test. Logistic regression was performed to explore clinical risk factors. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis.

Results

MVI-positive HCCs exhibited significantly lower true diffusion coefficient (Dt) from bi-IVIM, as well as fast-diffusion coefficients (Df) and slow-diffusion coefficients (Ds) from tri-IVIM, compared to MVI-negative HCCs (p < 0.05). Tumor size and alpha-fetoprotein (AFP) were identified as risk factors. The combination of tri-IVIM-derived metrics (Ds and Df) yielded higher diagnostic accuracy (AUC = 0.808) compared to bi-IVIM (AUC = 0.741). A predictive model based on a nomogram was constructed using Ds, Df, tumor size, and AFP, resulting in the highest diagnostic accuracy (AUC = 0.859). Decision curve analysis indicated that the constructed model, provided the highest net benefit by accurately stratifying the risk of MVI, followed by tri-IVIM and bi-IVIM.

Conclusion

Tri-IVIM can provide information on perfusion and diffusion for evaluating MVI in HCC. Additionally, tri-IVIM outperformed bi-IVIM in identifying MVI-positive HCC. By integrating clinical risk factors and metrics from tri-IVIM, a predictive nomogram exhibited the highest diagnostic accuracy, potentially aiding in the noninvasive and preoperative assessment of MVI.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status

OBJECTIVES

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas.

METHODS

Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (D_int and D_p) and fractions of strictly limited, intermediate, and perfusion-related diffusion (F_very-slow, F_int, and F_p) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed.

RESULTS

High-grade gliomas (HGGs) had significantly higher F_int, F_very-slow, and D_p values but significantly lower F_p and D_int values than low-grade gliomas (LGGs), and F_int and F_p differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). F_int achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, F_int, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, F_int remained the only differentiator. Additionally, F_int and F_p showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively).

CONCLUSIONS

The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses.

KEY POINTS

• The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • F_int was useful in evaluating the grade and genotype of gliomas.

Influence of saturation effects on biexponential liver intravoxel incoherent motion

PURPOSE

Studies on intravoxel incoherent motion (IVIM) imaging in the liver have been carried out with different acquisition protocols. The number of acquired slices and the distances between slices can influence IVIM measurements due to saturation effects, but these effects have often been disregarded. This study investigated differences in biexponential IVIM parameters between two slice settings.

METHODS

Fifteen healthy volunteers (21-30 years) were examined at a field strength of 3 T. Diffusion-weighted images of the abdomen were acquired with 16 b values (0-800 s/mm² ), with four slices for the few slices setting and 24-27 slices for the many slices setting. Regions of interest were manually drawn in the liver. The data were fitted with a monoexponential signal curve and a biexponential IVIM curve, and biexponential IVIM parameters were determined. The dependence on the slice setting was assessed with Student's t test for paired samples (normally distributed IVIM parameters) and the Wilcoxon signed-rank test (non-normally distributed parameters).

RESULTS

None of the parameters were significantly different between the settings. For few slices and many slices, respectively, the mean values (SDs) for D $$ D $$ were 1.21 μm 2 / ms $$ 1.21{\upmu \mathrm{m}}^2/\mathrm{ms} $$ ( 0.19 μm 2 / ms $$ 0.19\kern0.3em {\upmu \mathrm{m}}^2/\mathrm{ms} $$ ) and 1.20 μm 2 / ms $$ 1.20{\upmu \mathrm{m}}^2/\mathrm{ms} $$ ( 0.11 μm 2 / ms $$ 0.11\kern0.3em {\upmu \mathrm{m}}^2/\mathrm{ms} $$ ); for f $$ f $$ they were 29.7% (6.2%) and 27.7% (3.6%); and for D * $$ {D}^{\ast } $$ they were 8.76 ⋅ 10 - 2 mm 2 / s $$ 8.76\cdot {10}^{-2}{\mathrm{mm}}^2/\mathrm{s} $$ ( 4.54 ⋅ 10 - 2 mm 2 / s $$ 4.54\cdot {10}^{-2}\kern0.3em {\mathrm{mm}}^2/\mathrm{s} $$ ) and 8.71 ⋅ 10 - 2 mm 2 / s $$ 8.71\cdot {10}^{-2}{\mathrm{mm}}^2/\mathrm{s} $$ ( 4.06 ⋅ 10 - 2 mm 2 / s $$ 4.06\cdot {10}^{-2}\kern0.3em {\mathrm{mm}}^2/\mathrm{s} $$ ).

CONCLUSION

Biexponential IVIM parameters in the liver are comparable among IVIM studies that use different slice settings, with mostly negligible saturation effects. However, this may not hold for studies that use much shorter TR.

Precision of region of interest-based tri-exponential intravoxel incoherent motion quantification and the role of the Intervoxel spatial distribution of flow velocities

PURPOSE

The purpose of this work was to obtain precise tri-exponential intravoxel incoherent motion (IVIM) quantification in the liver using 2D (b-value and first-order motion moment [M1 ]) IVIM-DWI acquisitions and region of interest (ROI)-based fitting techniques.

METHODS

Diffusion MRI of the liver was performed in 10 healthy volunteers using three IVIM-DWI acquisitions: conventional monopolar, optimized monopolar, and optimized 2D (b-M1 ). For each acquisition, bi-exponential and tri-exponential full, segmented, and over-segmented ROI-based fitting and a newly proposed blood velocity SDdistribution (BVD) fitting technique were performed to obtain IVIM estimates in the right and left liver lobes. Fitting quality was evaluated using corrected Akaike information criterion. Precision metrics (test-retest repeatability, inter-reader reproducibility, and inter-lobar agreement) were evaluated using Bland-Altman analysis, repeatability/reproducibility coefficients (RPCs), and paired sample t-tests. Precision was compared across acquisitions and fitting methods.

RESULTS

High repeatability and reproducibility was observed in the estimations of the diffusion coefficient (Dtri  = [1.03 ± 0.11] × 10-3  mm2 /s; RPCs ≤ 1.34 × 10-4  mm2 /s), perfusion fractions (F1  = 3.19 ± 1.89% and F2  = 16.4 ± 2.07%; RPCs ≤ 2.51%), and blood velocity SDs (Vb,1  = 1.44 ± 0.14 mm/s and Vb,2  = 3.62 ± 0.13 mm/s; RPCs ≤ 0.41 mm/s) in the right liver lobe using the 2D (b-M1 ) acquisition in conjunction with BVD fitting. Using these methods, significantly larger (p < 0.01) estimates of Dtri and F1 were observed in the left lobe in comparison to the right lobe, while estimates of Vb,1 and Vb,2 demonstrated high interlobar agreement (RPCs ≤ 0.45 mm/s).

CONCLUSIONS

The 2D (b-M1 ) IVIM-DWI data acquisition in conjunction with BVD fitting enables highly precise tri-exponential IVIM quantification in the right liver lobe.

Self-supervised neural network improves tri-exponential intravoxel incoherent motion model fitting compared to least-squares fitting in non-alcoholic fatty liver disease

Recent literature suggests that tri-exponential models may provide additional information and fit liver intravoxel incoherent motion (IVIM) data more accurately than conventional bi-exponential models. However, voxel-wise fitting of IVIM results in noisy and unreliable parameter maps. For bi-exponential IVIM, neural networks (NN) were able to produce superior parameter maps than conventional least-squares (LSQ) generated images. Hence, to improve parameter map quality of tri-exponential IVIM, we developed an unsupervised physics-informed deep neural network (IVIM3-NET). We assessed its performance in simulations and in patients with non-alcoholic fatty liver disease (NAFLD) and compared outcomes with bi-exponential LSQ and NN fits and tri-exponential LSQ fits. Scanning was performed using a 3.0T free-breathing multi-slice diffusion-weighted single-shot echo-planar imaging sequence with 18 b-values. Images were analysed for visual quality, comparing the bi- and tri-exponential IVIM models for LSQ fits and NN fits using parameter-map signal-to-noise ratios (SNR) and adjusted R2. IVIM parameters were compared to histological fibrosis, disease activity and steatosis grades. Parameter map quality improved with bi- and tri-exponential NN approaches, with a significant increase in average parameter-map SNR from 3.38 to 5.59 and 2.45 to 4.01 for bi- and tri-exponential LSQ and NN models respectively. In 33 out of 36 patients, the tri-exponential model exhibited higher adjusted R2 values than the bi-exponential model. Correlating IVIM data to liver histology showed that the bi- and tri-exponential NN outperformed both LSQ models for the majority of IVIM parameters (10 out of 15 significant correlations). Overall, our results support the use of a tri-exponential IVIM model in NAFLD. We show that the IVIM3-NET can be used to improve image quality compared to a tri-exponential LSQ fit and provides promising correlations with histopathology similar to the bi-exponential neural network fit, while generating potentially complementary additional parameters.

Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology

Purpose

Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange?

Methods

Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity L/D0, and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm²; 5 unique gradient duration/separation pairs; SNR = {∞, 100, 80, 40, 20}), accounting for heterogeneity in (D0,L) and perfusion contamination. Diffusion (D) and kurtosis (K) coefficients were calculated, and relationships between (D0,L) and (D,K) were visualized. Functions mapping (D,K) to (D0,L) were computed to predict unseen (D0,L) values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers

Results

Relationships between (D,K) and (D0,L) are complex and depend on the diffusion encoding. Functions mapping D,K to (D0,L) captures salient characteristics of D0(D,K) and L(D,K) dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, bmax = 1500 s/mm², δ = 20 ms, and Δ = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes.

Conclusion

Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible.

b value and first-order motion moment optimized data acquisition for repeatable quantitative intravoxel incoherent motion DWI

PURPOSE

To design a b value and first-order motion moment (M₁ ) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver.

METHODS

Cramer-Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b-M₁ space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b-M₁ -optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test-retest or scan-rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions.

RESULTS

Cramer-Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b-M₁ -optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good-to-excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b-M₁ -optimized waveforms.

CONCLUSIONS

2D b-M₁ -optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases.

Assessment of Impaired Cerebrovascular Reactivity in Chronic Cerebral Ischemia using Intravoxel Incoherent Motion Magnetic Resonance Imaging

BACKGROUND

The severity of chronic cerebral ischemia can be assessed using cerebrovascular reactivity (CVR) to acetazolamide (ACZ) challenge, which is measured by single-photon emission computed tomography (SPECT); however, this is an invasive method. We investigated whether intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) can assess impaired CVR in preoperative patients with chronic cerebral ischemia and compared it to SPECT-CVR.

METHODS

Forty-seven patients with unilateral cervical carotid artery stenosis underwent diffusion-weighted MRI with 11 b-values in the range of 0-800 s/mm² and cerebral perfusion SPECT with the ACZ challenge. The perfusion fraction (f) and diffusion coefficient (D) of the IVIM parameters were calculated using a bi-exponential model. The f and D values and these ratios of the ipsilateral middle cerebral artery territory against the contralateral side were compared with the CVR values of the affected side calculated from the SPECT data.

RESULTS

The IVIM-f and D values in the affected side were significantly higher than those in the unaffected side (median: 7.74% vs. 7.45%, p = 0.027; 0.816 vs. 0.801 10^-3mm²/s, p < 0.001; respectively). However, there were no significant correlations between the f or D values and SPECT-CVR values in the affected side. In contrast, the f ratio showed a moderate negative correlation with the SPECT-CVR values (r = -0.40, p = 0.006) and detected impaired CVR (< 18.4%) with a sensitivity/specificity of 0.71/0.90.

CONCLUSION

The IVIM perfusion parameter, f, can noninvasively assess impaired CVR with high sensitivity and specificity in patients with unilateral cervical carotid artery stenosis.

Echo time dependence of biexponential and triexponential intravoxel incoherent motion parameters in the liver

PURPOSE

Intravoxel incoherent motion (IVIM) studies are performed with different acquisition protocols. Comparing them requires knowledge of echo time (TE) dependencies. The TE-dependence of the biexponential perfusion fraction f is well-documented, unlike that of its triexponential counterparts f₁ and f₂ and the biexponential and triexponential pseudodiffusion coefficients D^* , D 1 ∗ , and D 2 ∗ . The purpose was to investigate the TE-dependence of these parameters and to check whether the triexponential pseudodiffusion compartments are associated with arterial and venous blood.

METHODS

Fifteen healthy volunteers (19-58 y; mean: 24.7 y) underwent diffusion-weighted imaging of the abdomen with 24 b-values (0.2-800 s/mm² ) at TEs of 45, 60, 75, and 90 ms. Regions of interest (ROIs) were manually drawn in the liver. One set of bi- and triexponential IVIM parameters per volunteer and TE was determined. The TE-dependence was assessed with the Kruskal-Wallis test.

RESULTS

TE-dependence was observed for f (P < .001), f₁ (P = .001), and f₂ (P < .001). Their median values at the four measured TEs were: f: 0.198/0.240/0.274/0.359, f₁ : 0.113/0.139/0.146/0.205, f₂ : 0.115/0.155/0.182/0.194. D, D^* , D 1 ∗ , and D 2 ∗ showed no significant TE-dependence. Their values were: diffusion coefficient D (10^-4 mm² /s): 9.45/9.63/9.75/9.41, biexponential D^* (10^-2 mm² /s): 5.26/5.52/6.13/5.82, triexponential D 1 ∗ (10^-2 mm² /s): 1.73/2.91/2.25/2.51, triexponential D 2 ∗ (mm² /s): 0.478/1.385/0.616/0.846.

CONCLUSION

f₁ and f₂ show similar TE-dependence as f, ie, increase with rising TE; an effect that must be accounted for when comparing different studies. The diffusion and pseudodiffusion coefficients might be compared without TE correction. Because of the similar TE-dependence of f₁ and f₂ , the triexponential pseudodiffusion compartments are most probably not associated to venous and arterial blood.

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.

Age and gender dependence of liver diffusion parameters and the possibility that intravoxel incoherent motion modeling of the perfusion component is constrained by the diffusion component

The aim of this study was to establish reference values for middle-aged subjects and to investigate the age and gender dependence of liver diffusion MRI parameters. The IVIM type of liver diffusion scan was based on a single-shot spin-echo-type echo-planar sequence using a 1.5 T magnet with 16 b-values. Diffusion-derived vessel density (DDVD)(b0b2) or DDVD(b0b10) was the signal difference between b = 0 and b = 2 (or b = 10) s/mm² images after removing visible vessels. IVIM analysis was performed with full fitting and segmented fitting, and with a threshold b-value of 60 or 200 s/mm² , and fitting started from b = 2 s/mm² . Thirty-one men (age range: 25-71 years) and 26 men (age: 22-69 years) had DDVD and IVIM analysis, respectively, while 37 women (age: 20-71 years) and 36 women (age: 20-71 years) had DDVD and IVIM analysis, respectively. DDVD results showed a significant age-related reduction for women. IVIM results for full fitting showed excellent agreement with those for segmented fitting using a threshold b of 60 s/mm² , but this was less good for results with a threshold b of 200 s/mm² . As age increased, female subjects' D_slow measure showed a significant reduction, while their PF and D_fast measures showed a significant increase. For the age group of 40-55 years, DDVD(b0b2), DDVD(b0b10), D_slow , PF and D_fast were 12.26 ± 3.90 au/pixel, 16.95 ± 5.45 au/pixel, 1.072 ± 0.067 (10^-3 mm² /s), 0.141 ± 0.025 and 61.0 ± 14.0 (10^-3 mm² /s) for men, and 13.35 ± 3.6 au/pixel, 17.20 ± 3.62 au/pixel, 1.069 ± 0.074 (10^-3 mm² /s), 0.119 ± 0.014 and 57.1 ± 13.2 (10^-3 mm² /s) for women, respectively. DDVD measure of this study suggest that aging is associated with a reduction in liver perfusion. There is a possibility that a lower D_slow measure is associated with artificially higher PF and D_fast measures, and that IVIM modeling of the perfusion component is constrained by the diffusion component.

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.

An optimized b-value distribution for triexponential intravoxel incoherent motion (IVIM) in the liver

PURPOSE

To find an optimized b-value distribution for reproducible triexponential intravoxel incoherent motion (IVIM) exams in the liver.

METHODS

A numeric optimization of b-value distributions was performed using the triexponential IVIM equation and 27 different IVIM parameter sets. Starting with an initially optimized distribution of 6 b-values, the number of b-values was increased stepwise. Each new b-value was chosen from a set of 64 predefined b-values based on the computed summed relative mean error of the fitted triexponential IVIM parameters. This process was repeated for up to 100 b-values. In simulations and in vivo measurements, optimized b-value distributions were compared to 4 representative distributions found in literature.

RESULTS

The first 16 optimized b-values were 0, 0.3, 0.3, 70, 200, 800, 70, 1, 3.5, 5, 70, 1.2, 6, 45, 1.5, and 60 in units of s/mm² . Low b-values were much more frequent than high b-values. The optimized b-value distribution resulted in a higher fit stability compared to distributions used in literature in both, simulation and in vivo measurements. Using more than 6 b-values, ideally 16 or more, increased the fit stability considerably.

CONCLUSION

Using optimized b-values, the fit uncertainty in triexponential IVIM can be largely reduced. Ideally, 16 or more b-values should be acquired.

Value of intravoxel incoherent motion in detecting and staging liver fibrosis: A meta-analysis

BACKGROUND

Liver fibrosis (LF) is a common pathological feature of all chronic liver diseases. With the accumulation of extracellular matrix in the fibrotic liver, true molecular water diffusion and perfusion-related diffusion are restricted. Intravoxel incoherent motion (IVIM) can capture the information on tissue diffusivity and microcapillary perfusion separately and reflect the fibrotic severity with diffusion coefficients.

AIM

To investigate the diagnostic performance of IVIM in detecting and staging LF with histology as a reference standard.

METHODS

A comprehensive literature search was conducted to identify studies on the diagnostic accuracy of IVIM for assessment of histologically proven LF. The stages of LF were classified as F0 (no fibrosis), F1 (portal fibrosis without septa), F2 (periportal fibrosis with few septa), F3 (septal fibrosis), and F4 (cirrhosis) according to histopathological findings. Data were extracted to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio, as well as the area under the summary receiver operating characteristic curve (AUC) in each group.

RESULTS

A total of 12 studies with 923 subjects were included in this meta-analysis with 5 studies (n = 465) for LF ≥ F1, 9 studies (n = 757) for LF ≥ F2, 4 studies (n = 413) for LF ≥ F3, and 6 studies (n = 562) for LF = F4. The pooled sensitivity and specificity were estimated to be 0.78 (95% confidence interval: 0.73-0.82) and 0.81 (0.74-0.86) for LF ≥ F1 detection with IVIM; 0.82 (0.79-0.86) and 0.80 (0.75-0.84) for staging F2 fibrosis; 0.85 (0.79-0.90) and 0.83 (0.77-0.87) for staging F3 fibrosis, and 0.90 (0.84-0.94) and 0.75 (0.70-0.79) for detecting F4 cirrhosis, respectively. The AUCs for LF ≥ F1, F2, F3, F4 detection were 0.862 (0.811-0.914), 0.883 (0.856-0.909), 0.886 (0.865-0.907), and 0.899 (0.866-0.932), respectively. Moderate to substantial heterogeneity was observed with inconsistency index (I²) ranging from 0% to 77.9%. No publication bias was detected.

CONCLUSION

IVIM is a noninvasive tool with good diagnostic performance in detecting and staging LF. Optimized and standardized IVIM protocols are needed to further improve its diagnostic accuracy in clinical practice.

Use of Cross-Sectional Imaging for Tight Monitoring of Inflammatory Bowel Diseases

A treat-to-target strategy with close monitoring of intestinal inflammation is recommended in inflammatory bowel disease (IBD). Ileocolonoscopy remains the gold standard for assessing disease activity in IBD but is a relatively invasive procedure and is impossible to repeat in the context of tight monitoring strategies. In addition to biomarkers, cross-sectional imaging increasingly is used in these patients. Computed tomography is limited by the use of radiation, while the use of magnetic resonance enterography (MRE) is limited by its cost and access. There is growing interest in bowel ultrasound that represents a cost-effective, noninvasive, and well-tolerated modality in clinical practice, but it is operator dependent. Compared with ileocolonoscopy and MRE, bowel US has been shown to have the same level of accuracy in assessing and monitoring disease activity for both CD and UC and thus can be considered a point-of-care test. Diffusion-weighted imaging (DWI) is a MR imaging technique that increasingly is used in both IBD and non-IBD conditions and has been shown to be a valuable and accurate tool for assessing and monitoring IBD activity. Compared with conventional MRE, DWI is quicker, less time consuming, may not require intravenous contrast agent, fasting, bowel cleansing, oral preparation, or rectal preparation. This review discusses the role of these cross-sectional imaging techniques for the management of patients with IBD. In the near future, the value of DWI and ultrasound in assessing IBD will require further investigation in the era of transmural healing in CD and complete mucosal healing, including histologic remission, in ulcerative colitis.

Diffusion processes modeling in magnetic resonance imaging

Background

The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied.

Material and method

To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T₂: cyclomethicone and caprylyl methicone. For quantitative assessment of our phantom, we performed DWI on 1.5T magnetic resonance scanner with various fat suppression techniques. We assessed water-in-oil emulsion as an extracorporeal source signal by simultaneously scanning a patient in whole-body DWI sequence.

Results

We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm²/s. The ADC values of polymer solutions are well relevant to the mono-exponential equation with the mean relative difference of 0.91%.

Conclusion

The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.

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 .

Topics on quantitative liver magnetic resonance imaging

Liver magnetic resonance imaging (MRI) is subject to continuous technical innovations through advances in hardware, sequence and novel contrast agent development. In order to utilize the abilities of liver MR to its full extent and perform high-quality efficient exams, it is mandatory to use the best imaging protocol, to minimize artifacts and to select the most adequate type of contrast agent. In this article, we review the routine clinical MR techniques applied currently and some latest developments of liver imaging techniques to help radiologists and technologists to better understand how to choose and optimize liver MRI protocols that can be used in clinical practice. This article covers topics on (I) fat signal suppression; (II) diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) analysis; (III) dynamic contrast-enhanced (DCE) MR imaging; (IV) liver fat quantification; (V) liver iron quantification; and (VI) scan speed acceleration.

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Compartmental diffusion MRI models that account for intravoxel incoherent motion (IVIM) of blood perfusion allow for estimation of the fractional volume of the microvascular compartment. Conventional IVIM models are known to be biased by not accounting for partial volume effects caused by free water and cerebrospinal fluid (CSF), or for tissue-dependent relaxation effects. In this work, a three-compartment model (tissue, free water and blood) that includes relaxation terms is introduced. To estimate the model parameters, in vivo human data were collected with multiple echo times (TE), inversion times (TI) and b-values, which allowed a direct relaxation estimate alongside estimation of perfusion, diffusion and fractional volume parameters. Compared to conventional two-compartment models (with and without relaxation compensation), the three-compartment model showed less effects of CSF contamination. The proposed model yielded significantly different volume fractions of blood and tissue compared to the non-relaxation-compensated model, as well as to the conventional two-compartment model, suggesting that previously reported parameter ranges, using models that do not account for relaxation, should be reconsidered.

On the Field Strength Dependence of Bi- and Triexponential Intravoxel Incoherent Motion (IVIM) Parameters in the Liver

BACKGROUND

Studies on intravoxel incoherent motion (IVIM) imaging are carried out with different acquisition protocols.

PURPOSE

To investigate the dependence of IVIM parameters on the B₀ field strength when using a bi- or triexponential model.

STUDY TYPE

Prospective.

STUDY POPULATION

20 healthy volunteers (age: 19-28 years).

FIELD STRENGTH/SEQUENCE

Volunteers were examined at two field strengths (1.5 and 3T). Diffusion-weighted images of the abdomen were acquired at 24 b-values ranging from 0.2 to 500 s/mm² .

ASSESSMENT

ROIs were manually drawn in the liver. Data were fitted with a bi- and a triexponential IVIM model. The resulting parameters were compared between both field strengths.

STATISTICAL TESTS

One-way analysis of variance (ANOVA) and Kruskal-Wallis test were used to test the obtained IVIM parameters for a significant field strength dependency.

RESULTS

At b-values below 6 s/mm² , the triexponential model provided better agreement with the data than the biexponential model. The average tissue diffusivity was D = 1.22/1.00 μm² /msec at 1.5/3T. The average pseudodiffusion coefficients for the biexponential model were D^* = 308/260 μm² /msec at 1.5/3T; and for the triexponential model D 1 * = 81.3/65.9 μm² /msec, D 2 * = 2453/2333 μm² /msec at 1.5/3T. The average perfusion fractions for the biexponential model were f = 0.286/0.303 at 1.5/3T; and for the triexponential model f₁ = 0.161/0.174 and f₂ = 0.152/0.159 at 1.5/3T. A significant B₀ dependence was only found for the biexponential pseudodiffusion coefficient (ANOVA/KW P = 0.037/0.0453) and tissue diffusivity (ANOVA/KW: P < 0.001).

DATA CONCLUSION

Our experimental results suggest that triexponential pseudodiffusion coefficients and perfusion fractions obtained at different field strengths could be compared across different studies using different B₀ . However, it is recommended to take the field strength into account when comparing tissue diffusivities or using the biexponential IVIM model. Considering published values for oxygenation-dependent transversal relaxation times of blood, it is unlikely that the two blood compartments of the triexponential model represent venous and arterial blood.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1883-1892.

Dependence of intravoxel incoherent motion diffusion MR threshold b-value selection for separating perfusion and diffusion compartments and liver fibrosis diagnostic performance

BACKGROUND

Intravoxel incoherent motion (IVIM) tissue parameters depend on the threshold b-value.

PURPOSE

To explore how threshold b-value impacts PF ( f), D_slow ( D), and D_fast ( D*) values and their performance for liver fibrosis detection.

MATERIAL AND METHODS

Fifteen healthy volunteers and 33 hepatitis B patients were included. With a 1.5-T magnetic resonance (MR) scanner and respiration gating, IVIM data were acquired with ten b-values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm². Signal measurement was performed on the right liver. Segmented-unconstrained analysis was used to compute IVIM parameters and six threshold b-values in the range of 40-200 s/mm² were compared. PF, D_slow, and D_fast values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate volunteers from patients.

RESULTS

Higher threshold b-values were associated with higher PF measurement; while lower threshold b-values led to higher D_slow and D_fast measurements. The dependence of PF, D_slow, and D_fast on threshold b-value differed between healthy livers and fibrotic livers; with the healthy livers showing a higher dependence. Threshold b-value = 60 s/mm² showed the largest mean distance between healthy liver datapoints vs. fibrotic liver datapoints, and a classification and regression tree showed that a combination of PF (PF < 9.5%), D_slow (D_slow < 1.239 × 10^-3 mm²/s), and D_fast (D_fast < 20.85 × 10^-3 mm²/s) differentiated healthy individuals and all individual fibrotic livers with an area under the curve of logistic regression (AUC) of 1.

CONCLUSION

For segmented-unconstrained analysis, the selection of threshold b-value = 60 s/mm² improves IVIM differentiation between healthy livers and fibrotic livers.

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.

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

Removal of evidential motion-contaminated and poorly fitted image data improves IVIM diffusion MRI parameter scan-rescan reproducibility

Background It has been reported that intravoxel incoherent motion (IVIM) diffusion magnetic resonance imaging (MRI) scan-rescan reproducibility is unsatisfactory. Purpose To study IVIM MRI parameter reproducibility for liver parenchyma after the removal of motion-contaminated and/or poorly fitted image data. Material and Methods Eighteen healthy volunteers had liver scans twice in the same session to assess scan-rescan repeatability, and again in another session after an average interval of 13 days to assess reproducibility. Diffusion-weighted images were acquired with a 3-T scanner using respiratory-triggered echo-planar sequence and 16 b-values (0-800 s/mm²). Measurement was performed on the right liver with segment-unconstrained least square fitting. Image series with evidential anatomical mismatch, apparent artifacts, and poorly fitted signal intensity vs. b-value curve were excluded. A minimum of three slices was deemed necessary for IVIM parameter estimation. Results With a total 54 examinations, six did not satisfy inclusion criteria, leading to a success rate of 89%, and 14 volunteers were finally included for the repeatability/reproducibility study. A total of 3-10 slices per examination (mean = 5.3 slices, median = 5 slices) were utilized for analysis. Using threshold b-value = 80 s/mm², the coefficient of variation and within-subject coefficient of variation for repeatability were 2.86% and 3.36% for D_slow, 3.81% and 4.24% for perfusion fraction (PF), 18.16% and 24.88% for D_fast; and those for reproducibility were 2.48% and 3.24% for D_slow, 4.91% and 5.38% for PF, and 21.18% and 30.89% for D_fast. Conclusion Removal of motion-contaminated and/or poorly fitted image data improves IVIM parameter reproducibility.

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

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

Accurate IVIM model-based liver lesion characterisation can be achieved with only three b-value DWI

OBJECTIVE

The objective of this study was to evaluate a simplified intravoxel incoherent motion (IVIM) approach of diffusion-weighted imaging (DWI) with four b-values for liver lesion characterisation at 1.5 T.

METHODS

DWI data from a respiratory-gated MRI sequence with b = 0, 50, 250, 800 s/mm² were retrospectively analysed in 173 lesions and 40 healthy livers. The apparent diffusion coefficient ADC = ADC(0,800) and IVIM-based parameters D₁' = ADC(50,800), D₂' =ADC(250,800), f₁', f₂', D*', ADC_low = ADC(0,50), and ADC_diff=ADC_low-D₂' were calculated voxel-wise without fitting procedures. Differences between lesion groups were investigated.

RESULTS

Focal nodular hyperplasias were best discriminated from all other lesions by f₁' with an area under the curve (AUC) of 0.989. Haemangiomas were best discriminated by D₁' (AUC of 0.994). For discrimination between malignant and benign lesions, ADC(0,800) and D₁' were best suited (AUC of 0.915 and 0.858, respectively). Discriminatory power was further increased by using a combination of D₁' and f₁'.

CONCLUSION

IVIM parameters D and f approximated from three b-values provided more discriminatory power between liver lesions than ADC determined from two b-values. The use of b = 0, 50, 800 s/mm² was superior to that of b = 0, 250, 800 s/mm². The acquisition of four instead of three b-values has no further benefit for lesion characterisation.

KEY POINTS

• Diffusion and perfusion characteristics are assessable with only three b-values. • Association of b = 0, 50, 800 s/mm²is superior to b = 0, 250, 800 s/mm². • A fourth acquired b-value has no benefit for differential diagnosis. • For liver lesion characterisation, simplified IVIM analysis is superior to ADC determination. • Simplified IVIM approach guarantees numerically stable, voxel-wise results and short acquisition times.

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.

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.

Evaluating histologic differentiation of hepatitis B virus-related hepatocellular carcinoma using intravoxel incoherent motion and AFP levels alone and in combination

PURPOSE

To evaluate histologic differentiation of hepatitis B virus (HBV)-related hepatocellular carcinomas (HCCs) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM)-derived metrics and to compare findings with alpha-fetoprotein (AFP) levels alone and in combination.

MATERIALS AND METHOD

One hundred and six chronic HBV-related HCC patients who underwent IVIM diffusion-weighted magnetic resonance imaging with eleven b values were enrolled. Mean ADC, diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values were determined for all detected lesions. The metrics and AFP levels of different histologically differentiated groups were compared. Spearman's rank correlation was used to assess the statistical dependence among the histologically differentiated HCCs. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance of these metrics and AFP levels alone and in combination.

RESULTS

ADC, D, and f values and AFP levels were significantly different among well-, moderately, and poorly differentiated HCCs. The four metrics were significantly correlated with histologic differentiation. The area under the ROC curve (AUC-ROC) of ADC, D, f, and AFP for diagnosing well-differentiated HCCs was 0.903, 0.84, 0.782, and 0.806, respectively, and the AUC-ROC of above metrics for diagnosing poorly differentiated HCCs was 0.787, 0.726, 0.624, and 0.633, respectively. The combination of ADC and AFP provided an AUC-ROC of 0.945 for well-differentiated HCC. However, this did not provide better performance for diagnosing poorly differentiated HCC.

CONCLUSION

ADC, IVIM metrics, and AFP levels may be useful for evaluating histologic differentiation of HBV-related HCCs, and the combination of ADC and AFP provides better diagnostic performance for well-differentiated HCC.

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.

Hepatic fat quantification using the two-point Dixon method and fat color maps based on non-alcoholic fatty liver disease activity score

AIM

The two-point Dixon method for magnetic resonance imaging (MRI) is commonly used to non-invasively measure fat deposition in the liver. The aim of the present study was to assess the usefulness of MRI-fat fraction (MRI-FF) using the two-point Dixon method based on the non-alcoholic fatty liver disease activity score.

METHODS

This retrospective study included 106 patients who underwent liver MRI and MR spectroscopy, and 201 patients who underwent liver MRI and histological assessment. The relationship between MRI-FF and MR spectroscopy-fat fraction was used to estimate the corrected MRI-FF for hepatic multi-peaks of fat. Then, a color FF map was generated with the corrected MRI-FF based on the non-alcoholic fatty liver disease activity score. We defined FF variability as the standard deviation of FF in regions of interest. Uniformity of hepatic fat was visually graded on a three-point scale using both gray-scale and color FF maps. Confounding effects of histology (iron, inflammation and fibrosis) on corrected MRI-FF were assessed by multiple linear regression.

RESULTS

The linear correlations between MRI-FF and MR spectroscopy-fat fraction, and between corrected MRI-FF and histological steatosis were strong (R²  = 0.90 and R²  = 0.88, respectively). Liver fat variability significantly increased with visual fat uniformity grade using both of the maps (ρ = 0.67-0.69, both P < 0.001). Hepatic iron, inflammation and fibrosis had no significant confounding effects on the corrected MRI-FF (all P > 0.05).

CONCLUSIONS

The two-point Dixon method and the gray-scale or color FF maps based on the non-alcoholic fatty liver disease activity score were useful for fat quantification in the liver of patients without severe iron deposition.

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 MRI in Crohn's disease: Current status and recommendations

Over the past years, technological improvements and refinements in magnetic resonance imaging (MRI) hardware have made high-quality diffusion-weighted imaging (DWI) routinely possible for the bowel. DWI is promising for the detection and characterization of lesions in Crohn's disease (CD) and has been advocated as an alternative to intravenous gadolinium-based contrast agents. Furthermore, quantification using the apparent diffusion coefficient may have value as a biomarker of CD activity and has shown promise. In this article we critically review the literature pertaining to the value of DWI in CD for detection, characterization, and quantification of disease activity and complications. Although the body of supportive evidence is growing, it is clear that well-designed, multicenter studies are required before the role of DWI in clinical practice can be fully established. J. Magn. Reson. Imaging 2016;44:1381-1396.

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.