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To evaluate the potential of diffusion kurtosis imaging (DKI) analysis with the breath-hold technique to assess the stage or classify hepatic fibrosis.Patients (n=67) suspected of having a disease of the hepatobiliary system examined by diffusion-weighted imaging (DWI) using a 3.0-T magnetic resonance imaging unit were enrolled in this study. To evaluate hepatic fibrosis, mean kurtosis, Mean apparent diffusion (MD) and apparent diffusion coefficient (ADC) values were compared between groups with varying fibrosis; F0-F1, F2-F3, and F4. The Steel-Dwass test was used for overall comparisons. Correlations between the fibrosis stage and mean kurtosis, MD or ADC values were assessed using Spearman's rank correlation. Discriminative capacities of DKI were evaluated using receiver operating characteristic (ROC) analysis.There were significant differences in ADC, MD and mean kurtosis values between non-cirrhosis and cirrhosis groups. Moreover, the mean kurtosis value was statistically different between the F0-F1 and F2-F3, F0-F1 and F4, and F2-F3 and F4 groups (all P0.05). MD value was statistically different between the F0-F1 and F4 groups, and F2-F3 and F4 groups (all P0.05). However, there was no significant difference in ADC values for all groups (all P0.05). In addition, mean kurtosis and MD values significantly correlated with the extent of hepatic fibrosis staging (Spearman's rank correlation coefficient, ρ=0.851 and -0.672; P0.0001). However, ADC values did not reveal a correlation with the extent of hepatic fibrosis staging (ρ=-0.227; P=0.078). According to the ROC analysis for the assessment of no fibrosis (F0), fibrosis (≥F1), and advanced fibrosis (≥F2) and liver cirrhosis, the DKI cut-off values were 0.923, 0.955, and 1.11, respectively.Using the DKI method with the breath-hold technique in the liver, the stage of hepatic fibrosis can be classified into normal and early hepatic fibrosis, substantial stages, and advanced hepatic fibrosis.

OBJECTIVES: We aimed to determine whether diffusion kurtosis imaging (DKI) analysis with the breath-hold technique can replace liver function results obtained from laboratory tests. METHODS: Patients (n = 79) suspected of having a hepatobiliary disease, and control group without liver diseases (n = 15) were examined with non-Gaussian diffusion-weighted imaging using a 3.0 T magnetic resonance imaging unit. Based on the findings of DKI, various blood serum parameters, including the indocyanine green (ICG) retention rate 15 min after an intravenous injection of ICG (ICG-R15) and mean kurtosis values and Child–Pugh and albumin–bilirubin (ALBI) scores, were calculated. In total, 17 patients were tested using ICG-R15. For evaluating liver function, correlations between the mean kurtosis value and the Child–Pugh score, ALBI score, and ICG-R15 value as indicators of liver function obtained from blood data were assessed using Spearman’s rank correlation. In apparent diffusion coefficient as well, we assessed correlations with these indicators. RESULTS: The mean kurtosis value correlated with the Child–Pugh score (Spearman’s rank-correlation coefficient, ρ = 0.3992; p < 0.0001). Moreover, the mean kurtosis value revealed a correlation with the ICG-R15 value (Spearman’s rank-correlation coefficient, ρ = 0.5972; p = 0.00114). The correlation between the mean kurtosis value and the ALBI score was the poorest among these (Spearman’s rank-correlation coefficient, ρ = 0.3395; p = 0.0008). CONCLUSION: Liver function correlating with the Child–Pugh score and ICG-R15 value can be quantitatively estimated using the mean kurtosis value obtained from DKI analysis. DKI analysis with the breath-hold technique can be used to determine liver function instead of performing laboratory tests. ADVANCES IN KNOWLEDGE: Previous studies have not evaluated liver function in vivo using DKI.

With the wide dissemination of computed tomography (CT) screening for lung cancer, measuring the nodule volume accurately with computer-aided volumetry software is increasingly important. Many studies for determining the accuracy of volumetry software have been performed using a phantom with artificial nodules. These phantom studies are limited, however, in their ability to reproduce the nodules both accurately and in the variety of sizes and densities required. Therefore, we propose a new approach of using computer-simulated nodules based on the point spread function measured in a CT system. The validity of the proposed method was confirmed by the excellent agreement obtained between computer-simulated nodules and phantom nodules regarding the volume measurements. A practical clinical evaluation of the accuracy of volumetry software was achieved by adding simulated nodules onto clinical lung images, including noise and artifacts. The tested volumetry software was revealed to be accurate within an error of 20 % for nodules >5 mm and with the difference between nodule density and background (lung) (CT value) being 400–600 HU. Such a detailed analysis can provide clinically useful information on the use of volumetry software in CT screening for lung cancer. We concluded that the proposed method is effective for evaluating the performance of computer-aided volumetry software.