Non-Gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma

Correlation between diffusion-weighted image-derived parameters and dynamic contrast-enhanced magnetic resonance imaging-derived parameters in the orofacial region

Background

Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are widely used in the orofacial region. Furthermore, quantitative analyses have proven useful. However, a few reports have described the correlation between DWI-derived parameters and DCE-MRI-derived parameters, and the results have been controversial.

Purpose

To evaluate the correlation among parameters obtained by DWI and DCE-MRI and to compare them between benign and malignant lesions.

Material and Methods

Fifty orofacial lesions were analysed. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) were estimated by DWI. For DCE-MRI, TK model analysis was performed to estimate physiological parameters, for example, the influx forward volume transfer constant into the extracellular-extravascular space (EES) (Ktrans) and fractional volumes of EES and plasma components (ve and vp).

Results

Both ADC and D showed a moderate positive correlation with ve (ρ = 0.640 and 0.645, respectively). Ktrans showed a marginally weak correlation with f (ρ = 0.296), while vp was not correlated with f or D*; therefore, IVIM perfusion-related parameters and TK model perfusion-related parameters were not straightforward. Both D and ve yielded high diagnostic power between benign lesions and malignant tumours with areas under the curve (AUCs) of 0.830 and 0.782, respectively.

Conclusion

Both D and ve were reliable parameters that were useful for the differential diagnosis. In addition, the true diffusion coefficient (D) was affected by the fractional volume of EES.

A comparison among gamma distribution, intravoxel incoherent motion, and mono-exponential models with turbo spin-echo diffusion-weighted MR imaging in the differential diagnosis of orofacial lesions

OBJECTIVES

To compare the gamma distribution (GD), intravoxel incoherent motion (IVIM), and monoexponential (ME) models in terms of their goodness-of-fit, correlations among the parameters, and the effectiveness in the differential diagnosis of various orofacial lesions.

METHODS

A total of 85 patients underwent turbo spin-echo diffusion-weighted imaging with six b-values. The goodness-of-fit of three models was assessed using Akaike Information Criterion. We analysed the correlations and compared the effectiveness in the differential diagnosis among the parameters of GD model (κ, shape parameter; θ, scale parameter; fractions of diffusion: ƒ₁, cellular component; ƒ₂, extracellular diffusion; ƒ₃, perfusion component), IVIM model (D, true diffusion coefficient; D*, pseudodiffusion coefficient; f, perfusion fraction), and ME model (apparent diffusion coefficient, ADC).

RESULTS

The GD and IVIM models showed a better goodness-of-fit than the ME model (p < 0.05). ƒ₁ had strong negative correlations with D and ADC (ρ = -0.901 and -0.937, respectively), while ƒ₃ had a moderate positive correlation with f (ρ = 0.661). Malignant entity presented significantly higher ƒ₁ and lower D and ADC than benign entity (p < 0.0001). Malignant lymphoma had significantly higher ƒ₁ in comparison to squamous cell carcinoma (p = 0.0007) and granulation (p = 0.0075). The trend in ƒ₁ was opposite to the trend in D. Malignant lymphoma had significant lower ƒ₃ than squamous cell carcinoma (p = 0.005) or granulation (p = 0.0075).

CONCLUSIONS

The strong correlations were found between the GD- and IVIM-derived parameters. Furthermore, the GD model's parameters were useful for characterising the pathological structure in orofacial lesions.

Accuracy of quantitative diffusion-weighted imaging for differentiating benign and malignant pancreatic lesions: a systematic review and meta-analysis

BACKGROUND

A variety of imaging techniques can be used to evaluate diffusion characteristics to differentiate malignant and benign pancreatic lesions. The diagnostic performance of diffusion parameters has not been systematic assessed.

PURPOSE

We aimed to investigate the diagnostic efficacy of quantitative diffusion-weighted imaging (DWI) for pancreatic lesions.

METHODS

A literature search was conducted using the PubMed, Embase, and Cochrane Library databases for studies from inception to March 30, 2020, which involves the quantitative diagnostic performance of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in the pancreas. Studies were reviewed according to inclusion and exclusion criteria. The quality of articles was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUATAS-2). A bivariate random-effects model was used to evaluate pooled sensitivities and specificities. Univariable meta-regression analysis was used to test the effects of factors that contributed to the heterogeneity.

RESULTS

A total of 31 studies involving 1558 patients were ultimately eligible for data extraction. The lowest heterogeneity was found in specificity of perfusion fraction (f) with the I2 value was 17.97% and Cochran p value was 0.28. However, high heterogeneities were found for the other parameters (all I2 > 50%). There was no publication bias found in funnel plot (p = 0.30) for the apparent diffusion coefficient (ADC) parameter. The pooled sensitivities for ADC, f, pure diffusion coefficient (D), and pseudo diffusivity coefficient (D*) were 83%, 81%, 76%, and 84%, respectively. The pooled specificities for ADC, f, D, and D* were 87%, 83%, 69%, and 81% respectively. The areas under the curves for ADC, f, D, and D* were 0.92, 0.87, 0.79, and 0.87 respectively.

CONCLUSION

Quantitative DWI and IVIM have a good diagnostic performance for differentiating malignant and benign pancreatic lesions.

KEY POINTS

• IVIM has high sensitivity and specificity (84% and 83%, respectively) for differential diagnosis of pancreatic lesions, which is comparable to that of the ADC (83% and 87%, respectively). • The ADC has an excellent diagnostic performance for differentiating malignant from benign IPMNs (sensitivity, 0.83; specificity, 0.92); the f has the best diagnostic performance for differentiating pancreatic carcinoma from PNET (sensitivity, 0.85; specificity, 0.85). • For the ADC, using a maximal b value < 800 s/mm2 has a higher diagnostic accuracy than ≥ 800 s/mm2; performing in a high field strength (3.0 T) system has a higher diagnostic accuracy than a low field strength (1.5 T) for pancreatic lesions.

Value of intravoxel incoherent motion and diffusion kurtosis imaging in predicting peritumoural infiltration of soft-tissue sarcoma: a prospective study based on MRI-histopathology comparisons

AIM

To investigate the performance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in the identification of peritumoural infiltration of soft-tissue sarcoma (STS).

MATERIALS AND METHODS

From July 2018 to January 2020, 34 STS patients who underwent 3-T magnetic resonance imaging (MRI), including IVIM and DKI, were reviewed. The standard apparent diffusion coefficient (ADC), true diffusion (D), pseudo-diffusion coefficient (D∗), perfusion fraction (f), mean kurtosis (MK), and mean diffusion (MD) of each lesion were analysed independently by two observers. An MRI-histopathology control method was used to ensure the correspondence of MRI sections with histopathological sections. Differences in STS with and without infiltration were evaluated. The area under the curve (AUC) was used to determine the best cut-off point for different parameters. Interobserver agreement was assessed using the intraclass correlation coefficient.

RESULTS

Standard ADC, D, MK, and MD values reliably distinguished STS that had positive and negative infiltration. The MD value had the best diagnostic performance. Use of an MD cut-off value of 2.35 × 10^-3 mm²/s to distinguish positive and negative infiltration had an AUC of 0.85, accuracy of 88.2%, sensitivity of 94.4%, and specificity of 81.3%. The two independent observers had nearly perfect agreement for all parameters.

CONCLUSION

The standard ADC and D value of IVIM, and the MK and MD values of DKI reliably identify the presence of peritumoural infiltration of STS.

Gamma distribution model of diffusion MRI for the differentiation of primary central nerve system lymphomas and glioblastomas

The preoperative imaging-based differentiation of primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBs) is of high importance since the therapeutic strategies differ substantially between these tumors. In this study, we investigate whether the gamma distribution (GD) model is useful in this differentiation of PNCSLs and GBs. Twenty-seven patients with PCNSLs and 57 patients with GBs were imaged with diffusion-weighted imaging using 13 b-values ranging from 0 to 1000 sec/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) <1.0×10-3 mm2/sec; f2, D >1.0×10-3 and <3.0×10-3 mm2/sec; f3, D >3.0 × 10-3 mm2/sec. The GD model-derived parameters were compared between PCNSLs and GBs. Receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. The correlations with intravoxel incoherent motion (IVIM)-derived parameters were evaluated. The PCNSL group's κ (2.26 ± 1.00) was significantly smaller than the GB group's (3.62 ± 2.01, p = 0.0004). The PCNSL group's f1 (0.542 ± 0.107) was significantly larger than the GB group's (0.348 ± 0.132, p<0.0001). The PCNSL group's f2 (0.372 ± 0.098) was significantly smaller than the GB group's (0.508 ± 0.127, p<0.0001). The PCNSL group's f3 (0.086 ± 0.043) was significantly smaller than the GB group's (0.144 ± 0.062, p<0.0001). The combination of κ, f1, and f3 showed excellent diagnostic performance (area under the curve, 0.909). The f1 had an almost perfect inverse correlation with D. The f2 and f3 had very strong positive correlations with D and f, respectively. The GD model is useful for the differentiation of GBs and PCNSLs.

Diffusional kurtosis imaging in evaluation of microstructural changes of spinal cord in cervical spondylotic myelopathy feasibility study

To explore the value of diffusion kurtosis imaging in the changes of spinal cord microstructures in patients with early cervical spondylotic myelopathy.Twenty nine patients with cervical myelopathy were selected in this study. All images were acquired on a 3.0 T MR scanner (Skyra, Siemens Medical Systems, Germany). The imaging parameters for diffusion kurtosis imaging were as follows: repetition time/echo time, 3000/91 ms; averages, 2; slice thickness/gap, 3/0.3 mm; number of slices, 17; field of view, 230 × 230 mm; Voxel size, 0.4 × 0.4 × 3.0 mm; 3 b-values (0, 1000, and 2000 s/mm) with diffusion encoding in 20 directions for each b-value. Values for fractional anisotropy, mean diffusivity, and mean diffusional kurtosis (MK) were calculated and compared between unaffected and affected spinal cords.In all patients MK was significantly lower in normal appearing spinal cords adjacent to the affected cervical spinal cords than in normal cervical spinal cords (0.862 ± 0.051 vs 0.976 ± 0.0924, P < .0001), but the difference of fractional anisotropy and apparent diffusion coefficient was no significant (P > .05). The affected cervical spinal cords had lower MK (0.716 ± 0.0753), FA and higher apparent diffusion coefficient than normal cervical spinal cords (P < .001).MK values in the cervical spinal cord may reflect microstructural changes of spinal cord damage in cervical myelopathy, and it could potentially provide more information that obtained with conventional diffusion metrics.

Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging

Purpose

To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer.

Methods

A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance.

Results

For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer (p < 0.05). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and K_app (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer.

Conclusion

The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.

Estimation error bound for GRAPPA diffusion-weighted MRI

In recent years, diffusion weight magnetic resonance imaging (DW-MRI) has become one of the most important MRI imaging modalities. The importance of the DW-MRI grew thanks to the combination of parallel magnetic resonance imaging (pMRI) techniques with the echo-planar imaging (EPI), which minimize scan time and lead to reduced distortion, allowing the DW-MRI to become a routine clinical exam. Additionally, this has brought various new parameters that influence image quality and biomarkers used in DW-MRI. This work aims to investigate the effects of these parameters on the estimation quality, by using the Cramér-Rao bound tool, which gives analytical expressions of the lower limit on the estimation error variance of different DW-MRI variables when using the pMRI technique. In particular, these bounds will be used to study and optimize the impact of different factors of generalized autocalibrating partially parallel acquisition (GRAPPA) technique and system parameters on the estimation quality of the desired clinical metrics. Moreover, the obtained results of this study can be exploited and adapted in all human body DW-MRI clinical routines, further improving disease diagnosis, and tractography studies.

The application of a gamma distribution model to diffusion-weighted images of the orofacial region

OBJECTIVES

This study evaluated the correlation among the diffusion-derived parameters obtained by monoexponential (ME), intravoxel incoherent motion (IVIM) and γ distribution (GD) models and compared these parameters among representative orofacial tumours.

METHODS

Ninety-two patients who underwent 1.5 T MRI including diffusion-weighted imaging were included. The shape parameter (κ), scale parameter (θ), ratio of the intracellular diffusion (ƒ₁), extracellular diffusion (ƒ₂) and perfusion (ƒ₃) were obtained by the GD model; the true diffusion coefficient (D) and perfusion fraction (f) were obtained by the IVIM model; and the apparent diffusion coefficient (ADC) was obtained by the ME model.

RESULTS

ƒ₁ had a strongly negative correlation with the ADC (ρ = -0.993) and D (ρ = -0.926). A strong positive correlation between f and ƒ₃ (ρ = 0.709) was found. Malignant lymphoma (ML) had the highest ƒ₁, followed by squamous cell carcinoma (SCC), malignant salivary gland tumours, pleomorphic adenoma (Pleo) and angioma. Both the IVIM and GD models suggested the highest perfusion in angioma and the lowest perfusion in ML. The GD model demonstrated a high extracellular component in Pleo and revealed that the T_4a+T_4b SCC group had a lower ƒ₂ than the T₂+T₃ SCC group, and poor to moderately differentiated SCC had a higher ƒ₁ than highly differentiated SCC.

CONCLUSIONS

Given the correlation among the diffusion-derived parameters, the GD model might be a good alternative to the IVIM model. Furthermore, the GD model's parameters were useful for characterizing the pathological structure.

Quantitative Evaluation of Intravoxel Incoherent Motion and Diffusion Kurtosis Imaging in Assessment of Pathological Grade of Clear Cell Renal Cell Carcinoma

RATIONALE AND OBJECTIVES

To evaluate the diagnostic value of intravoxel incoherent motion and diffusion kurtosis imaging parameters for clear cell renal cell carcinoma (ccRCC) grading.

MATERIALS AND METHODS

A total of 60 patients with pathologically proven ccRCC who underwent intravoxel incoherent motion and diffusion kurtosis imaging were retrospectively evaluated. The standard apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) maps were calculated and compared between high-grade and low-grade ccRCC using Mann-Whitney U test. Receiver-operating characteristic analysis was performed for all parameters.

RESULTS

ADC, D and MD values were significantly lower for high-grade ccRCC compared to low-grade ccRCC (p < 0.05). MK values were significantly higher in high-grade ccRCC compared to low-grade ccRCC (p < 0.05). However, D* and f were not significantly difference between the two groups (p > 0.05). MD had the largest area under the curve (AUC = 0.888), followed by ADC (AUC = 0.796), D (AUC = 0.780), MK (AUC = 0.736), f (AUC = 0.582), and D*(AUC = 0.533).

CONCLUSION

Diffusion-related parameters (D, ADC, MD, and MK) were able to significantly distinguish between low- and high-grade ccRCC. However, perfusion-related parameters (D* and f) were unable to separate high- and low-grade ccRCC. MD may be the most promising parameter for grading ccRCC in the clinic.

Diffusion MR Imaging in the Head and Neck: Principles and Applications

Diffusion imaging is a functional MR imaging tool that creates tissue contrast representative of the random, microscopic translational motion of water molecules within human body tissues. Long considered a cornerstone MR imaging sequence for brain imaging, diffusion-weighted imaging (DWI) increasingly is used for head and neck imaging. This review reports the current state of diffusion techniques for head and neck imaging, including conventional DWI, DWI trace with apparent diffusion coefficient map, diffusion tensor imaging, intravoxel incoherent motion, and diffusion kurtosis imaging. This article describes background physics, reports supportive evidence and potential pitfalls, highlights technical advances, and details practical clinical applications.

Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: a comparative analysis with diffusion-weighted imaging

PURPOSE

We aimed to investigate histogram analysis of diffusion kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) to distinguish between deep myometrial invasion and superficial myometrial invasion in endometrial carcinoma (EC).

METHODS

A total of 118 pathologically confirmed EC patients with preoperative DWI were included. The data were postprocessed with a DKI (b value of 0, 700, 1400, and 2000 s/mm2) model for quantitation of apparent diffusion values (D) and apparent kurtosis coefficient values (K) for non-Gaussian distribution. The apparent diffusion coefficient (ADC) was postprocessed with a conventional DWI model (b values of 0 and 800 s/mm2). A whole-tumor analysis approach was used. Comparisons of the histogram parameters of D, K, and ADC were carried out for the deep myometrial invasion and superficial myometrial invasion subgroups. Diagnostic performance of the imaging parameters was assessed.

RESULTS

The Dmean, D10th, and D90th in deep myometrial invasion group were significantly lower than those in superficial invasion group (P < 0.001, P < 0.001, and P = 0.023, respectively), as well as the ADCmean, ADC10th, and ADC90th (P = 0.001, P = 0.001, and P = 0.042, respectively). The Kmean and K90th were significantly higher in deep invasion group than those in superficial myometrial invasion group (P = 0.002 and P = 0.026, respectively). The D10th, Kmean, and ADC10th had a relatively higher area under the curve (AUC) (0.72, 0.66, and 0.71, respectively) than other parameters for distinguishing deep myometrial invasion of EC. D10th showed a relatively higher AUC than ADC10th for the differentiation of lesions with deep myometrial invasion from those with superficial myometrial invasion (0.72 vs. 0.71), but the variation was not statistically significant (P = 0.35).

CONCLUSION

Distribution of DKI and conventional DWI parameters characterized by histogram analysis may represent an indicator for deep myometrial invasion in EC. Both DKI and DWI models showed relatively equivalent effectiveness.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test–retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland–Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (D_app × 10⁻³ mm²/s) and kurtosis (K_app, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, D_app and K_app mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.

Characterization of breast lesions using diffusion kurtosis model-based imaging: An initial experience

OBJECTIVE

To investigate the characterization of breast lesions using diffusion kurtosis model-based imaging.

METHODS

This prospective study included 120 consecutive patients underwent preoperative DCE-MRI examinations and multi-b-value diffusion-weighted imaging (DWI). Among them, 88 malignant lesions and 44 benign lesions were detected, 56 normal fibroglandular breast tissue were selected as normal control. Conventional apparent diffusion coefficient (ADC), DKI-based parameters mean kurtosis (MK) and mean diffusivity (MD) were analyzed by lesions types and histological subtypes using one-way ANOVA and receiver operating characteristic (ROC) curve.

RESULTS

(1) The malignant group showed significantly lower ADC and MD (1.07±0.32×10-3 mm2/s and 1.30±0.40×10-3 mm2/s, respectively) and higher MK (0.87±0.18) than those in the benign group (1.29±0.26×10-3 mm2/s, 1.62±0.31×10-3 mm2/s and 0.67±0.18) and control group (1.67±0.33×10-3 mm2/s, 2.24±0.28×10-3 mm2/s and 0.52±0.08) with all P < 0.001. (2) Areas under ROC curve (AUC) for diagnosing malignant lesions were 0.936 for MD, 0.911 for MK and 0.897 for ADC, respectively. AUC for MD was significantly higher than that for ADC (P = 0.015). The optimal cut-off value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were as follow: ADC = 1.18×10-3mm2/s, 78.3%, 93.2%, 81.2%, 81.6%, 81.4%; MD = 1.48×10-3mm2/s, 82.2%, 98.3%, 84.4%, 87.8%, 86.2%; MK = 0.78, 91.5%, 85.3%, 89.0%, 85.8%, 87.2%. (3) Invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS) and mucinous adenocarcinoma also showed significant differences among ADC, MD and MK (with P < 0.05).

CONCLUSIONS

MR-DKI parameters enable to improve breast lesion characterization and have diagnostic potential applying to different pathological subtypes of breast cancers.

An alternative approach to contrast-enhanced imaging: diffusion-weighted imaging and T1-weighted imaging identifies and quantifies necrosis in Wilms tumour

OBJECTIVES

Volume of necrosis in Wilms tumour is informative of chemotherapy response. Contrast-enhanced T1-weighted MRI (T1w) provides a measure of necrosis using gadolinium. This study aimed to develop a non-invasive method of identifying non-enhancing (necrotic) tissue in Wilms tumour.

METHODS

In this single centre, retrospective study, post-chemotherapy MRI data from 34 Wilms tumour patients were reviewed (March 2012-March 2017). Cases with multiple b value diffusion-weighted imaging (DWI) and T1w imaging pre- and post-gadolinium were included. Fractional T1 enhancement maps were generated from the gadolinium T1w data. Multiple linear regression determined whether fitted parameters from a mono-exponential model (ADC) and bi-exponential model (IVIM - intravoxel incoherent motion) (D, D*, f) could predict fractional T1 enhancement in Wilms tumours, using normalised pre-gadolinium T1w (T1wnorm) signal as an additional predictor. Measured and predicted fractional enhancement values were compared using the Bland-Altman plot. An optimum threshold for separating necrotic and viable tissue using fractional T1 enhancement was established using ROC.

RESULTS

ADC and D (diffusion coefficient) provided the strongest predictors of fractional T1 enhancement in tumour tissue (p < 0.001). Using the ADC-T1wnorm model (adjusted R2 = 0.4), little bias (mean difference = - 0.093, 95% confidence interval = [- 0.52, 0.34]) was shown between predicted and measured values of fractional enhancement and analysed via the Bland-Altman plot. The optimal threshold for differentiating viable and necrotic tissue was 33% fractional T1 enhancement (based on measured values, AUC = 0.93; sensitivity = 85%; specificity = 90%).

CONCLUSIONS

Combining ADC and T1w imaging predicts enhancement in Wilms tumours and reliably identifies and measures necrotic tissue without gadolinium.

KEY POINTS

• Alternative method to identify necrotic tissue in Wilms tumour without using contrast agents but rather using diffusion and T 1 weighted MRI. • A method is presented to visualise and quantify necrotic tissue in Wilms tumour without contrast. • The proposed method has the potential to reduce costs and burden to Wilms tumour patients who undergo longitudinal follow-up imaging as contrast agents are not used.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging?

OBJECTIVE

To quantitatively compare the diagnostic values of various diffusion parameters obtained from mono- and biexponential diffusion-weighted imaging (DWI) models and diffusion kurtosis imaging (DKI) in differentiating between benign and malignant solitary pulmonary lesions (SPLs).

METHODS

Multiple b-value DWIs and DKIs were performed in 89 patients with SPL by using a 3-T magnetic resonance (MR) imaging unit. The apparent diffusion coefficient (ADC) of various b-value sets, true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), apparent diffusional kurtosis (Kapp), and kurtosis-corrected diffusion coefficient (Dapp) were calculated and compared between the malignant and benign groups using a Mann-Whitney U test. Receiver-operating characteristic analysis was performed for all parameters.

RESULT

The ADC(0, 150) values of malignant tumors were lower than those of the benign group (p = 0.01). The ADC(0, 300), ADC(0, 500), ADC(0, 600), ADC(0, 800), ADC(0, 1000), ADCtotal, D, and Dapp of malignant tumors were significantly lower than those of benign lesions (all p < 0.001). D*, f, and Kapp showed no statistically significant differences between the two groups. ADCtotal showed the highest area under the curve (AUC = 0.862), followed by ADC(0, 800)(AUC = 0.844), ADC(0, 600)(AUC = 0.843), D(AUC = 0.834), ADC(0, 1000)(AUC = 0.834) and ADC(0, 500)(AUC = 0.824), Dapp(AUC = 0.796), and ADC(0, 300) (AUC = 0.773). However, the difference in diagnostic efficacy among these parameters was not statistically significant (p > 0.05).

CONCLUSION

Intravoxel incoherent motion (IVIM) and DKI-derived parameters have similar performance compared with conventional ADC in differentiating SPLs.

KEY POINTS

• Mono- and biexponential DWI and DKI are feasible for differentiating SPLs. • ADC (0, ≥500) has better performance than ADC (0, <500) in assessing SPLs. • IVIM and DKI have similar performance compared with conventional DWI in differentiating SPLs.

Differentiation between malignant and benign musculoskeletal tumors using diffusion kurtosis imaging

OBJECTIVE

The purpose of this study was to evaluate differences in parameters of diffusion kurtosis imaging (DKI) and minimum apparent diffusion coefficient (ADC_min) between benign and malignant musculoskeletal tumors.

MATERIALS AND METHODS

In this prospective study, 43 patients were scanned using a DKI protocol on a 3-T MR scanner. Eligibility criteria were: non-fatty, non-cystic soft tissue or osteolytic tumors; > 2 cm; location in the retroperitoneum, pelvis, leg, or neck; and no prior treatment. They were clinically or histologically diagnosed as benign (n = 27) or malignant (n = 16). In the DKI protocol, diffusion-weighted imaging was performed using four b values (0-2000 s/mm²) and 21 diffusion directions. Mean kurtosis (MK) values were calculated on the MR console. A recently developed software application enabling reliable calculation was used for DKI analysis.

RESULTS

MK showed a strong correction with ADC_min (Spearman's rs = 0.95). Both MK and ADC_min values differed between benign and malignant tumors (p < 0.01). For benign and malignant tumors, the mean MK values (± SD) were 0.49 ± 0.17 and 1.14 ± 0.30, respectively, and ADC_min values were 1.54 ± 0.47 and 0.49 ± 0.17 × 10^-3 mm²/s, respectively. At cutoffs of MK = 0.81 and ADC_min = 0.77 × 10^-3 mm²/s, the specificity and sensitivity for diagnosis of malignant tumors were 96.3 and 93.8% for MK and 96.3 and 93.8% for ADC_min, respectively. The areas under the curve were 0.97 and 0.99 for MK and ADC_min, respectively (p = 0.31).

CONCLUSIONS

MK and ADC_min showed high diagnostic accuracy and strong correlation, reflecting the accuracy of MK. However, no clear added value of DKI could be demonstrated in differentiating musculoskeletal tumors.

Diffusional kurtosis imaging in head and neck cancer: On the use of trace-weighted images to estimate indices of non-Gaussian water diffusion

PURPOSE

While previous studies have demonstrated the feasibility and potential usefulness of quantitative non-Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion-weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (K_x , K_y , K_z ) and (D_x , D_y , D_z ) - namely K and D, respectively - represent the best-possible surrogates of directionless DKI-derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion-weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion-sensitizing gradient along (x, y, z), referred to as trace-weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI-derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients.

METHODS

Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b-values: 0, 500, 1000, 1500, 2000 s/mm² ). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI-derived indices were estimated, voxel-by-voxel, using single diffusion-weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (D_err ) and K (K_err ). Also, diffusivity anisotropy (D_anis ) and diffusional kurtosis anisotropy (K_anis ) were estimated. Agreements between the two estimation methods were assessed by Bland-Altman plots. The Spearman rank correlation test was used to study the correlations between K_err /D_err and D_anis /K_anis. RESULTS: The median (95% confidence interval) K_err and D_err were 5.1% (0.8%, 32.6%) and 1.7% (-2.5%, 5.3%), respectively. A significant relationship was observed between K_err  and D_anis (correlation coefficient R = 0.694, P < 0.0001), as well as between K_err and K_anis (R = 0.848, P < 0.0001).

CONCLUSIONS

In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non-negligible for single lesions, and should hence be adopted with caution.

Quantitative diffusion magnetic resonance imaging in head and neck tumors

In patients with head and neck cancer, conventional anatomical magnetic resonance imaging (MRI) scans are commonly used for identification of primary lesion, assessment of structural distortion, and presence of metastatic lymph nodes. However, quantitative analysis of diffusion MRI can provide added value to structural and anatomical evaluation of head and neck tumors (HNT), by differentiation of primary malignant process, prognostic prediction, and treatment monitoring. In this article, we will review the applications of quantitative diffusion MRI in identification of primary malignant tissue, differentiation of tumor pathology, prediction of molecular phenotype, monitoring of treatment response, and evaluation of posttreatment changes in patient with HNT.

In Vivo Imaging Markers for Prediction of Radiotherapy Response in Patients with Nasopharyngeal Carcinoma: RESOLVE DWI versus DKI

In this prospective study, we compared the performance of readout segmentation of long variable echo trains of diffusion-weighted imaging (RESOLVE DWI) and diffusion kurtosis imaging (DKI) for the prediction of radiotherapy response in patients with nasopharyngeal carcinoma (NPC). Forty-one patients with NPC were evaluated. All patients underwent conventional MRI, RESOLVE DWI and DKI, before and after radiotherapy. All patients underwent conventional MRI every 3 months until 1 year after radiotherapy. The patients were divided into response group (RG; 36/41 patients) and no-response group (NRG; 5/41 patients) based on follow-up results. DKI (the mean of kurtosis coefficient, Kmean and the mean of diffusion coefficient, Dmean) and RESOLVE DWI (the minimum apparent diffusion coefficient, ADC_min) parameters were calculated. Parameter values at the pre-treatment period, post-treatment period, and the percentage change between these 2 periods were obtained. All parameters differed between the RG and NRG groups except for the pretreatment Dmean and ADC_min. Kmean-post was considered as an independent predictor of local control, with 87.5% sensitivity and 91.3% specificity (optimal threshold = 0.30, AUC: 0.924; 95% CI, 0.83-1.00). Kmean-post values of DKI have the potential to be used as imaging biomarkers for the early evaluation of treatment effects of radiotherapy on NPC.

Evaluation of Effects of TGF-β1 Inhibition on Gastric Cancer in Nude Mice by Diffusion Kurtosis Imaging and In-Line X-ray Phase Contrast Imaging With Sequential Histology

Background

Accurate and complete response evaluation after treatment is important to implement individualized therapy for gastric cancer.

Purpose

To investigate the effectiveness of diffusion kurtosis imaging (DKI) and in‐line X‐ray phase contrast imaging (ILXPCI) in the assessment of the therapeutic efficacy by transforming growth factor beta 1 (TGF‐β1) inhibition.

Study Type

Prospective animal study.

Animal Model

Thirty nude mice subcutaneous xenotransplantation tumor model of gastric cancer for DKI and 10 peritoneal metastasis nude mice model for ILXPCI.

Field Strength/Sequence

Examinations before and serially at 7, 14, 21, and 28 days after TGF‐β1 inhibition treatment were performed at 3T MRI including T₂‐weighted imaging (T₂WI) and DKI with five b values of 0, 500, 1000, 1500, 2000 s/mm²; ILXPCI examinations were performed at 14 days after treatment.

Assessment

DKI parameters (apparent diffusion coefficient [ADC], diffusivity [D] and kurtosis [K]) were calculated by two experienced radiologists after postprocessing.

Statistical Tests

For the differences in all the parameters between the baseline and each timepoint for both the treated and the control mice, the Mann–Whitney test was used. The Spearman correlation test was used to evaluate correlations among the DKI parameters and corresponding pathologic necrosis fraction (NF).

Results

ADC, D, and K values were significantly different between the two groups after treatment (P < 0.05). Serial measurements in the treated group showed that the ADC, D, and K values were significantly different at 7, 14, 21, and 28 days compared with baseline (P < 0.05). There were significant correlations between DKI parameters and NF (ADC, r = 0.865, P < 0.001; D, r = 0.802, P < 0.001; K, r = –0.944, P < 0.001). The ILXPCI results in the treated group showed a stronger absorption area than the control group.

Data Conclusion

DKI may be used to evaluate the complete course therapeutic effects of gastric cancer induced by TGF‐β1 inhibition, and the ILXPCI technique will improve the tumor microstructure resolution.

Level of Evidence: 1

Technical Efficacy: Stage 4

J. Magn. Reson. Imaging 2019;49:1553–1564.

Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma

BACKGROUND

Since neoadjuvant chemotherapy (NAC) has proven a benefit for locally advanced nasopharyngeal carcinoma (NPC), early response evaluation after chemotherapy is important to implement individualized therapy for NPC in the era of precision medicine.

PURPOSE

To determine the combined and independent contribution between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) in the early monitoring of NAC response for NPC.

STUDY TYPE

Prospective.

POPULATION

Fifty-three locally advanced NPC patients.

FIELD STRENGTH/SEQUENCE

Four examinations before and at 4, 20, and 40 days after NAC initiation were performed at 3T MRI including DCE-MRI and DKI (b values = 0, 500, 1000, 1500 s/mm² ).

ASSESSMENT

DCE-MRI parameters (K^trans [the volume transfer constant of Gd-DTPA], k_ep [rate constant], ν_e [the extracellular volume fraction of the imaged tissue], and ν_p [the blood volume fraction]) and DKI parameters (D_app [apparent diffusion for non-Gaussian distribution] and K_app [apparent kurtosis coefficient]) were analyzed using dedicated software.

STATISTICAL TESTS

MRI parameters and their corresponding changes were compared between responders and nonresponders after one or two NAC cycles treatment using independent-samples Student's t-test or Mann-Whitney U-test depending on the normality contribution test and then followed by logistic regression and receiver operating characteristic curve (ROC) analyses.

RESULTS

The responder group (RG) patients presented significantly higher mean K^trans and D_app values at baseline and larger Δ K ( 0 - 4 ) trans , Δv_p(0-4) , and ΔD_app(0-4) values after either one or two NAC cycles compared with the nonresponder group (NRG) patients (all P < 0.05). ROC analyses demonstrated the higher diagnostic accuracy of combined DCE-MRI and DKI model to distinguish nonresponders from responders after two NAC cycles than using DCE-MRI (0.987 vs. 0.872, P = 0.033) or DKI (0.987 vs. 0.898, P = 0.047) alone.

DATA CONCLUSION

Combined DCE-MRI and DKI models had higher diagnostic accuracy for NAC assessment compared with either model used independently.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1208-1216.

Utility of Readout-Segmented Echo-Planar Imaging-Based Diffusion Kurtosis Imaging for Differentiating Malignant from Benign Masses in Head and Neck Region

Objective

To compare the diagnostic performance of readout-segmented echo-planar imaging (RS-EPI)-based diffusion kurtosis imaging (DKI) and that of diffusion-weighted imaging (DWI) for differentiating malignant from benign masses in head and neck region.

Materials and Methods

Between December 2014 and April 2016, we retrospectively enrolled 72 consecutive patients with head and neck masses who had undergone RS-EPI-based DKI scan (b value of 0, 500, 1000, and 1500 s/mm²) for pretreatment evaluation. Imaging data were post-processed by using monoexponential and diffusion kurtosis (DK) model for quantitation of apparent diffusion coefficient (ADC), apparent diffusion for Gaussian distribution (D_app), and apparent kurtosis coefficient (K_app). Unpaired t test and Mann-Whitney U test were used to compare differences of quantitative parameters between malignant and benign groups. Receiver operating characteristic curve analyses were performed to determine and compare the diagnostic ability of quantitative parameters in predicting malignancy.

Results

Malignant group demonstrated significantly lower ADC (0.754 ± 0.167 vs. 1.222 ± 0.420, p < 0.001) and D_app (1.029 ± 0.226 vs. 1.640 ± 0.445, p < 0.001) while higher K_app (1.344 ± 0.309 vs. 0.715 ± 0.249, p < 0.001) than benign group. Using a combination of D_app and K_app as diagnostic index, significantly better differentiating performance was achieved than using ADC alone (area under curve: 0.956 vs. 0.876, p = 0.042).

Conclusion

Compared to DWI, DKI could provide additional data related to tumor heterogeneity with significantly better differentiating performance. Its derived quantitative metrics could serve as a promising imaging biomarker for differentiating malignant from benign masses in head and neck region.

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.

Histogram analysis of stretched-exponential and monoexponential diffusion-weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma

BACKGROUND

Noninvasive measures to evaluate the aggressiveness of prostate carcinoma (PCa) may benefit patients.

PURPOSE

To assess the value of stretched-exponential and monoexponential diffusion-weighted imaging (DWI) for predicting the aggressiveness of PCa.

STUDY TYPE

Retrospective study.

SUBJECTS

Seventy-five patients with PCa.

FIELD STRENGTH

3T DWI examinations were performed using b-values of 0, 500, 1000, and 2000 s/mm² .

ASSESSMENT

The research were based on entire-tumor histogram analysis and the reference standard was radical prostectomy.

STATISTICAL TESTS

The correlation analysis was programmed with Spearman's rank-order analysis between the histogram variables and Gleason grade group (GG). Receiver operating characteristic (ROC) regression was used to analyze the ability of these histogram variables to differentiate low-grade (LG) from intermediate/high-grade (HG) PCa.

RESULTS

The percentiles and mean of apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were correlated with GG (ρ: 0.414-0.593), while there was no significant relation among α value, skewnesses, and kurtosises with GG (ρ:0.034-0.323). HG tumors (ADC:484 ± 136, 592 ± 139, 670 ± 144, 788 ± 146, 895 ± 141 mm² /s; DDC: 410 ± 142, 532 ± 172, 666 ± 193, 786 ± 196, 914 ± 181 mm² /s) had lower values in the 10^th , 25^th , 50^th , 75^th percentiles and means than LG tumors (ADC: 644 ± 779, 737 ± 84, 836 ± 83, 919 ± 82, 997 ± 107 mm² /s; DDC: 552 ± 82, 680 ± 94, 829 ± 112, 931 ± 106, 1045 ± 100 mm² /s). However, there was no difference between LG and HG tumors in α value (0.671 ± 0.041 vs. 0.633 ± 0.114), kurtosises (ADC 0.09 vs. 0.086; DDC -0.033 vs. -0.317), or skewnesses (ADC -0.036 vs. 0.073; DDC -0.063 vs. 0.136). The above statistics were P < 0.01. ADC10 with AUC = 0.840 and DDC10 with AUC = 0.799 were similar in discriminating between LG and HG PCa at P < 0.05.

DATA CONCLUSION

Histogram variables of DDC and ADC may predict the aggressiveness of PCa, while α value does not. The abilities of ADC10 and DDC10 to discriminate LG from HG tumors were similar, and both better than their respective means.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:491-498.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status

OBJECTIVES

To explore the correlations of parameters derived from standard diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) with the Ki-67 proliferation status.

METHODS

Seventy-five patients with histologically proven sinonasal malignancies who underwent standard DWI, DKI and IVIM were retrospectively reviewed. The mean, minimum, maximum and whole standard DWI [apparent diffusion coefficient (ADC)], DKI [diffusion kurtosis (K) and diffusion coefficient (Dk)] and IVIM [pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f)] parameters were measured and correlated with the Ki-67 labelling index (LI). The Ki-67 LI was categorised as high (> 50%) or low (≤ 50%).

RESULTS

The K and f values were positively correlated with the Ki-67 LI (rho = 0.295~0.532), whereas the ADC, Dk and D values were negatively correlated with the Ki-67 LI (rho = -0.443~-0.277). The ADC, Dk and D values were lower, whereas the K value was higher in sinonasal malignancies with a high Ki-67 LI than in those in a low Ki-67 LI (all p < 0.05). A higher maximum K value (K_max > 0.977) independently predicted a high Ki-67 status [odds ratio (OR) = 7.614; 95% confidence interval (CI) = 2.197-38.674; p = 0.017].

CONCLUSION

ADC, Dk, K, D and f are correlated with Ki-67 LI. K_max is the strongest independent factor for predicting Ki-67 status.

KEY POINTS

• DWI-derived parameters from different models are capable of providing different pathophysiological information. • DWI, DKI and IVIM parameters are associated with Ki-67 proliferation status. • K _max derived from DKI is the strongest independent factor for the prediction of Ki-67 proliferation status.

Effects of diffusion time on non-Gaussian diffusion and intravoxel incoherent motion (IVIM) MRI parameters in breast cancer and hepatocellular carcinoma xenograft models

Background

Perfusion-related intravoxel incoherent motion (IVIM) and non-Gaussian diffusion magnetic resonance (MR) parameters are becoming important biomarkers for differentiating malignant from benign tumors without contrast agents. However, diffusion-time dependence has rarely been investigated in tumors.

Purpose

To investigate the relationship between diffusion time and diffusion parameters in breast cancer and hepatocellular carcinoma xenograft mouse models.

Material and Methods

Diffusion-weighted MR images (DWI) were obtained on a 7-T magnetic resonance imaging (MRI) scanner at two different diffusion times (9.6 ms and 27.6 ms) in human breast cancer (MDA-MB-231) and hepatocellular carcinoma (HepG2 and PLC/PRF/5) xenograft mouse models. Perfusion-related IVIM (fIVIM and D*) and non-Gaussian diffusion (ADC₀ and K) parameters were estimated. Parametric maps of diffusion changes with the diffusion times were generated using a synthetic apparent diffusion coefficient (sADC) obtained from b = 438 and 2584 s/mm².

Results

ADC₀ values significantly decreased when diffusion times were changed from 9.6 ms to 27.6 ms in MDA-MB-231, HepG2, and PLC/PRF/5 groups (P = 0.0163, 0.0351, and 0.0170, respectively). K values significantly increased in MDA-MB-231 and HepG2 groups (P < 0.0003 and = 0.0007, respectively); however, no significant difference was detected in the PLC/PRF/5 group. fIVIM values increased, although not significantly (P = 0.164–0.748). The maps of sADC changes showed that diffusion changes with the diffusion time were not homogeneous across tumor tissues.

Conclusion

Diffusion MR parameters in both breast cancer and HCC xenograft models were found to be diffusion time-dependent. Our results show that diffusion time is an important parameter to consider when interpreting DWI data.

Diffusion kurtosis imaging of a human nasopharyngeal carcinoma xenograft model: Initial experience with pathological correlation

PURPOSE

The aim of this study was to investigate the relationship between diffusion kurtosis imaging (DKI)-related parameters and pathological measures using human nasopharyngeal carcinoma (NPC) xenografts in a nude mouse model.

MATERIALS AND METHODS

Twenty-six BALB/c-nu nude mice were divided into two groups that were injected with two different nasopharyngeal squamous cell carcinoma cell lines (CNE1 and CNE2). DK magnetic resonance (MR) imaging was performed on a 3.0 Tesla MR scanner. DWI and DKI-related parameters, including apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were measured. Mice were euthanatized when the maximum diameter of the primary tumor reached 1.5cm after MR scanning. Tumor tissues were then processed for hematoxylin and eosin staining. The pathological images were analyzed using a computer-aided pixel-wise clustering method to evaluate tumor cellular density, nuclei portion, cytoplasm portion, extracellular space portion, the ratio of nuclei to cytoplasm and the ratio of nuclei to extracellular space. The relationships between DWI and DKI-related parameters and pathological features were analyzed statistically.

RESULTS

The ADC and MD values of the CNE1 group (1.16±0.24×10^-3mm²/s, 2.28±0.29×10^-3mm²/s) was higher than that of the CNE2 group (0.82±0.14×10^-3mm²/s, 1.53±0.24×10^-3mm²/s, P<0.001), but the MK values between the two groups were not significantly different (CNE1: 0.55±0.14; CNE2: 0.47±0.23; P>0.05). A Pearson test showed that the ADC and MD values were significantly correlated with cellular density, nuclei portion, extracellular space portion and the ratio of nuclei to extracellular space (r=-0.861; -0.909, P<0.001; r=-0.487; 0.591, P<0.05; r=0.567; 0.625, P<0.05; r=-0.645; -0.745, P<0.001, respectively). The MK values were significantly correlated with nuclei portion, cytoplasm portion and the ratio of nuclei to cytoplasm (r=-0.475, P<0.05; r=0.665, P<0.001; r=-0.494, P<0.05, respectively).

CONCLUSION

The preliminary animal results suggest that DKI findings can provide valuable bio-information for NPC tissue characterization. DKI imaging might be utilized as a surrogate biomarker for the non-invasive assessment of tumor microstructures.

Diffusion-kurtosis imaging predicts early radiotherapy response in nasopharyngeal carcinoma patients

In this prospective study, we analyzed diffusion kurtosis imaging (DKI) parameters to predict the early response to radiotherapy in 23 nasopharyngeal carcinoma (NPC) patients. All patients underwent conventional magnetic resonance imaging (MRI) and DKI before and after radiotherapy. The patients were divided into response (RG; no residual tumors; 16/23 patients) and no-response (NRG; residual tumors; 7/23 patients) groups, based on MRI and biopsy results 3 months after radiotherapy. The maximum diameter of tumors in RG and NRG patients were similar prior to radiotherapy (p=0.103). The pretreatment diffusion coefficient (D) parameters (D_axis, D_mean and D_rad) were higher in RG than NRG patients (p=0.022, p=0.027 and p=0.027). Conversely, the pre-treatment fractional anisotropy (FA) and kurtosis coefficient (K) parameters (K_axis, K_fa, K_mean, K_rad and Mkt) were lower in RG than NRG patients (p=0.015, p=0.022, p=0.008, p=0.004, p=0.001, p=0.002). The K_rad coefficient (0.76) was the best parameter to predict the radiotherapy response. Based on receiver operating characteristic curve analysis K_rad showed 71.4% sensitivity and 93.7% specificity (AUC: 0.897, 95% CI, 0.756-1). Multivariate analysis indicated DKI parameters were independent prognostic factors for the short-term effect in NPC. Thus, DKI predicts the early response to radiotherapy in NPC patients.

Non-Mono-Exponential Analysis of Diffusion-Weighted Imaging for Treatment Monitoring in Prostate Cancer Bone Metastases

Diffusion-weighted imaging quantified using the mono-exponential model has shown great promise for monitoring treatment response in prostate cancer bone metastases. The aim of this prospective study is to evaluate whether non-mono-exponential diffusion models better describe the water diffusion properties and may improve treatment response assessment. Diffusion-weighted imaging data of 12 treatment-naïve patients with 34 metastases acquired before and at one, two, and three months after initiation of antiandrogen treatment are analysed using the mono-exponential, the intravoxel incoherent motion, the stretched exponential, and the statistical model. Repeatability of the fitted parameters and changes under therapy are quantified. Model preference is assessed and correlation coefficients across times are calculated to delineate the relationship between the prostate-specific antigen levels and the diffusion parameters as well as between the diffusion parameters within each model. There is a clear preference for non-mono-exponential diffusion models at all time points. Particularly the stretched exponential is favoured in approximately 60% of the lesions. Its parameters increase significantly in response to treatment and are highly repeatable. Thus, the stretched exponential may be utilized as a potential optimal model for monitoring treatment response. Compared with the mono-exponential model, it may provide complementary information on tissue properties and improve response assessment.

Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations

OBJECTIVES

To assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T.

METHODS

Thirty-six female patients (median age 53, range 32-75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels.

RESULTS

All patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability.

CONCLUSION

DW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.

KEY POINTS

• Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders. • Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders. • The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders. • The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability. • Treatment effects can potentially be assessed by non-mono-exponential diffusion models.

Differentiation of olfactory neuroblastomas from nasal squamous cell carcinomas using MR diffusion kurtosis imaging and dynamic contrast-enhanced MRI

PURPOSE

To evaluate the use of magnetic resonance (MR) diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MR imaging (DCE-MRI) in the differentiation of olfactory neuroblastomas (ONBs) from squamous cell carcinomas (SCCs).

MATERIALS AND METHODS

DKI and DCE-MRI were performed in 17 patients with ONBs and 23 patients with SCCs on a 3T MR scanner. Parameters derived from DKI and DCE-MRI were measured and compared between ONBs and SCCs using an independent samples t-test. The sensitivity, specificity, accuracy, positive predictive values (PPV), negative predictive values (NPV), and the area under the receiver operating characteristic (ROC) curve were determined.

RESULTS

The mean kurtosis (K) value of ONBs was significantly higher than that of SCCs (P < 0.001), and the mean fractional volume in the extravascular extracellular space (V_e ) value of ONBs was lower than that of SCCs (P < 0.001). The ROC curve analyses yielded a cutoff K value of 0.953, with a sensitivity of 94.1%, a specificity of 69.6%, and an accuracy of 80.0%; the cutoff V_e value was 0.493, with a sensitivity of 70.6%, a specificity of 95.7%, and an accuracy of 85.0%. A parallel test with K value >0.953 or V_e value ≤0.493 achieved a sensitivity of 94.1%, a specificity of 100.0%, and an accuracy of 97.5% for differentiating ONBs from SCCs.

CONCLUSION

The K value of DKI and V_e value of DCE-MRI have potential use in the differentiation of ONBs and SCCs.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:354-361.

State of the art MRI in head and neck cancer

Head and neck cancer affects more than 11,000 new patients per year in the UK¹ and imaging has an important role in the diagnosis, treatment planning, and assessment, and post-treatment surveillance of these patients. The anatomical detail produced by magnetic resonance imaging (MRI) is ideally suited to staging and follow-up of primary tumours and cervical nodal metastases in the head and neck; however, anatomical images have limitations in cancer imaging and so increasingly functional-based MRI techniques, which provide molecular, metabolic, and physiological information, are being incorporated into MRI protocols. This article reviews the state of the art of these functional MRI techniques with emphasis on those that are most relevant to the current management of patients with head and neck cancer.

Diffusional kurtosis imaging (DKI) incorporation into an intravoxel incoherent motion (IVIM) MR model to measure cerebral hypoperfusion induced by hyperventilation challenge in healthy subjects

OBJECTIVES

The objectives were to investigate the diffusional kurtosis imaging (DKI) incorporation into the intravoxel incoherent motion (IVIM) model for measurements of cerebral hypoperfusion in healthy subjects.

MATERIALS AND METHODS

Eight healthy subjects underwent a hyperventilation challenge with a 4-min diffusion weighted imaging protocol, using 8 b values chosen with the Cramer-Rao Lower Bound optimization approach. Four regions of interest in gray matter (GM) were analyzed with the DKI-IVIM model and the bi-exponential IVIM model, for normoventilation and hyperventilation conditions.

RESULTS

A significant reduction in the perfusion fraction (f) and in the product fD* of the perfusion fraction with the pseudodiffusion coefficient (D*) was found with the DKI-IVIM model, during the hyperventilation challenge. In the cerebellum GM, the percentage changes were f: -43.7 ± 40.1, p = 0.011 and fD*: -50.6 ± 32.1, p = 0.011; in thalamus GM, f: -47.7 ± 34.7, p = 0.012 and fD*: -47.2 ± 48.7, p = 0.040. In comparison, using the bi-exponential IVIM model, only a significant decrease in the parameter fD* was observed for the same regions of interest. In frontal-GM and posterior-GM, the reduction in f and fD* did not reach statistical significance, either with DKI-IVIM or the bi-exponential IVIM model.

CONCLUSION

When compared to the bi-exponential IVIM model, the DKI-IVIM model displays a higher sensitivity to detect changes in perfusion induced by the hyperventilation condition.

The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging

Objectives

The aim of this study is to comprehensively evaluate the advantage of diffusion kurtosis imaging (DKI) in distinguishing pathological complete response (pCR) from non-pCR patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiation therapy (CRT) in comparison to conventional diffusion-weighted imaging (DWI).

Material and Methods

Fifty-six consecutive patients diagnosed with LARC were prospectively enrolled and underwent pre- and post-CRT MRI on a 3.0 T MRI scanner. Apparent diffusion coefficient (ADC), mean diffusion (MD) and mean kurtosis (MK) values of the tumor were measured in pre- and post-CRT phases and then compared to histopathologic findings after total mesorectal excision (TME). Both Mann-Whitney U-test and Kruskal-Wallis test were used as statistical methods. Diagnostic performance was determined using receiver operating characteristic (ROC) curve analysis.

Results

For a total of 56 rectal lesions (pCR, n = 14; non-pCR, n = 42), the MK_pre and MK_post values were much lower for the pCR patients (mean±SD, 0.72±0.09 and 0.56±0.06, respectively) than those for the non-pCR patients (0.89±0.11 and 0.68±0.08, respectively) (p < 0.001). The ADC_post and the change ratio of apparent diffusion coefficient (ADC_ratio) values was significantly higher for the pCR patients (mean±SD, 1.31±0.13 and 0.64±0.34, respectively) than for the non-pCR patients (1.12±0.16 and 0.33±0.27, respectively) (p < 0.001 and p = 0.001, respectively). In addition, the MD_post and the change ratio of mean diffusion (MD_ratio) (2.45±0.33 vs. 1.95±0.30, p < 0.001; 0.80±0.43 vs. 0.35±0.32, p < 0.001, respectively) also increased, whereas the ADC_pre, MD_pre and the change ratio of mean kurtosis (MK_ratio) of the pCR (0.82±0.11, 1.40±0.21, and 0.23±0.010, respectively) exhibited a neglectable difference with that of the non-pCR (p = 0.332, 0.269, and 0.678, respectively). The MK_post showed relatively high sensitivity (92.9%) and high specificity (83.3%) in comparison to other image indices. The area under the receiver operating characteristic curve (AUROC) that is available for the assessment of pCR using MK_post (0.908, cutoff value = 0.6196) were larger than other parameters and the overall accuracy of MK_post (85.7%) was the highest.

Conclusions

Both DKI and conventional DWI hold great potential in predicting treatment response to neoadjuvant chemoradiation therapy in rectal cancer. The DKI parameters, especially MK_post, showed a higher specificity than conventional DWI in assessing pCR and non-pCR in patients with LARC, but the pre-CRT ADC and MD are unreliable.

Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI

OBJECTIVES

Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours.

METHODS

Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm-2). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively.

RESULTS

Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models.

CONCLUSIONS

Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours.

KEY POINTS

• Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours. • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours. • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types. • Micro-structural features are likely to affect parameters in non-mono-exponential models differently.

Non-Gaussian diffusion imaging for malignant and benign pulmonary nodule differentiation: a preliminary study

BACKGROUND

Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) has demonstrated inconsistent results in pulmonary nodule differentiation. Diffusion kurtosis imaging (DKI), which quantifies non-Gaussian diffusion, is believed to better characterize tissue micro-structure than conventional DWI.

PURPOSE

To assess the feasibility of DKI in human lungs and to compare its diagnostic value with standard DWI in differentiating malignancies from benign pulmonary nodules.

MATERIAL AND METHODS

Thirty-five pulmonary nodules in 32 consecutive patients were evaluated by DKI by using 3b-values of 0, 500, and 1000 s/mm² and conventional DWI with b values of 0 and 800 s/mm². Two observers independently evaluated and compared diagnostic accuracy of mean kurtosis (MK) and ADC values in differentiating malignancies from benign pulmonary nodules. The intra- and inter-observer repeatability (intra-class correlation coefficient [ICC]) were also assessed for each derived measures.

RESULTS

The diagnostic accuracy, and the area under curve (AUC) in differentiating malignancies from benign pulmonary nodule, were not significantly higher for MK (Obs. 1a: 85.70%, 0.87; Obs. 1b: 80.00%, 0.80; and Obs. 2: 82.80%, 0.91) as compared to ADC (Obs. 1a: 77.14%, 0.81; Obs. 1b: 80.00%, 0.85; and Obs. 2: 77.14%, 0.85 respectively). The intra- and inter-observer agreement (ICC) for malignant and benign lesions was substantial for each reading.

CONCLUSION

The initial results of this study indicate the feasibility of DKI in human lungs. However, there was no significant benefit of DKI derived MK values over ADC for malignant and benign pulmonary nodule differentiation.

Comparison of non-Gaussian and Gaussian diffusion models of diffusion weighted imaging of rectal cancer at 3.0 T MRI

Water molecular diffusion in vivo tissue is much more complicated. We aimed to compare non-Gaussian diffusion models of diffusion-weighted imaging (DWI) including intra-voxel incoherent motion (IVIM), stretched-exponential model (SEM) and Gaussian diffusion model at 3.0 T MRI in patients with rectal cancer, and to determine the optimal model for investigating the water diffusion properties and characterization of rectal carcinoma. Fifty-nine consecutive patients with pathologically confirmed rectal adenocarcinoma underwent DWI with 16 b-values at a 3.0 T MRI system. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models (IVIM-mono, IVIM-bi and SEM) on primary tumor and adjacent normal rectal tissue. Parameters of standard apparent diffusion coefficient (ADC), slow- and fast-ADC, fraction of fast ADC (f), α value and distributed diffusion coefficient (DDC) were generated and compared between the tumor and normal tissues. The SEM exhibited the best fitting results of actual DWI signal in rectal cancer and the normal rectal wall (R² = 0.998, 0.999 respectively). The DDC achieved relatively high area under the curve (AUC = 0.980) in differentiating tumor from normal rectal wall. Non-Gaussian diffusion models could assess tissue properties more accurately than the ADC derived Gaussian diffusion model. SEM may be used as a potential optimal model for characterization of rectal cancer.

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

PURPOSE

To determine the utility of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging (DWI) models in differentiating between minimal fat angiomyolipoma (MFAML) and clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

One hundred thirty-one patients with pathologically confirmed MFAML (n = 27) or ccRCC (n = 104) underwent multi-b value DWI (0∼1700 s/mm² ) imaging at 3.0 Tesla MRI. An isotropic apparent diffusion coefficient (ADC) was calculated from diffusion-weighted images by using a monoexponential model. A pseudo-ADC (D_p ), true ADC (D_t ), and perfusion fraction (f_p ) were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index (α) and distributed diffusion coefficient (DDC) were calculated from diffusion-weighted images by using a stretched exponential model. All parameters were compared between MFAML and ccRCC by using the Student's t test. Receiver operating characteristic and intraclass correlation coefficient analysis were used for statistical evaluations.

RESULTS

ADC, D_t , and α values were significantly lower in the MFAML group than in the ccRCC group (P < 0.001). D_p , f_p , and DDC values were slightly higher in the MFAML group than in the ccRCC group; however, the difference was not significant (P = 0.136, 0.090, and 0.424, respectively). The AUC values for both α (0.953) and D_t (0.964) were significantly higher than those for ADC (0860), D_p (0.605), f_p (0.596), and DDC (0.477) in the differentiation of MFAML from ccRCC (P < 0.001).

CONCLUSION

Water molecular diffusion heterogeneity index (α) and D_t may provide additional information and could lead to improved differentiation with better sensitivity and specificity between MFAML and ccRCC compared with conventional diffusion parameters.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:240-247.

A Standardized Parameter-Free Algorithm for Combined Intravoxel Incoherent Motion and Diffusion Kurtosis Analysis of Diffusion Imaging Data

OBJECTIVES

The aims of this study were to implement and systematically evaluate the performance of a new parameter-free segmented algorithm for analysis of diffusion imaging data using a combined intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) model of spin diffusion in comparison with the simpler intravoxel incoherent motion (IVIM) model.

MATERIALS AND METHODS

A multistep algorithm was implemented intended to separate diffusion kurtosis from IVIM effects in multi-b-value diffusion measurements using an adaptive b-value threshold technique. For each possible b-value threshold (separating diffusion and perfusion effects), diffusion kurtosis analysis of high b-values is followed by IVIM analysis keeping kurtosis parameters fixed. The b-value threshold with smallest Akaike information criterion is chosen as best model solution. The algorithm was tested in diffusion data sets of the upper abdomen from 8 healthy volunteers with 16 different b-values and compared with a standard multistep IVIM analysis.

RESULTS

The proposed algorithm could successfully be applied to all data sets and provided a significantly better fit of the observed signal decay in all assessed organs (all P < 0.03). Using the proposed IVIM-DKI model of diffusion instead of an IVIM model had a systematic impact on the resulting IVIM parameters: The pure diffusion coefficient and the pseudodiffusion coefficient were significantly increased (P < 0.03 in all assessed organs), accompanied by a decrease in the perfusion fraction in liver, pancreas, renal cortex, and skeletal muscle (all P < 0.02). Optimal b-value thresholds separating diffusion from perfusion effects had a tendency to lower values when the IVIM-DKI model was used.

CONCLUSIONS

The proposed algorithm provides a new approach for separation of IVIM and kurtosis effects of diffusion data without organ-specific adaptation.