Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters

Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review

Intravoxel incoherent motion (IVIM) modeling is a widely used double-exponential model for describing diffusion-weighted imaging (DWI) signal, with a slow component related to pure molecular diffusion and a fast component associated with microcirculatory perfusion, which compensates for the limitations of traditional DWI. IVIM is a noninvasive technique for obtaining liver pathological information and characterizing liver lesions, and has potential applications in the initial diagnosis and treatment monitoring of liver diseases. Recent studies have demonstrated that IVIM-derived parameters are useful for evaluating liver lesions, including nonalcoholic fatty liver disease (NAFLD), liver fibrosis and liver tumors. However, the results are not stable. Therefore, it is necessary to summarize the current applications of IVIM in liver disease research, identify existing shortcomings, and point out the future development direction. In this review, we searched for studies related to hepatic IVIM-DWI applications over the past two decades in the PubMed database. We first introduce the fundamental principles and influential factors of IVIM, and then discuss its application in NAFLD, liver fibrosis, and focal hepatic lesions. It has been found that IVIM is still unstable in ensuring the robustness and reproducibility of measurements in the assessment of liver fibrosis grade and liver tumors differentiation, due to inconsistent and substantial overlap in the range of IVIM-derived parameters for different fibrotic stages. In the end, the future direction of IVIM-DWI in the assessment of liver diseases is discussed, emphasizing the need for further research on the stability of IVIM-derived parameters, particularly perfusion-related parameters, in order to promote the clinical practice of IVIM-DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Retrospective Motion Artifact Reduction by Spatial Scaling of Liver Diffusion-Weighted Images

Cardiac motion causes unpredictable signal loss in respiratory-triggered diffusion-weighted magnetic resonance imaging (DWI) of the liver, especially inside the left lobe. The left liver lobe may thus be frequently neglected in the clinical evaluation of liver DWI. In this work, a data-driven algorithm that relies on the statistics of the signal in the left liver lobe to mitigate the motion-induced signal loss is presented. The proposed data-driven algorithm utilizes the exclusion of severely corrupted images with subsequent spatially dependent image scaling based on a signal-loss model to correctly combine the multi-average diffusion-weighted images. The signal in the left liver lobe is restored and the liver signal is more homogeneous after applying the proposed algorithm. Furthermore, overestimation of the apparent diffusion coefficient (ADC) in the left liver lobe is reduced. The proposed algorithm can therefore contribute to reduce the motion-induced bias in DWI of the liver and help to increase the diagnostic value of DWI in the left liver lobe.

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

PURPOSE

This study aims to investigate three different image processing methods on quantitative parameters of IVIM sequence, as well as apparent diffusion coefficients and simple perfusion fractions, for benign and malignant liver tumors.

MATERIALS AND METHODS

IVIM images with 8 b-values (0-1000 s/mm2) and 1.5 T MRI scanner in 16 patients and 3 healthy people were obtained. Next, the regions of interest were selected for malignant, benign, and healthy liver regions (50, 56, and 12, respectively). Then, the bi-exponential equation of the IVIM technique was fitted with two segmented fitting methods as well as one full fitting method (three methods in total). Using the segmented fitting method, diffusion coefficient (D) is fixed with a mono-exponential equation with b-values that are greater than 200 s/mm2. The perfusion fraction (f) can then be calculated by extrapolating, as the first method, or fitting simultaneously with the pseudo-diffusion coefficient (D*) as the second method. In the full fitting method, as the third method, all IVIM parameters were obtained simultaneously. The mean values of parameters from different methods were compared in different grades of lesions.

RESULTS

Our results indicate that the image processing method can change statistical comparisons between different groups for each parameter. The D value is the only quantity in this technique that does not depend on the fitting process and can be used as an indicator of comparison between studies (P < 0.05). The most effective method to distinguish liver lesions is the extrapolated f method (first method). This method created a significant difference (P < 0.05) between the perfusion parameters between benign and malignant lesions.

CONCLUSION

Using extrapolated f is the most effective method of distinguishing liver lesions using IVIM parameters. The comparison between groups does not depend on the fitting method only for parameter D.

Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms

PURPOSE

This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs).

METHODS

This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m²). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs.

RESULTS

The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients.

CONCLUSION

The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.

Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management

Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.

Evaluation of the efficacy of transcatheter arterial embolization combined with apatinib on rabbit VX2 liver tumors by intravoxel incoherent motion diffusion-weighted MR imaging

Background and purpose

It is crucial to evaluate the efficacy, recurrence, and metastasis of liver tumors after clinical treatment. This study aimed to investigate the value of Introvoxel Incoherent Motion (IVIM) imaging in the evaluation of rabbit VX2 liver tumors treated with Transcatheter Arterial Embolization (TAE) combined with apatinib.

Methods

Twenty rabbit VX2 liver tumor models were established and randomly divided into either the experimental group (n=15) or the control group (n=5). The experimental group was treated with TAE combined with oral apatinib after successful tumor inoculation, while no treatment was administered following inoculation in the control group. IVIM sequence scan was performed in the experimental group before treatment, at 7 and 14 days after treatment. All rabbits were sacrificed after the last scan of the experimental group. Marginal tissues from the tumors of both groups were excised for immunohistochemical analysis to observe and compare the expression of microvessel density (MVD). The alterations of IVIM-related parameters of tumor tissues in the experimental group, including Apparent Diffusion Coefficient (ADC), True Diffusion Coefficient (D), Pseudodiffusion Coefficient (D*), and Perfusion Fraction (f) were compared at different periods, and the correlation between these parameters and MVD was analyzed.

Results

After treatment, ADC and D values significantly increased, whereas D* and f values both decreased, with statistically significant differences.(P<0.05). The average tumor MVD of the experimental group after TAE combined with apatinib ((33.750 ± 6.743) bars/high power field (HPF)) was significantly lower than that in the control group ((64.200 ± 10.164) bars/HPF)). Moreover, D and f were positively correlated with tumor MVD in the experimental group (r=0.741 for D and r=0.668 for f, P<0.05). However, there was no significant correlation between ADC and D* values of the experimental group and tumor MVD (r=0.252 for ADC and r=0.198 for D*, P>0.05).

Conclusion

IVIM imaging can be employed to evaluate the efficacy of TAE combined with apatinib in rabbit VX2 liver tumors. Alterations in D and f values were closely related to the MVD of liver tumor tissues.

Establishment of a multi-parameters MRI model for predicting small lymph nodes metastases (<10 mm) in patients with resected pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the potential role of MR findings and DWI parameters in predicting small regional lymph nodes metastases (with short-axis diameter < 10 mm) in pancreatic ductal adenocarcinomas (PDACs).

METHODS

A total of 127 patients, 82 in training group and 45 in testing group, with histopathologically diagnosed PDACs who underwent pancreatectomy were retrospectively analyzed. PDACs were divided into two groups of positive and negative lymph node metastases (LNM) based on the pathological results. Pancreatic cancer characteristics, short axis of largest lymph node, and DWI parameters of PDACs were evaluated.

RESULTS

Univariate and multivariate analyses showed that extrapancreatic distance of tumor invasion, short-axis diameter of the largest lymph node, and mean diffusivity of tumor were independently associated with small LNM in patients with PDACs. The combining MRI diagnostic model yielded AUCs of 0.836 and 0.873, and accuracies of 81.7% and 80% in the training and testing groups. The AUC of the MRI model for predicting LNM was higher than that of subjective MRI diagnosis in the training group (rater 1, P = 0.01; rater 2, 0.008) and in a testing group (rater 1, P = 0.036; rater 2, 0.024). Comparing the subjective diagnosis, the error rate of the MRI model was decreased. The defined LNM-positive group by the MRI model showed significantly inferior overall survival compared to the negative group (P = 0.006).

CONCLUSIONS

The MRI model showed excellent performance for individualized and noninvasive prediction of small regional LNM in PDACs. It may be used to identify PDACs with small LNM and contribute to determining an appropriate treatment strategy for PDACs.

Application of intravoxel incoherent motion diffusion-weighted imaging in hepatocellular carcinoma

The morbidity and mortality of hepatocellular carcinoma (HCC) rank 6^th and 4^th, respectively, among malignant tumors worldwide. Traditional diffusion-weighted imaging (DWI) uses the apparent diffusion coefficient (ADC) obtained by applying the monoexponential model to reflect water molecule diffusion in active tissue; however, the value of ADC is affected by microcirculation perfusion. Using a biexponential model, intravoxel incoherent motion (IVIM)-DWI quantitatively measures information related to pure water molecule diffusion and microcirculation perfusion, thus compensating for the shortcomings of DWI. The number of studies examining the application of IVIM-DWI in patients with HCC has gradually increased over the last few years, and many results show that IVIM-DWI has vital value for HCC differentiation, pathological grading, and predicting and evaluating the treatment response. The present study principally reviews the principle of IVIM-DWI and its research progress in HCC differentiation, pathological grading, predicting and evaluating the treatment response, predicting postoperative recurrence and predicting gene expression prediction.

Value of Intravoxel Incoherent Motion (IVIM) Imaging for Differentiation between Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma

Efficient noninvasive imaging techniques in the differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are very important because of their different management and prognosis. Our purpose was to evaluate the difference of parameters extracted from intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) between the two groups and their performance for the differentiation, as well as the significance of perfusion information. IVIM studies (9 b-values) in 41 patients with either ICC or HCC were reviewed retrospectively by two observers. Diffusion coefficient (D), pseudodiffusion coefficient (D∗), perfusion fraction (f), ADC, and the mean percentage of parenchymal enhancement (MPPE) at 30 s after contrast-enhancement were calculated and compared between ICC and HCC. The relationship between D∗, f values, and MPPE was evaluated by Spearman's correlation test. The diagnostic efficacy of all parameters was analyzed by the receiver operating characteristic (ROC) curve. Interobserver and intraobserver agreements were analyzed. The parameters (D and ADC) of ICC were distinctly higher than those of HCC; whereas the parameters (f and MPPE of arterial phase) were distinctly lower (all false discovery rate [FDR]-corrected P < 0.05). The metric D∗ value of ICC was slightly higher than that of HCC (71.44 vs 69.41) with FDR-corrected P > 0.05. Moreover, the value of parameter D was significantly lower than that of ADC (FDR-corrected P < 0.05). The parameters (D and f values) extracted from IVIM showed excellent diagnostic efficiency in the identification, and the diagnostic efficiency of D value was significantly higher than that of the ADC. There were positive correlations between perfusion-related parameters (D∗, f values) and MPPE. Interobserver and intraobserver agreements were excellent or perfect in measurements of all parameters. Parameters derived from IVIM were valuable for distinguishing ICC and HCC. Moreover, the D value showed better diagnostic efficiency for the differential diagnosis than monoexponential fitting-derived ADC value. Meanwhile, the significant correlation between perfusion-related parameters and MPPE demonstrates that specific IVIM metrics may serve as a noninvasive indicator for the vascular perfusion information of ICC and HCC.

Perfusion-Diffusion Ratio: A New IVIM Approach in Differentiating Solid Benign and Malignant Primary Lesions of the Liver

Introduction

In order to improve the efficacy of intravoxel incoherent motion (IVIM) parameters in characterising specific tissues, a new concept is introduced: the perfusion–diffusion ratio (PDR), which expresses the relationship between the signal S(b) decline rate as a result of IVIM and the rate of signal S(b) decline due to diffusion. The aim of this study was to investigate this novel approach in the differentiation of solid primary liver lesions. Material and Methods. Eighty-three patients referred for liver MRI between August 2017 and January 2020 with a suspected liver tumour were prospectively examined with the standard liver MRI protocol extended by DWI-IVIM sequence. Patients with no liver lesions, haemangiomas, or metastases were excluded. The final study population consisted of 34 patients with primary solid liver masses, 9 with FNH, 4 with regenerative nodules, 10 with HCC, and 11 with CCC. The PDR coefficient was introduced, defined as the ratio of the rate of signal S(b) decrease due to the IVIM effect to the rate of signal S(b) decrease due to the diffusion process, for b = 0.

Results

No significant differences were found between benign and malignant lesions in the case of IVIM parameters (f, D, or D^∗) and ADC. Significant differences were observed only for PDR, with lower values for malignant lesions (p = 0.03). The ROC analysis yielded an AUC value for PDR equal to 0.74, with a cut-off value of 5.06, sensitivity of 81%, specificity of 77%, and accuracy of 79%.

Conclusion

PDR proved to be more effective than IVIM parameters and ADC in the differentiation of solid benign and malignant primary liver lesions.

Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment

Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.

Non-gaussian models of 3-Tesla diffusion-weighted MRI for the differentiation of pancreatic ductal adenocarcinomas from neuroendocrine tumors and solid pseudopapillary neoplasms

PURPOSE

To assess the MRI performance in differentiating pancreatic ductal adenocarcinomas (PDACs), from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine tumors (PNETs) using non-gaussian diffusion-weighted imaging models.

METHODS

This was a retrospective study of patients diagnosed with PDACs (01/2015-06/2019) or with PNETs or SPNs diagnosed (01/2011-12/2019) at our hospital. The lesions were randomized 1:1 to the primary and validation cohorts. The regions of interest (ROIs) were manually drawn on each slice at DWI (b = 1500 s/mm²) from 3 T MRI. D (diffusion coefficient), D* (pseudodiffusion coefficient), f (perfusion fraction), distributed diffusion coefficient (DDC), α (diffusion heterogeneity index), mean diffusivity (MD) and mean kurtosis (MK) were obtained. The parameters with largest performance for differentiation were used to establish a diagnostic model.

RESULTS

There were 148, 56, and 60 patients with PDAC, PNET, and SPN, respectively. For differentiating PDACs from SPNs, f and MK values were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation groups, respectively. For distinguishing PDACs from PNETs, α and MK values were used to establish a diagnostic model with AUCs of 0.87 and 0.86 in the primary and validation groups, respectively. The accuracy rate of the subjective evaluation with the assistance of non-gaussian DWI models for differentiating PDAC from SPNs and PNETs were higher than that of subjective diagnosis alone (P < 0.05).

CONCLUSIONS

The non-gaussian DWI models could assist radiologists in accurately differentiating PDACs from PNETs and SPNs.

Diagnosis of hilar cholangiocarcinoma using intravoxel incoherent motion diffusion-weighted magnetic resonance imaging

OBJECTIVE

To investigate the value of intravoxel incoherent motion diffusion-weighed magnetic resonance imaging (IVIM-DWI) in discriminating the pathological grades of hilar cholangiocarcinoma (HC).

PATIENTS AND METHODS

Thirty-seven HC patients were enrolled and received routine and advanced DWI scanning with multiple b-values. IVIM-DWI images were obtained using echo-planar imaging sequence.

RESULTS

The consistency of the maximum cross-sectional area ROI measuring method was higher than that of the repeated sampling ROI measuring method. ADC_slow values were closely correlated with the pathological grades of HC. The degrees of biliary dilatation and MELD scores had no influence on the negative correlation between ADC_slow values and the pathological degrees of HC patients.

CONCLUSIONS

ADC_slow values could be applied in indicating the pathological grades of HC, which was independent on the extent of biliary dilatation.

A study of the correlations between IVIM-DWI parameters and the histologic differentiation of hepatocellular carcinoma

The present study aimed to investigate the value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in the preoperative prediction of the histologic differentiation of hepatocellular carcinoma (HCC). Seventy HCC patients were scanned with a 3.0 T magnetic resonance scanner. The values of apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and the fraction of the fast apparent diffusion coefficient (f) were measured. Analysis of variance was used to compare the differences in parameters between groups with different degrees of histologic differentiation. p < 0.05 was considered statistically significant. Receiver operating characteristic (ROC) curves were used to analyse the efficacy of IVIM-DWI parameters for predicting the histologic differentiation of HCC. The ADC and D values for well, moderately and poorly differentiated HCC were 1.35 ± 0.17 × 10^-3 mm²/s, 1.16 ± 0.17 × 10^-3 mm²/s, 0.98 ± 0.21 × 10^-3 mm²/s, and 1.06 ± 0.15 × 10^-3 mm²/s, 0.88 ± 0.16 × 10^-3 mm²/s, 0.76 ± 0.18 × 10^-3 mm²/s, respectively, and all differences were significant. The D* and f values of the three groups were 32.87 ± 14.70 × 10^-3 mm²/s, 41.68 ± 17.90 × 10^-3 mm²/s, 34.54 ± 18.60 × 10^-3 mm²/s and 0.22 ± 0.07, 0.23 ± 0.08, 0.18 ± 0.07, respectively, with no significant difference. When the cut-off values of ADC and D were 1.25 × 10^-3 mm²/s and 0.97 × 10^-3 mm²/s, respectively, their diagnostic sensitivities and specificities for distinguishing well differentiated HCC from moderately differentiated and poorly differentiated HCC were 73.3%, 85.5%, 86.7%, and 78.2%, and their areas under the ROC curve were 0.821 and 0.841, respectively. ADC and D values can be used preoperatively to predict the degree of histologic differentiation in HCC, and the D value has better diagnostic value.

A study of the correlations between IVIM-DWI parameters and the histologic differentiation of hepatocellular carcinoma

The present study aimed to investigate the value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in the preoperative prediction of the histologic differentiation of hepatocellular carcinoma (HCC). Seventy HCC patients were scanned with a 3.0 T magnetic resonance scanner. The values of apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (D), fast apparent diffusion coefficient (D*), and the fraction of the fast apparent diffusion coefficient (f) were measured. Analysis of variance was used to compare the differences in parameters between groups with different degrees of histologic differentiation. p < 0.05 was considered statistically significant. Receiver operating characteristic (ROC) curves were used to analyse the efficacy of IVIM-DWI parameters for predicting the histologic differentiation of HCC. The ADC and D values for well, moderately and poorly differentiated HCC were 1.35 ± 0.17 × 10⁻³ mm²/s, 1.16 ± 0.17 × 10⁻³ mm²/s, 0.98 ± 0.21 × 10⁻³ mm²/s, and 1.06 ± 0.15 × 10⁻³ mm²/s, 0.88 ± 0.16 × 10⁻³ mm²/s, 0.76 ± 0.18 × 10⁻³ mm²/s, respectively, and all differences were significant. The D* and f values of the three groups were 32.87 ± 14.70 × 10⁻³ mm²/s, 41.68 ± 17.90 × 10⁻³ mm²/s, 34.54 ± 18.60 × 10⁻³ mm²/s and 0.22 ± 0.07, 0.23 ± 0.08, 0.18 ± 0.07, respectively, with no significant difference. When the cut-off values of ADC and D were 1.25 × 10⁻³ mm²/s and 0.97 × 10⁻³ mm²/s, respectively, their diagnostic sensitivities and specificities for distinguishing well differentiated HCC from moderately differentiated and poorly differentiated HCC were 73.3%, 85.5%, 86.7%, and 78.2%, and their areas under the ROC curve were 0.821 and 0.841, respectively. ADC and D values can be used preoperatively to predict the degree of histologic differentiation in HCC, and the D value has better diagnostic value.

The role of quantitative diffusion-weighted imaging in characterization of hypovascular liver lesions: A prospective comparison of intravoxel incoherent motion derived parameters and apparent diffusion coefficient

BACKGROUND

The utility of intravoxel incoherent motion (IVIM) related parameters in differentiation of hypovascular liver lesions is still unknown.

PURPOSE

The purpose of this study was to evaluate the value of IVIM related parameters in comparison to apparent diffusion coefficient (ADC) for differentiation among intrahepatic mass-forming cholangiocarcinoma (IMC), and hypovascular liver metastases (HLM).

METHODS

Seventy-four prospectively enrolled patients (21 IMC, and 53 HLM) underwent 1.5T magnetic resonance examination with IVIM diffusion-weighted imaging using seven b values (0-800 s/mm2). Two independent readers performed quantitative analysis of IVIM-related parameters and ADC. Interobserver reliability was tested using a intraclass correlation coefficient. ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (ƒ) were compared among the lesions using Kruskal-Wallis H test. The diagnostic accuracy of each parameter was assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

The interobserver agreement was good for ADC (0.802), and excellent for D, D*, and ƒ (0.911, 0.927, and 0.942, respectively). ADC, and D values were significantly different among IMC and HLM (both p < 0.05), while there was no significant difference among these lesions for ƒ and D* (p = 0.101, and p = 0.612, respectively). ROC analysis showed higher diagnostic performance of D in comparison to ADC (AUC = 0.879 vs 0.821).

CONCLUSION

IVIM-derived parameters in particular D, in addition to ADC, could help in differentiation between most common hypovascular malignant liver lesions, intrahepatic mass-forming cholangiocarcinoma and hypovascular liver metastases.

Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis

Background and objectives

The diagnostic performance of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the differential diagnosis of pulmonary tumors remained debatable among published studies. This study aimed to pool and summary the relevant results to provide more robust evidence in this issue using a meta-analysis method.

Materials and methods

The researches regarding the differential diagnosis of lung lesions using IVIM-DWI were systemically searched in Pubmed, Embase, Web of science and Wangfang database without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan’s nomogram was used to predict the post-test probabilities.

Results

Eleven studies with 481 malignant and 258 benign lung lesions were included. Most include studies showed a low to unclear risk of bias and low concerns regarding applicability. Lung cancer demonstrated a significant lower ADC (SMD = -1.17, P < 0.001), D (SMD = -1.02, P < 0.001) and f values (SMD = -0.43, P = 0.005) than benign lesions, except D* value (SMD = 0.01, P = 0.96). D value demonstrated the best diagnostic performance (sensitivity = 89%, specificity = 71%, AUC = 0.90) and highest post-test probability (57, 57, 43 and 43% for D, ADC, f and D* values) in the differential diagnosis of lung tumors, followed by ADC (sensitivity = 85%, specificity = 72%, AUC = 0.86), f (sensitivity = 71%, specificity = 61%, AUC = 0.71) and D* values (sensitivity = 70%, specificity = 60%, AUC = 0.66).

Conclusion

IVIM-DWI parameters show potentially strong diagnostic capabilities in the differential diagnosis of lung tumors based on the tumor cellularity and perfusion characteristics, and D value demonstrated better diagnostic performance compared to mono-exponential ADC.

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

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

Magnetic Resonance Diffusion Kurtosis Imaging versus Diffusion-Weighted Imaging in Evaluating the Pathological Grade of Hepatocellular Carcinoma

Purpose

To investigate the diagnostic efficacy of diffusion kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) for pathological grading.

Methods

From December 2015 to January 2017, consecutive patients suspected of having hepatocellular carcinoma (HCC) without prior treatment were prospectively enrolled in this study. MRI examinations were performed before surgical treatment. HCC patients confirmed by surgical pathology were included in the study. The mean diffusivity (MD) values, mean kurtosis (MK) values, and apparent diffusion coefficient (ADC) were calculated. The differences and correlations of these parameters among different pathological grades were analyzed. The diagnostic efficiency of DKI and DWI for predicting high-grade HCC was evaluated by receiver operating characteristic (ROC) curves. Logistic regression analyses were used to evaluate the predictive factors for pathological grade.

Results

A total of 128 patients (79 males and 49 females, age: 56.9±10.9 years, range, 32–80) with primary HCC were included: grade I: 22 (17.2%) patients, grade II: 37 (28.9%) patients, grade III: 43 (33.6%) patients, grade IV: 26 (20.3%) patients. The MK values of stage I, II, III, and IV were 0.86±0.13, 1.06±0.11, 1.27±0.17, and 1.57±0.13, respectively. The MK values were significantly higher in the high-grade group than in the low-grade group and were positively correlated with pathological grade (rho =0.7417, P<0.001). The MK value demonstrated a larger area under the curve (AUC), with a value of 0.93 than the MD value, which had an AUC of 0.815 (P<0.001), and ADC, which had an AUC of 0.662 (P=0.01). The MK value (>1.19), ADC (≤1.29×10^–3 mm²/s), and HBV (+) were independent predictors for the pathological grade of HCCs.

Conclusion

The MK values derived from DKI and the ADC values obtained from traditional DWI were more valuable than the MD values in predicting the histological grade of HCCs and could potentially guide clinical treatment before surgery.

Intravoxel incoherent motion diffusion-weighted imaging to differentiate hepatocellular carcinoma from intrahepatic cholangiocarcinoma

The present study aimed to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). This study included 65 patients with malignant hepatic nodules (55 with HCC, 10 with ICC), and 17 control patients with normal livers. All patients underwent IVIM-DWI scans on a 3.0 T magnetic resonance imaging (MRI) scanner. The standard apparent diffusion coefficient (ADC), pure diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), and perfusion fraction (f) were obtained. Differences in the parameters among the groups were analysed using one-way ANOVA, with p < 0.05 indicating statistical significance. Receiver operating characteristic (ROC) curve analysis was used to compare the efficacy of each parameter in differentiating HCC from ICC. ADC, Dslow, Dfast, f significantly differed among the three groups. ADC and Dslow were significantly lower in the HCC group than in the ICC group, while Dfast was significantly higher in the HCC group than in the ICC group; f did not significantly differ between the HCC and ICC groups. When the cut-off values of ADC, Dslow, and Dfast were 1.27 × 10−3 mm2/s, 0.81 × 10−3 mm2/s, and 26.04 × 10−3 mm2/s, respectively, their diagnostic sensitivities for differentiating HCC from ICC were 98.18%, 58.18%, and 94.55%, their diagnostic specificities were 50.00%, 80.00%, and 80.00%, and their areas under the ROC curve (AUCs) were 0.687, 0.721, and 0.896, respectively. Dfast displayed the largest AUC value. IVIM-DWI can be used to differentiate HCC from ICC.

Intravoxel incoherent motion diffusion-weighted imaging to differentiate hepatocellular carcinoma from intrahepatic cholangiocarcinoma

The present study aimed to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). This study included 65 patients with malignant hepatic nodules (55 with HCC, 10 with ICC), and 17 control patients with normal livers. All patients underwent IVIM-DWI scans on a 3.0 T magnetic resonance imaging (MRI) scanner. The standard apparent diffusion coefficient (ADC), pure diffusion coefficient (D_slow), pseudo-diffusion coefficient (D_fast), and perfusion fraction (f) were obtained. Differences in the parameters among the groups were analysed using one-way ANOVA, with p < 0.05 indicating statistical significance. Receiver operating characteristic (ROC) curve analysis was used to compare the efficacy of each parameter in differentiating HCC from ICC. ADC, D_slow, D_fast, f significantly differed among the three groups. ADC and D_slow were significantly lower in the HCC group than in the ICC group, while D_fast was significantly higher in the HCC group than in the ICC group; f did not significantly differ between the HCC and ICC groups. When the cut-off values of ADC, D_slow, and D_fast were 1.27 × 10⁻³ mm²/s, 0.81 × 10⁻³ mm²/s, and 26.04 × 10⁻³ mm²/s, respectively, their diagnostic sensitivities for differentiating HCC from ICC were 98.18%, 58.18%, and 94.55%, their diagnostic specificities were 50.00%, 80.00%, and 80.00%, and their areas under the ROC curve (AUCs) were 0.687, 0.721, and 0.896, respectively. D_fast displayed the largest AUC value. IVIM-DWI can be used to differentiate HCC from ICC.

A comparative study of monoexponential versus biexponential models of diffusion-weighted imaging in differentiating histologic grades of hepatitis B virus-related hepatocellular carcinoma

PURPOSE

To compare the diagnostic value of apparent diffusion coefficient (ADC) and intravoxel incoherent motion metrics in discriminating histologic grades of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) infection.

METHODS

117 chronic HBV patients with 120 pathologically confirmed HCCs after surgical resection or liver transplantation were enrolled in this retrospective study. Diffusion-weighted imaging was performed using eleven b values (0-1500 s/mm²) and two b values (0, 800 s/mm²) successively on a 3.0 T system. ADC_{0, 800}, ADC_total, diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were calculated. The parameters of three histologically differentiated subtypes were investigated using Kruskal-Wallis test, Spearman rank correlation, and receiver-operating characteristic analysis. Interobserver agreement was assessed using the intraclass correlation coefficient.

RESULTS

There was excellent agreement for ADC_total/D/f, good agreement for ADC_0,800, and moderate agreement for D*. ADC_total, ADC_{0, 800,}D, and f were significantly different for well, moderately, and poorly differentiated HCCs (P < 0.001), and they were all inversely correlated with histologic grades: r = - 0.633, - 0.394, - 0.435, and - 0.358, respectively (P < 0.001). ADC_total demonstrated higher performance than ADC_0,800 in diagnosing both well and poorly differentiated HCCs (P < 0.001 and P = 0.04, respectively). ADC_total showed higher performance than D and f in diagnosing well differentiated HCCs (P < 0.001) and similar performance in diagnosing poorly differentiated HCCs (P = 0.06 and 0.13, respectively).

CONCLUSIONS

ADC_total showed better diagnostic performance than ADC_0,800, D, and f to discriminate histologic grades of HCC.

Intravoxel incoherent motion imaging for diagnosing and staging the liver fibrosis and inflammation

PURPOSE

To evaluate the diagnostic accuracy of intravoxel incoherent motion (IVIM) model parameters for the diagnosis and staging of liver fibrosis and inflammation in patients with chronic hepatitis B.

METHODS

Fifty-four patients with chronic hepatitis B and 42 healthy volunteers were included in the study. All subjects were examined by 3 T magnetic resonance imaging. Diffusion-weighted imaging was undertaken with sixteen b values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histological evaluation of biopsy samples was considered the reference standard for the staging of liver fibrosis and inflammation. Differences in IVIM parameters between patient and control groups were analyzed. In the patient group, fibrosis stage and inflammation grade groups were analyzed with respect to IVIM parameters. The correlation was assessed between IVIM parameters and Ishak-modified scale of fibrosis stages and inflammation grades.

RESULTS

The D was significantly lower in the patient group than the control group, p = 0.038 with Cohen's d effect size of 0.452. D was significantly different between fibrosis stage levels. D values decreased in fibrosis stages from the minimal to moderate to marked fibrosis. Fibrosis grades significantly negatively correlated with D and D* values, p = 0.001, and 0.021, respectively. In addition, inflammation grades negatively correlated with f values, p = 0.047.

CONCLUSION

D values measured with IVIM imaging may help to diagnose liver fibrosis. IVIM imaging could be an alternative to liver biopsy for the staging of liver fibrosis.

Topics on quantitative liver magnetic resonance imaging

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

Role of Intravoxel Incoherent Motion in Discriminating Hepatitis B Virus-Related Intrahepatic Mass-Forming Cholangiocarcinoma from Hepatocellular Carcinoma Based on Liver Imaging Reporting and Data System v2018

Backgroud: Intravoxel incoherent motion (IVIM) could be used to characterize benign and malignant hepatic lesions and predict the histological grade of hepatocellular carcinoma (HCC). To evaluate IVIM-derived parameters for differentiating between hepatitis B virus (HBV)-related intrahepatic mass-forming cholangiocarcinoma (IMCC) and HCC based on the Liver Imaging Reporting and Data System (LI-RADS) v2018. Materials and Methods: 20 IMCC patients and one-to-one matched control HCC patients were retrospectively assessed. IVIM scanning with 11 b-values (from 0 to 1500 s/mm²) was obtained using a 3.0-T magnetic resonance scanner. Apparent diffusion coefficient (ADC) and IVIM parameters, including diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f), were compared between IMCC and HCC. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performances of ADC, D, f, and D*. The LI-RADS features and a final category were also compared using LI-RADS v2018. Results: ADC and D were significantly higher in IMCC than in HCC (p = 0.012 and p = 0.007, respectively); f was significantly higher in HCC than in IMCC (p = 0.004). The area under the ROC curve values for ADC, D, and f for differentiating HBV-related IMCC from HCC were 0.724, 0.753, and 0.741, respectively. Conclusion: The majority of HBV-related IMCCs can be categorized as LR-M by using LI-RADS. However, atypical IMCCs may be classified as non-LR-M. ADC, D, and f values may be helpful in differentiating HBV-related IMCC from HCC, and similar diagnostic performances were obtained for these values.

Can IVIM help predict HCC recurrence after hepatectomy?

PURPOSE

To determine the diagnostic performance of intravoxel incoherent motion (IVIM) parameters to predict tumor recurrence after hepatectomy in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

One hundred and fifty-seven patients (mean age 52.54 ± 11.32 years, 87% male) with surgically and pathologically confirmed HCC were included. Regions of interests were drawn including the tumors by two independent radiologists. ADC and IVIM-derived parameters (true diffusion coefficient [D]; pseudodiffusion coefficient [D*]; pseudodiffusion fraction [f]) were obtained preoperatively. The Cox proportional hazards model was used to analyze the predictors associated with tumor recurrence after hepatectomy.

RESULTS

Forty-seven of 157 (29.9%) patients experienced tumor recurrence. The multivariate Cox proportional hazards model revealed that a D value < 0.985 × 10^-3 mm²/s (hazard ratio (HR), 0.190; p = 0.023) was a risk factor for tumor recurrence. Additional risk factors included younger age (HR, 0.328; p = 0.034) and higher serum alpha-fetoprotein (AFP) level (HR, 2.079; p = 0.013). Further, receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of the obtained Cox regression model improved from 0.68 for the combination of AFP and age alone to 0.724 for the combination of D value, AFP, and age.

CONCLUSION

The D value derived from the IVIM model is a potential biomarker for the preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

KEY POINTS

• The recurrence rate of HCC after hepatectomy was higher in patients with ADC, D, and f values that were lower than the optimal cutoff values. • The optimal cutoff values of ADC, D, D*, and f for predicting recurrence in HBV associated HCC were 0.858 × 10^-3 mm²/s, 0.985 × 10^-3 mm²/s, 12.5 × 10^-3 mm²/s, and 23.4%, respectively. • The D value derived from IVIM diffusion-weighted imaging may be a useful biomarker for preoperative prediction of recurrence after hepatectomy in patients with HCC. When combined with age and AFP levels, D can improve the predictive performance for tumor recurrence.

The Application of Intravoxel Incoherent Motion Diffusion-Weighted Imaging in the Diagnosis of Hilar Obstructive Jaundice

OBJECTIVE

The aim of this study was to investigate the potential of intravoxel incoherent motion diffusion-weighted imaging in diagnosing hilar obstructive jaundice.

METHODS

Fifty-nine patients diagnosed with hilar obstructive jaundice were enrolled in our hospital form January 2017 to January 2018. All the patients received scanning by a 3.0-T nuclear magnetic resonance scanner. The values of apparent diffusion coefficient (ADC)slow, ADCfast, and f were obtained and analyzed by 2 experienced radiologists. The differences between patients with hilar biliary obstruction and healthy volunteers in ADCslow, ADCfast, and f values were analyzed. Moreover, the differences between benign and malignant obstructive jaundice in ADCslow, ADCfast, and f values were analyzed. According to the serum levels of total bilirubin, patients were divided into 3 groups: mild, moderate, and severe obstructive jaundice. The differences between the 3 groups in ADCslow, ADCfast, and f values were also analyzed.

RESULTS

The ADCfast values were obviously lower in patients with hilar obstructive jaundice than in healthy controls, whereas no significant difference in the values of ADCslow and f was found between both groups. The optimal cutoff value for ADCfast was 0.0341. The ADCfast values were significantly different between patients with benign and malignant hilar obstructive jaundice. The ADCfast values were negatively associated with the severity of hilar obstructive jaundice.

CONCLUSIONS

Intravoxel incoherent motion diffusion-weighted imaging was a promising method for diagnosing hilar biliary obstruction jaundice.

Characterization of focal liver lesions using the stretched exponential model: comparison with monoexponential and biexponential diffusion-weighted magnetic resonance imaging

OBJECTIVE

To compare the stretched exponential model of diffusion-weighted imaging (DWI) with monoexponential and biexponential models in terms of the ability to characterize focal liver lesions (FLLs).

METHODS

This retrospective study included 180 patients with FLLs who underwent magnetic resonance imaging including DWI with nine b values at 3.0 T. The distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched exponential model; true diffusion coefficient (D_t), pseudo-diffusion coefficient (D_p), and perfusion fraction (f) from a biexponential model; and apparent diffusion coefficient (ADC) were calculated for each lesion. Diagnostic performances of the parameters were assessed through receiver operating characteristic (ROC) analysis. For 20 patients with treated hepatic metastases, the correlation between the DWI parameters and the percentage of tumor necrosis on pathology was evaluated using the Spearman correlation coefficient.

RESULTS

DDC had the highest area under the ROC curve (AUC, 0.905) for differentiating malignant from benign lesions, followed by D_t (0.903) and ADC (0.866), without significant differences among them (DDC vs. D_t, p = 0.946; DDC vs. ADC, p = 0.157). For distinguishing hypovascular from hypervascular lesions, and hepatocellular carcinoma from metastasis, f  had a significantly higher AUC than the other DWI parameters (p < 0.05). The α had the strongest correlation with the degree of tumor necrosis (ρ = 0.655, p = 0.002).

CONCLUSION

The DDC from stretched exponential model of DWI demonstrated excellent diagnostic performance for differentiating malignant from benign FLLs. The α is promising for evaluating the degree of necrosis in treated metastases.

KEY POINTS

• The stretched exponential DWI model is valuable for characterizing focal liver lesions. • The DDC from stretched exponential model shows excellent performance for differentiating malignant from benign focal liver lesions. • The α from stretched exponential model is promising for evaluating the degree of necrosis in hepatic metastases after chemotherapy.

Reproducibility of intravoxel incoherent motion of liver on a 3.0T scanner: free-breathing and respiratory-triggered sequences acquired with different numbers of excitations

Purpose

To optimise the intravoxel incoherent motion (IVIM) imaging of the liver on a 3.0T scanner by assessing parameter reproducibility on free-breathing (FB) and respiratory-triggered (RT) sequences acquired with different numbers of signal averages (NSA).

Material and methods

In this prospective study 20 subjects (M/F: 10/10; age: 25-62 years, mean: 39 years) underwent IVIM magnetic resonance imaging (MRI) on a 3.0T scanner using an 18-channel phase-arrayed coil and four different echo-planar sequences, each with 10 b values: 0, 10, 30, 50, 75, 100, 150, 200, 500, and 900 s/mm². Images were acquired with FB and RT with NSA = 1-4 (FBNSA1-4, RTNSA1-4) and with NSA = 3-6 (FBNSA3-6, RTNSA3-6). Subsequently, for the assessment of reproducibility of IVIM-derived parameters (f, D, D*), each subject was scanned again with an identical protocol during the same session. IVIM parameters were calculated. The distribution of IVIM-parameters for each DWI sequence were given as the median value with first and third quartile. Inter-scan reproducibility for each IVIM parameter was evaluated using coefficient of variance and Bland-Altman difference. Differences between FB sequence and RT sequence were tested using non-parametric Wilcoxon signed-rank test.

Results

Mean coefficient of variance (%) for f, D, and D* ranged from 60 to 64, from 58 to 84, and from 82 to 99 for FBNSA1-4 sequence; from 50 to 69, from 41 to 97, and from 80 to 82 for RTNSA1-4 sequence; from 22 to 27, 15, and from 70 to 80 for FBNSA3-6 sequence; and from 21 to 32, from 12 to, and from 50 to 80 for RTNSA3-6 sequence, respectively.

Conclusions

Increasing the number of signal averages for IVIM acquisitions allows us to improve the reproducibility of IVIM-derived parameters. The sequence acquired during free-breathing with NSA = 3-6 was optimal in terms of reproducibility and acquisition time.

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

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

Intravoxel incoherent motion MRI for differentiating sinonasal small round cell malignant tumours (SRCMTs) from Non-SRCMTs: comparison and correlation with dynamic contrast-enhanced MRI

AIM

To investigate the value of intravoxel incoherent motion (IVIM) in the differentiation of sinonasal small round cell malignant tumours (SRCMTs) from non-SRCMTs and to compare and correlate these results with those of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Ninety patients with histologically confirmed sinonasal malignant tumours (53 SRCMTs and 37 non-SRCMTs) who underwent conventional MRI, IVIM, and DCE-MRI before treatment were enrolled. The IVIM and DCE-MRI parameters were measured. Statistical analyses were performed using Student's t-tests, receiver operating characteristic (ROC) curve analyses, and Spearman's correlation coefficients.

RESULTS

A lower pure diffusion coefficient (D) value and a higher pseudo-diffusion coefficient (D*) value were found in the sinonasal SRCMTs than in the non-SRCMTs (p<0.001 and p=0.011, respectively). Moreover, the mean extravascular extracellular space volume ratio (V_e) of the SRCMTs was significantly lower than that of the non-SRCMTs (p=0.020). ROC curve analysis showed that the diagnostic performance of D outperformed those of the other perfusion and diffusion parameters. A cut-off D value of 0.56 ×10^-3 mm²/s yielded a sensitivity of 80.4%, a specificity of 75%, and an accuracy of 78.2%, with an AUC of 0.825. Significant but poor-to-fair correlations were found between the parameters from IVIM and DCE-MRI.

CONCLUSIONS

The D and D* values of IVIM and the V_e value of DCE-MRI are helpful in distinguishing sinonasal SRCMTs from non-SRCMTs, with the D values having the best diagnostic efficiency.

Meta-analysis of intravoxel incoherent motion magnetic resonance imaging in differentiating focal lesions of the liver

INTRODUCTION

Accurate detection and characterization of focal liver lesions, including differentiation between malignant and benign lesions, are particularly important. The objective of this meta-analysis was to evaluate the parameters of intravoxel incoherent motion (IVIM), including apparent diffusion coefficient (ADC), pure molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f) in differentiating focal liver lesions.

METHODS

IVIM method employed for focal liver lesion and the quality assessment of diagnostic studies were evaluated. Standardized mean differences and 95% confidence intervals were calculated. The heterogeneity was quantified with the I statistic.

RESULTS

The difference between groups was analyzed according to the I values from 6 different studies using fixed effects or random effects models. Significant differences in ADC (P < .001) and D (P < .001) were observed between benign and malignant lesions. Moreover, significant differences in ADC (P < .001), D (P < .001), and f (P = .01) were found between hemangioma and hepatocellular carcinoma (HCC). In addition, no significant difference was observed between the metastases and HCC.

CONCLUSIONS

D and ADC values were useful for the differentiation between benignity and malignancy; higher values of ADC, D, and f were observed in hemangioma compared to HCC. Nevertheless, IVIM did not result as the optimal approach for differentiation between the metastases and HCC.

CT-MRI LI-RADS v2017: A Comprehensive Guide for Beginners

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the second leading cause of cancer-related deceases worldwide. Early diagnosis is essential for correct management and improvement of prognosis. Proposed for the first time in 2011 and updated for the last time in 2017, the Liver Imaging-Reporting and Data System (LI-RADS) is a comprehensive system for standardized interpretation and reporting of computed tomography (CT) and magnetic resonance imaging (MRI) liver examinations, endorsed by the American College of Radiology to achieve congruence with HCC diagnostic criteria in at-risk populations. Understanding its algorithm is fundamental to correctly apply LI-RADS in clinical practice. In this pictorial review, we provide a guide for beginners, explaining LI-RADS indications, describing major and ancillary features and eventually elucidating the diagnostic algorithm with the use of some clinical examples.

Pre-TACE kurtosis of ADCtotal derived from histogram analysis for diffusion-weighted imaging is the best independent predictor of prognosis in hepatocellular carcinoma

PURPOSE

To determine the feasibility of pre-TACE IVIM imaging based on histogram analysis for predicting prognosis in the treatment of unresectable hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Fifty-five patients prospectively underwent 1.5T MRI 1 week before TACE. Histogram metrics for IVIM parameters and ADCs maps between responders and non-responders with mRECIST assessment were compared. Kaplan-Meier, log-rank tests and Cox proportional hazard regression model were used to correlate variables with time to progression (TTP).

RESULTS

Mean (p = 0.022), median (p = 0.043), and 25^th percentile (p < 0.001) of perfusion fraction (PF), mean (p < 0.001), median (p < 0.001), 25^th percentile (p < 0.001) and 75^th percentile (p = 0.001) of ADC_(0,500), mean (p = 0.005), median (p = 0.008) and 25^th percentile (p = 0.039) of ADC_total were higher, while skewness and kurtosis of PF (p = 0.001, p = 0.005, respectively), kurtosis of ADC_(0,500) and ADC_total (p = 0.005, p = 0.001, respectively) were lower in responders compared to non-responders. Multivariable analysis demonstrated that mRECIST was associated with TTP independently, and kurtosis of ADC_total had the best predictive performance for disease progression.

CONCLUSION

Pre-TACE kurtosis of ADC_total is the best independent predictor for TTP.

KEY POINTS

• mRECIST was associated with TTP independently. • Lower kurtosis and higher mean for ADCs tend to have good response. • Pre-TACE kurtosis of ADC _total is the best independent predictor for TTP.

Intravoxel Incoherent Motion: Model-Free Determination of Tissue Type in Abdominal Organs Using Machine Learning

PURPOSE

For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determine tissue type without a priori assumptions on the underlying physiology.

MATERIALS AND METHODS

In 8 healthy volunteers, diffusion data sets were acquired using an echo-planar imaging sequence with 16 b-values in the range between 0 and 1000 s/mm. Using the k-nearest neighbors technique, the machine learning algorithm was trained to distinguish abdominal organs (liver, kidney, spleen, muscle) using the signal intensities at different b-values as training features. For systematic variation of model complexity (number of neighbors), performance was assessed by calculation of the accuracy and the kappa coefficient (κ). Most important b-values for tissue discrimination were determined by principal component analysis.

RESULTS

The optimal trade-off between model complexity and overfitting was found in the range between K = 11 to 13. On "real-world" data not previously applied to optimize the algorithm, the k-nearest neighbors algorithm was capable to accurately distinguish tissue types with best accuracy of 94.5% and κ = 0.92 reached for intermediate model complexity (K = 11). The principal component analysis showed that most important b-values are (with decreasing importance): b = 1000 s/mm, b = 970 s/mm, b = 750 s/mm, b = 20 s/mm, b = 620 s/mm, and b = 40 s/mm. Applying a reduced set of 6 most important b-values, still a similar accuracy was achieved on the real-world data set with an average accuracy of 93.7% and a κ coefficient of 0.91.

CONCLUSIONS

Machine learning allows for a model-free determination of tissue type using intra voxel incoherent motion signal decay curves as features. The technique may be useful for segmentation of abdominal organs or distinction between healthy and pathological tissues.

Preliminary Results of High-Precision Computed Diffusion Weighted Imaging for the Diagnosis of Hepatocellular Carcinoma at 3 Tesla

Objective

To compare the utility of high-precision computed diffusion-weighted imaging (hc-DWI) and conventional computed DWI (cc-DWI) for the diagnosis of hepatocellular carcinoma (HCC) at 3 T.

Methods

We subjected 75 HCC patients to DWI (b-value 150 and 600 s/mm²). To generate hc-DWI we applied non-rigid image registration to avoid the mis-registration of images obtained with different b-values. We defined c-DWI with a b-value of 1500 s/mm² using DWI with b-value 150 and 600 s/mm² as cc-DWI, and c-DWI with b-value 1500 s/mm² using registered DWI with b-value 150 and 600 s/mm² as hc-DWI. A radiologist recorded the contrast ratio (CR) between HCC and the surrounding hepatic parenchyma.

Results

The CR for HCC was significantly higher on hc- than cc-DWIs (median 2.0 vs. 1.8, P < 0.01).

Conclusion

The CR of HCC can be improved with image registration, indicating that hc-DWI is more useful than cc-DWI for the diagnosis of HCC.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

Intravoxel incoherent motion: application in differentiation of hepatocellular carcinoma and focal nodular hyperplasia

PURPOSE

We aimed to explore whether intravoxel incoherent motion (IVIM)-related parameters of hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) demonstrate differences that could be used to differentiate and improve diagnostic efficiency.

METHODS

A total of 27 patients, including 22 with HCC and 5 with FNH, underwent liver 3.0 T magnetic resonance imaging for routine sequences. They were concurrently examined by IVIM diffusion-weighted imaging (DWI) scanning with 11 different b values (0-800 s/mm2). IVIM-derived parameters, such as pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADCtotal), were quantified automatically by post-processing software and compared between HCC and FNH groups. A receiver operating characteristic (ROC) curve was then created to predict their diagnostic value.

RESULTS

D* was weak in terms of reproducibility among the other parameters. ADCtotal, D, and D* were significantly lower in the HCC group than in the FNH group, while f did not show a significant difference. ADCtotal and D had the largest area under the curve values (AUC; 0.915 and 0.897, respectively) and similarly high efficacy to differentiate the two conditions.

CONCLUSION

IVIM provides a new modality to differentiate the HCC and FNH. ADCtotal and D demonstrated outstanding and comparable diagnosing utility.

Intrahepatic cholangiocarcinoma in the setting of HBV-related cirrhosis: Differentiation with hepatocellular carcinoma by using Intravoxel incoherent motion diffusion-weighted MR imaging

Accurate preoperative differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) in the setting of cirrhotic liver is of great clinical significance because the treatment and prognosis of these entities differ markedly. Through a retrospectively research, we sought to determine the diagnostic performances of intravoxel incoherent motion (IVIM) and diffusion weighted imaging (DWI) parameters in the differentiating of ICC and HCC. According to the results, we found that apparent diffusion coefficient (ADC) derived from mono-exponential model and true ADC (ADC^slow) derived from bi-exponential model can be used to distinguish the ICC and HCC, and ADC^slowentailed the higher diagnostic performance than ADC. However, pseudo-ADC (ADC^fast) and perfusion fraction (f) can not be used to differentiate ICC and HCC. These results suggested that IVIM and DWI parameters can be useful in differentiating ICC and HCC and might be helpful in selecting the treatment plan and predicting prognosis.

Can diffusion-weighted imaging distinguish between benign and malignant pediatric liver tumors?

BACKGROUND

There are limited data on utility of diffusion-weighted imaging (DWI) in the evaluation of pediatric liver lesions.

OBJECTIVE

To determine whether qualitative and quantitative DWI can be used to differentiate benign and malignant pediatric liver lesions.

MATERIALS AND METHODS

We retrospectively reviewed MRIs in children with focal liver lesions to qualitatively evaluate lesions noting diffusion restriction, T2 shine-through, increased diffusion, hypointensity on DWI and apparent diffusion coefficient (ADC) maps, and intermediate signal on both, and to measure ADC values. Pathology confirmation or a combination of clinical, laboratory and imaging features, and follow-up was used to determine final diagnosis.

RESULTS

We included 112 focal hepatic lesions in 89 children (median age 11.5 years, 51 female), of which 92 lesions were benign and 20 malignant. Interobserver agreement was almost perfect for both qualitative (kappa 0.8735) and quantitative (intraclass correlation coefficient [ICC] 0.96) diffusion assessment. All malignant lesions showed diffusion restriction. Most benign lesions other than abscesses were not restricted. There was significant association of qualitative restriction with malignancy and non-restriction with benignancy (Fisher exact test P<0.0001). Mean normalized ADC values of malignant lesions (1.23x10^-3 mm²/s) were lower than benign lesions (1.62x10^-3 mm²/s; Student's t-test, P<0.015). However, there was significant overlap of ADC between benign and malignant lesions, with wide range for each diagnosis. Receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.63 for predicting malignancy using an ADC cut-off value of ≤1.20x10^-3 mm²/s, yielding a sensitivity of 78% and a specificity of 54% for differentiating malignant from benign lesions.

CONCLUSION

Qualitative diffusion restriction in pediatric liver lesions is a good predictor of malignancy and can help to differentiate between benign and malignant lesions, in conjunction with conventional MR sequences. Even though malignant lesions demonstrated significantly lower ADC values than benign lesions, the use of quantitative diffusion remains limited in its utility for distinguishing them because of the significant overlap and wide ranges of ADC values.

What can we see with IVIM MRI?

Intravoxel Incoherent Motion (IVIM) refers to translational movements which within a given voxel and during the measurement time present a distribution of speeds in orientation and/or amplitude. The IVIM concept has been used to estimate perfusion in tissues as blood flow in randomly oriented capillaries mimics a pseudo-diffusion process. IVIM-based perfusion MRI, which does not require contrast agents, has gained momentum recently, especially in the field oncology. In this introductory review the basic concepts, models, technical requirements and limitations inherent to IVIM-based perfusion MRI are outlined, as well as new, non-perfusion applications of IVIM MRI, such as virtual MR Elastography.

Intravoxel incoherent motion diffusion-weighted MR imaging for differentiation of benign and malignant musculoskeletal tumours at 3 T

OBJECTIVE

To evaluate the intravoxel incoherent motion (IVIM) diffusion-weighted (DW) MRI for differentiating between benign and malignant musculoskeletal tumours at 3 T.

METHODS

65 patients with treatment-naïve musculoskeletal tumours (47 malignant and 23 benign lesions) who underwent 3 T MRI including IVIM DW imaging were included. IVIM-derived parameters included pure diffusion coefficient (D), perfusion related incoherent microcirculation (D*, pseudodiffusion coefficient), and perfusion fraction (f). IVIM parameters and mono-exponential apparent diffusion coefficient (ADC) were retrospectively measured by two independent musculoskeletal radiologists.

RESULTS

D and ADC values of malignant tumours (923 ± 360, 965 ± 353 µm² s^-1, respectively) were significantly lower than those of benign tumours (1668 ± 546, 1689 ± 526 µm² s^-1) (p < 0.001). F values of malignant tumours (9.6%) were significantly higher than those of benign tumours (7.2%) (p = 0.021), whereas D* values showed no significant difference (p > 0.05). The area under the receiver operating characteristic (ROC) curve of D, ADC and f were 0.874, 0.880 and 0.671, respectively. Using cut-off values of D and ADC of 1200 µm² s^-1, the sensitivity, specificity and accuracy were 92, 83, 89%, 92, 87 and 90%, respectively.

CONCLUSION

D and ADC may be more accurate and reliable for differentiation of malignant from benign musculoskeletal tumours than f and D* at 3 T IVIM DW imaging. Advances in knowledge: Among IVIM-derived parameters, D is more accurate and reliable in differentiating malignant from benign musculoskeletal tumours than f and D* at 3.0T IVIM DW imaging. There was no significant difference in the diagnostic performance of D and ADC.

Diffusion Quantification in Body Imaging

Diffusion-weighted imaging (DWI) is increasingly incorporated into routine body magnetic resonance imaging protocols. DWI can assist with lesion detection and even in characterization. Quantitative DWI has exhibited promise in the discrimination between benign and malignant pathology, in the evaluation of the biologic aggressiveness, and in the assessment of the response to treatment. Unfortunately, inconsistencies in DWI acquisition parameters and analysis have hampered widespread clinical utilization. Focusing primarily on liver applications, this article will review the basic principles of quantitative DWI. In addition to standard mono-exponential fitting, the authors will discuss intravoxel incoherent motion and diffusion kurtosis imaging that involve more sophisticated approaches to diffusion quantification.

Intravoxel Incoherent Motion Diffusion-weighted Imaging: Evaluation of the Differentiation of Solid Hepatic Lesions

PURPOSE

To evaluate whether intravoxel incoherent motion (IVIM)-related parameters could be used to differentiate malignant from benign focal liver lesions (FLLs) and to improve diagnostic efficiency.

METHODS

Seventy-four patients with 75 lesions, including 51 malignant FLLs and 24 benign FLLs, underwent liver 3.0-T magnetic resonance imaging for routine examination sequences. IVIM diffusion-weighted imaging (DWI) with 11 b values (0-800s/mm²) was also acquired concurrently. Apparent diffusion coefficient (ADC_total) and IVIM-derived parameters, such as the pure diffusion coefficient (D), the pseudodiffusion coefficient (D^⁎), and the perfusion fraction (f), were calculated and compared between the two groups. A receiver operating characteristic curve analysis was performed to assess their diagnostic value.

RESULTS

ADC_total, D, and f were significantly lower in the malignant group than in the benign group, whereas D^⁎ did not show a statistical difference. D had a larger area under the curve value (0.968) and higher sensitivity (92.30%) for differentiation.

CONCLUSION

IVIM is a useful method to differentiate malignant and benign FLLs. The D value showed higher efficacy to detect hepatic solid lesions.

Histological Grading of Hepatocellular Carcinomas with Intravoxel Incoherent Motion Diffusion-weighted Imaging: Inconsistent Results Depending on the Fitting Method

Purpose:

To compare the abilities of three intravoxel incoherent motion (IVIM) imaging approximation methods to discriminate the histological grade of hepatocellular carcinomas (HCCs).

Methods:

Fifty-eight patients (60 HCCs) underwent IVIM imaging with 11 b-values (0–1000 s/mm²). Slow (D) and fast diffusion coefficients (D^*) and the perfusion fraction (f) were calculated for the HCCs using the mean signal intensities in regions of interest drawn by two radiologists. Three approximation methods were used. First, all three parameters were obtained simultaneously using non-linear fitting (method A). Second, D was obtained using linear fitting (b = 500 and 1000), followed by non-linear fitting for D^* and f (method B). Third, D was obtained by linear fitting, f was obtained using the regression line intersection and signals at b = 0, and non-linear fitting was used for D^* (method C). A receiver operating characteristic analysis was performed to reveal the abilities of these methods to distinguish poorly-differentiated from well-to-moderately-differentiated HCCs. Inter-reader agreements were assessed using intraclass correlation coefficients (ICCs).

Results:

The measurements of D, D^*, and f in methods B and C (Az-value, 0.658–0.881) had better discrimination abilities than did those in method A (Az-value, 0.527–0.607). The ICCs of D and f were good to excellent (0.639–0.835) with all methods. The ICCs of D^* were moderate with methods B (0.580) and C (0.463) and good with method A (0.705).

Conclusion:

The IVIM parameters may vary depending on the fitting methods, and therefore, further technical refinement may be needed.