Diffusion kurtosis imaging of the liver at 3 Tesla: in vivo comparison to standard diffusion-weighted imaging

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver

PURPOSE

To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques.

METHODS

DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference.

RESULTS

In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( p = 0 . 0078 $$ p=0.0078 $$ , reproducibility coefficients (RPC) =  470 × 1 0 - 6 $$ 470\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( p = 0 . 29 $$ p=0.29 $$ , RPC = 220 × 1 0 - 6 $$ \mathrm{RPC}=220\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( p < 0 . 001 $$ p<0.001 $$ ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI.

CONCLUSION

Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

3D variable flip angle T1 mapping for differentiating benign and malignant liver lesions at 3T: comparison with diffusion weighted imaging

BACKGROUND

Different methods have been used to improve the imaging diagnosis of focal liver lesions (FLL). Among them, magnetic resonance imaging (MRI) has received more attention since it provides significant amount of information without radiation exposure. However, atypical imaging characteristics of FLL on MRI may complicate the differential diagnosis between benign and malignant FLL. This study aimed to compare the diagnostic value of T1 mapping and diffusion-weighted imaging (DWI) for differentiating of benign and malignant FLLs.

METHODS

This retrospective study enrolled 294 FLLs, including 150 benign and 144 malignant lesions. Whole liver T1 mapping sequences were obtained before and 2 min after the administration of Gd-DTPA to acquire native T1 and enhanced T1 and ΔT1%. Additionally, DWI sequence was conducted to generate apparent diffusion coefficient (ADC) maps. These quantitative parameters were compared using one-way analysis of variance, and the diagnostic accuracy of T1 mapping and ADC for FLLs was calculated by area under the curve (AUC).

RESULTS

Significant differences were observed regarding the native T1, enhanced T1, ΔT1%, and ADC between benign and malignant FLLs. Furthermore, the sensitivity and specificity of the parameters are as follows: native T1 0.797/0.702 (cut off value 1635.5 ms); enhanced T1, 0.911/0.976 (cutoff value 339.2 ms); ΔT1%, 0.901/0.905 (cutoff value 70.8%); and ADC, 0.975/0.952 (cutoff value 1.21 × 10^-3 mm²/s). The ideal cutoff values for native T1 and ADC in identifying cyst and haemangioma were 2422.9 ms (AUC 0.990, P < 0.01) and 2.077 × 10^-3 mm²/s (AUC 0.949, P < 0.01), respectively, with a sensitivity and specificity of 0.963/1 and 0.852/0.892, respectively. ADC was significantly positively correlated with T1 and ΔT1%, and significantly negatively correlated with enhanced T1.

CONCLUSION

The 3D Variable flip angle T1 mapping technique with Gd-DTPA has a high clinical potential for identifying benign and malignant FLLs. The enhanced T1 and ΔT1% values have similar diagnostic accuracy compared with DWI in evaluating FLLs. Native T1 shows better performance than DWI in distinguishing benign liver lesions, specifically, cysts, and haemangioma.

T2*-weighted imaging and diffusion kurtosis imaging (DKI) of rectal cancer: correlation with clinical histopathologic prognostic factors

PURPOSE

Histopathologic prognostic factors of rectal cancer are closely associated with local recurrence and distant metastasis. We aim to investigate the feasibility of T2*WI in assessment of clinical prognostic factors of rectal cancer, and compare with DKI.

METHODS

This retrospective study enrolled 50 out of 205 patients with rectal cancer according to the inclusion criteria. The following parameters were obtained: R2* from T2*WI, mean diffusivity (MD_k), mean kurtosis (MK), and mean diffusivity (MD_t) from DKI using tensor method. Above parameters were compared by Mann-Whitney U-test or students' t test. Spearman correlations between different parameters and histopathological prognostic factors were determined. The diagnostic performances of R2* and DKI-derived parameters were analyzed by receiver operating characteristic curves (ROC), separately and jointly.

RESULTS

There were positive correlations between R2* and multiple prognostic factors of rectal cancer such as T category, N category, tumor grade, CEA level, and LVI (P < 0.004). MD_k and MD_t showed negative correlations with almost all the histopathological prognostic factors except CRM and TIL involvement (P < 0.003). MK correlated positively with the prognostic factors except CA19-9 level and CRM involvement (P < 0.006). The AUC ranges were 0.724-0.950 for R2* and 0.755-0.913 for DKI-derived parameters for differentiation of prognostic factors. However, no significant differences of diagnostic performance were found between T2*WI, DKI, or the combined imaging methods in characterizing rectal cancer.

CONCLUSION

R2* and DKI-derived parameters were associated with different histopathological prognostic factors, and might act as noninvasive biomarkers for histopathological characterization of rectal cancer.

Comparison of the Effects of Hepatic Steatosis on Monoexponential DWI, Intravoxel Incoherent Motion Diffusion-weighted Imaging and Diffusion Kurtosis Imaging

RATIONALE AND OBJECTIVES

Diffusion-weighted imaging (DWI) techniques have drawn attention for their capability of staging hepatic fibrosis. However, the diagnostic performance of DWI for hepatic fibrosis might be affected by hepatic steatosis because hepatic steatosis and fibrosis may have a similar effect on diffusion/perfusion parameters. Therefore, the purpose of our study is to investigate the effect of hepatic steatosis on DWI parameters.

MATERIALS AND METHODS

51 patients with MR elastography liver stiffness values below 3.45kPa underwent DWI with multiple b-values and a multi-echo Dixon sequence for fat quantification. Correlation analysis was conducted between fat fraction and DWI parameters, and DWI parameters were compared between steatosis and non-steatosis groups.

RESULTS

Significant negative correlation was observed between fat fraction and apparent diffusion coefficient (ADC) (r = -0.62, p <0.001), pure molecular diffusion (D) (r = -0.62, p <0.001), corrected ADC (D_app) (r = -0.36, p = 0.01) and a positive correlation with mean kurtosis (K_app) (r = 0.53, p <0.001). The results of the comparison of DWI parameters were that ADC, D and D_app were statistically lower in the steatosis group (p < 0.001, p < 0.001 and p = 0.026, respectively) and K_app was significantly higher in the steatosis group (p <0.001) compared to the non-steatosis group. However, perfusion-related parameters (D* and f) did not show any statistical significance.

CONCLUSION

DWI parameters except for perfusion-related parameters (D* and f) are affected by changes in hepatic steatosis. Thus, hepatic steatosis may be considered as a possible confounding factor in DWI-based assessment of liver fibrosis.

The history of magnetic resonance imaging and its reflections in Acta Radiologica

The first reports in Acta Radiologica on magnetic resonance imaging (MRI) were published in 1984, four years after the first commercial MR scanners became available. For the first two years, all MR papers originated from the USA. Nordic contributions started in 1986, and until 2020, authors from 44 different countries have published MR papers in Acta Radiologica. Papers on MRI have constituted, on average, 30%-40% of all published original articles in Acta Radiologica, with a high of 49% in 2019. The MR papers published since 1984 document tremendous progress in several areas such as magnet and coil design, motion compensation techniques, faster image acquisitions, new image contrast, contrast-enhanced MRI, functional MRI, and image analysis. In this historical review, all of these aspects of MRI are discussed and related to Acta Radiologica papers.

The Role of Non-Gaussian Models of Diffusion Weighted MRI in Hepatocellular Carcinoma: A Systematic Review

The importance of Diffusion Weighted Imaging (DWI) in hepatocellular carcinoma (HCC) has been widely handled in the literature. Due to the mono-exponential model limitations, several studies recently investigated the role of non-Gaussian DWI models in HCC. However, their results are variable and inconsistent. Therefore, the aim of this systematic review is to summarize current knowledge on non-Gaussian DWI techniques in HCC. A systematic search of the literature, including PubMed, Google Scholar, MEDLINE, and ScienceDirect databases, was performed to identify original articles since 2010 that evaluated the role of non-Gaussian DWI models for HCC diagnosis, grading, response to treatment, and prognosis. Studies were grouped and summarized according to the non-Gaussian DWI models investigated. We focused on the most used non-Gaussian DWI models (Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Stretched Exponential—SE). The quality of included studies was evaluated by using QUADAS-2 and QUIPS tools. Forty-three articles were included, with IVIM and DKI being the most investigated models. Although the role of non-Gaussian DWI models in clinical settings has not fully been established, our findings showed that their parameters may potentially play a role in HCC. Further studies are required to identify a standardized DWI acquisition protocol for HCC diagnosis, grading, response to treatment, and prognosis.

Radiological Imaging of Gastro-Entero-Pancreatic Neuroendocrine Tumors. The Review of Current Literature Emphasizing the Diagnostic Value of Chosen Imaging Methods

Despite development of radiologic imaging, detection and follow-up of neuroendocrine neoplasms (NENs) still pose a diagnostic challenge, due to the heterogeneity of NEN, their relatively long-term growth, and small size of primary tumor. A set of information obtained by using different radiological imaging tools simplifies a choice of the most appropriate treatment method. Moreover, radiological imaging plays an important role in the assessment of metastatic lesions, especially in the liver, as well as, tumor response to treatment. This article reviews the current, broadly in use imaging modalities which are applied to the diagnosis of GEP-NETs, (the most common type of NENs) and put emphasis on the strengths and limitations of each modality.

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.

Comparison of models of diffusion in Wilms' tumours and normal contralateral renal tissue

Objective

ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms’ tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms’ tumours and the unaffected contralateral kidneys.

Materials and methods

DWI data were retrospectively reviewed (110 Wilms’ tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse–Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model.

Results

IVIM and stretched exponential provided the best fits to the Wilms’ tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms’ tumour and normal kidney data.

Discussion

The diffusion weighted signal in Wilms’ tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion.

Evaluation of diffusion kurtosis imaging in stratification of nonalcoholic fatty liver disease and early diagnosis of nonalcoholic steatohepatitis in a rabbit model

PURPOSE

To examine the feasibility of MR diffusion kurtosis imaging (DKI) for characterizing nonalcoholic fatty liver disease (NAFLD) and diagnosing nonalcoholic steatohepatitis (NASH).

METHODS

Thirty-two rabbits on high fat diet with different severities of NAFLD were imaged at 3 T MR including diffusion weighted imaging (DWI) and DKI using b values of 0, 400, 800 s/mm² with 15 diffusion directions at each b value. Apparent diffusion coefficient (ADC) was derived from the linear exponential DWI model. Mean diffusion (MD) and mean kurtosis (MK) were derived from the quadratic exponential model of DKI. Correlations between MR parameters and hepatic pathology determined by the NAFLD activity scoring system were analyzed by Spearman rank correlation analysis. Receiver operating characteristic analyses were applied to determine the cutoff values of MD, MK as well as ADC in distinguishing NASH from non-NASH. The diagnostic efficacies of MD and MK in detecting NASH were compared with that of ADC.

RESULTS

Values for ADC and MD significantly decreased as the severity of NAFLD increased (ρ = -0.529, -0.904, respectively; P < 0.05). MK values significantly increased as the severity of NAFLD increased (ρ = 0.761; P < 0.05). In addition, both MD and MK values were significantly different between borderline NASH and NASH groups (MD: 1.729 ± 0.144 vs. 1.458 ± 0.240[×10^-3 mm²/s]; MK: 1.096 ± 0.079 vs. 1.237 ± 0.180; P < 0.05). Moreover, there was a significantly higher area under the curve (AUC) for both MD (0.955) and MK (0.905), as compared to ADC (0.736).

CONCLUSION

Diffusion kurtosis imaging was feasible for stratifying NAFLD, and more accurately discriminated NASH from non-NASH when compared with DWI.

Diffusion Kurtosis MR Imaging versus Conventional Diffusion-Weighted Imaging for Distinguishing Hepatocellular Carcinoma from Benign Hepatic Nodules

Objectives

To assess the efficacy of diffusion kurtosis imaging (DKI) and compare DKI-derived parameters with conventional diffusion-weighted imaging (DWI) for distinguishing hepatocellular carcinoma (HCC) from benign hepatic nodules including focal nodular hyperplasia (FNH), hemangioma, and hepatocellular adenoma (HCA).

Materials and Methods

151 patients with 182 hepatic nodules (114 HCCs and 68 benign nodules including 33 FNHs, 29 hemangiomas, and 6 HCAs) were analyzed. Preoperative MRI examinations including DKI (b values: 0, 200, 500, 800, 1500, and 2000 sec/mm²) were performed, and kurtosis (K), diffusivity (D), and apparent diffusion coefficient (ADC) were calculated. The efficacy of DKI-derived parameters K, D, and ADC for distinguishing HCC from these benign nodules was analyzed.

Results

ROC (receiver operating characteristic curve) analysis showed the optimal cutoff values of ADC, D, and K for identification of these benign nodules, and HCCs were 1.295 (area under the curve (AUC): 0.826; sensitivity 80.6%; specificity 70.8%), 1.787 (AUC: 0.770; sensitivity 83.6%; specificity 59.6%), and 1.002 (AUC: 0.761; sensitivity 65.5%; specificity 79.0%), respectively. Statistically significant differences were found in ADC, D, and K values between groups of HCC-FNH and HCC-hemangioma (P < 0.05). There were significant differences in K and ADC values between groups of FNH-hemangioma and HCA-hemangioma (P < 0.05), respectively. Using logistic regression analysis, a regression equation was obtained: Logit(P)=−1.982X₁+1.385X₃+1.948(X₁: ADC; X₃: K), and odds ratios (OR) were 0.138 (95% confidence interval (CI): 0.052, 0.367), and 8.996 (95% CI: 0.970, 16.460), respectively.

Conclusion

Both ADC value and DKI-derived parameters K and D values have demonstrated a higher preoperative efficacy in distinguishing HCC from FNH, hemangioma, and HCA. No evidence was shown to suggest D or K value was superior to the ADC value.

Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade

BACKGROUND

The aim of this study was to prospectively evaluate the diagnostic efficacy of diffusion kurtosis imaging (DKI) in predicting microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) with comparison to the conventional diffusion-weighted imaging (DWI).

METHODS

This prospective study was approved by the Institutional Review Board, and written informed consent was obtained from all patients. From September 2015 to January 2017, 74 consecutive HCC patients were enrolled in this study. Preoperative magnetic resonance imaging including DKI protocol was performed, and patients were followed up for at least one year after surgery. Diffusion parameters including the mean corrected apparent diffusion coefficient (MD), mean apparent kurtosis coefficient (MK), and apparent diffusion coefficient (ADC) were calculated. Differences of diffusion parameters among different histopathological groups were compared. For parameters that were significantly different between pathological groups, receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic efficiency for identifying MVI and predicting high-grade HCC. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of clinical and laboratory variables and diffusion parameters as risk factors for early recurrence (≤1 year).

RESULTS

Among all the studied diffusion parameters, only MK differed significantly between the MVI-positive and MVI-negative group (0.91±0.10 vs. 0.82±0.09, P<0.001), and showed moderate diagnostic efficacy (AUC =0.77) for identifying MVI. High-grade HCCs showed significantly higher MK values (0.93±0.10 vs. 0.82±0.09, P<0.001), along with MD (1.34±0.18 vs. 1.54±0.22, P<0.001) and ADC values (1.17±0.15 vs. 1.30±0.16, P=0.001) than low-grade HCCs. For differentiating high-grade from low-grade HCCs, MK demonstrated a higher area under the ROC curve (AUC) and significantly higher specificity than MD and ADC (AUC =0.81 vs. 0.76 and 0.74; specificity =82.2% vs. 60.0% and 60.0%, P=0.02). In addition, higher MK (OR =5.700, P=0.002) and Barcelona Clinic Liver Cancer (BCLC) stage C (OR =6.329, P=0.005) were independent risk factors for early HCC recurrence.

CONCLUSIONS

DKI-derived MK values outperformed conventional ADC values for predicting MVI and histologic grade of HCC, and are associated with increased risk of early tumor recurrence.

MR diffusion kurtosis imaging for cancer diagnosis: A meta-analysis of the diagnostic accuracy of quantitative kurtosis value and diffusion coefficient

PURPOSE

To perform a meta-analysis for assessing the accuracy of diffusion kurtosis imaging (DKI)-derived quantitative parameters (kurtosis values, K; and corrected diffusion coefficients non-Gaussian bias, D) in separating malignant cancers from benign lesions.

METHODS

Relevant studies were searched in PubMed and Cochrane Library databases and were analyzed by Meta-DiSc software.

RESULTS

Fourteen eligible studies involving 1847 lesions in 1107 patients (895 were benign and 952 were malignant) were included. Pooled analysis showed the sensitivity, specificity, positive likelihood ratio (LR), and negative LR were respectively 0.83 (95% CI, 0.79-0.85), 0.83 (95% CI, 0.80-0.86), 4.61 (95% CI, 2.98-7.14), and 0.22 (95% CI, 0.18-0.28) for K, with the overall area under curve (AUC) of 0.89. The sensitivity, specificity, positive LR, and negative LR were 0.85 (95% CI, 0.80-0.88), 0.85 (95% CI, 0.79-0.89), 6.39 (95% CI, 3.14-12.99), and 0.18 (95% CI, 0.14-0.23) for D, with the overall AUC of 0.92. The sensitivity, specificity, positive LR, and negative LR for apparent diffusion coefficient (ADC) derived from standard diffusion-weighted imaging (DWI) were 0.82 (95% CI, 0.79-0.84), 0.85 (95% CI, 0.82-0.88), 4.75 (95% CI, 3.38-6.68), and 0.24 (95% CI, 0.19-0.29), with the overall AUC of 0.89. The superiority of D to K and ADC was also confirmed by the subgroup analysis of prostate cancer.

CONCLUSION

Our findings suggest that DKI should be added to the routine imaging protocol for screening cancer, with the highest diagnostic accuracy of diffusion coefficients.