Evaluation of two-point Dixon water-fat separation for liver specific contrast-enhanced assessment of liver maximum capacity

MRI Dixon Fat-Corrected Look-Locker T1 Mapping for Quantification of Liver Fibrosis and Inflammation-A Comparison With the Non-Fat-Corrected Shortened Modified Look-Locker Inversion Recovery Technique

OBJECTIVES

This study evaluates the impact of liver steatosis on the discriminative ability for liver fibrosis and inflammation using a novel Dixon water-only fat-corrected Look-Locker T1 mapping sequence, compared with a standard shortened Modified Look-Locker Inversion Recovery (shMOLLI) sequence, with the aim of overcoming the limitation of steatosis-related confounding in liver T1 mapping.

MATERIALS AND METHODS

3 T magnetic resonance imaging of the liver including the 2 T1 mapping sequences and proton density fat fraction (PDFF) was prospectively performed in 24 healthy volunteers and 38 patients with histologically proven liver fibrosis evaluated within 90 days of liver biopsy. Paired Mann-Whitney test compared sequences between participants with and without significant liver steatosis (PDFF cutoff 10%), and unpaired Kruskal-Wallis test compared healthy volunteers to patients with early (F0-2) and advanced (F3-4) liver fibrosis, as well as low (A0-1) and marked (A2-3) inflammatory activity. Univariate and multivariate logistic regression models assessed the impact of liver steatosis on both sequences.

RESULTS

Dixon_W T1 was higher than shMOLLI T1 in participants without steatosis (median 896 ms vs 890 ms, P = 0.04), but lower in participants with liver steatosis (median 891 ms vs 973 ms, P < 0.001). Both methods accurately differentiated between volunteers and patients with early and advanced fibrosis (Dixon_W 849 ms, 910 ms, 947 ms, P = 0.011; shMOLLI 836 ms, 918 ms, 978 ms, P < 0.001), and those with mild and marked inflammation (Dixon_W 849 ms, 896 ms, 941 ms, P < 0.01; shMOLLI 836 ms, 885 ms, 978 ms, P < 0.001). Univariate logistic regression showed slightly lower performance of the Dixon_W sequence in differentiating fibrosis (0.69 vs 0.73, P < 0.01), compensated by adding liver PDFF in the multivariate model (0.77 vs 0.75, P < 0.01).

CONCLUSIONS

Dixon water-only fat-corrected Look-Locker T1 mapping accurately identifies liver fibrosis and inflammation, with less dependency on liver steatosis than the widely adopted shMOLLI T1 mapping technique, which may improve its predictive value for these conditions.

Influence of Gadoxetate disodium to the hepatic proton density fat fraction quantified with the Dixon sequences in a rabbit model

OBJECTIVE

To study the impact of Gx on quantification of hepatic fat contents under metabolic dysfunction-associated steatotic liver disease (MASLD) imaged on VIBE Dixon in hepatobiliary specific phase.

METHODS

Forty-two rabbits were randomly divided into control group (n = 10) and high-fat diet group (n = 32). Imaging was performed before enhancement (Pre-Gx) and at the 13th (Post-Gx13) and 17th (Post-Gx17) min after Gx enhancement with 2E- and 6E-VIBE Dixon to determine hepatic proton density fat fractions (PDFF). PDFFs were compared with vacuole percentage (VP) measured under histopathology.

RESULTS

33 animals were evaluated and including control group (n = 11) and MASLD group (n = 22). Pre-Gx, Post-Gx13, Post-Gx17 PDFFs under 6E-VIBE Dixon had strong correlations with VPs (r2 = 0.8208-0.8536). PDFFs under 2E-VIBE Dixon were reduced significantly (P < 0.001) after enhancement (r2 = 0.7991/0.8014) compared with that before enhancement (r2 = 0.7643). There was no significant difference between PDFFs of Post-Gx13 and Post-Gx17 (P = 0.123) for which the highest consistency being found with 6E-VIBE Dixon before enhancement (r2 = 0.8536). The signal intensity of the precontrast compared with the postcontrast, water image under 2E-VIBE Dixon increased significantly (P < 0.001), fat image showed no significant difference (P = 0.754).

CONCLUSION

2E- and 6E-VIBE Dixon can obtain accurate PDFFs in the hepatobiliary specific phase from 13 to 17th min after Gx enhancement. On 2E-VIBE Dixon (FA = 10°), effective minimization of T1 Bias by the Gx administration markedly improved the accuracy of the hepatic PDFF quantification.

Early and accurate diagnosis of steatotic liver by artificial intelligence (AI)-supported ultrasonography

OBJECTIVES

Steatotic liver disease is the most frequent chronic liver disease worldwide. Ultrasonography (US) is commonly employed for the assessment and diagnosis. Few information is available on the possible use of artificial intelligence (AI) to ameliorate the diagnostic accuracy of ultrasonography.

MATERIALS AND METHODS

An AI-based algorithm was developed using a dataset of US images. We prospectively enrolled 134 patients for algorithm validation. Patients underwent abdominal US and Proton Density Fat Fraction MRI scans (MRI-PDFF), assumed as reference technique. The hepatorenal index was manually calculated (HRIM) by 4 operators. An automatic hepatorenal index (HRIA) was obtained by the algorithm. The accuracy of HRIA to discriminate steatosis grades was evaluated by ROC analysis using MRI-PDFF cut-offs.

RESULTS

Overweight was 40 % of subjects (BMI 26.4 kg/cm2). The median HRIA was 1.11 (IQR 0.32) and the average of 4 manually calculated HRIM was 1.08 (IQR 0.26), with a 15 % inter-operator variability. Both HRIA (R = 0.79, P < 0.0001) and HRIM (R = 0.69, P < 0.0001) significantly correlated with liver fat percentage (MRI-PDFF). According to MRI-PDFF, 32 % of enrolled subjects had steatosis. Discrimination capacity by AUC between patient with steatosis and patient without steatosis was better for HRIA than HRIM (AUC: 0.87 vs. 0.82, respectively). ROC analysis showed an AUC = 0.98 for HRIA with 1.64 cut-off in distinguishing between mild and moderate/severe groups.

CONCLUSIONS

The use of AI improves accuracy and speed of ultrasonography in the diagnosis of liver steatosis. Further studies should evaluate the routine use of this technique in the management of liver steatosis at high cardio-metabolic risk.

Water-Fat Separated T1 Mapping in the Liver and Correlation to Hepatic Fat Fraction

(1) Background: T1 mapping in magnetic resonance imaging (MRI) of the liver has been proposed to estimate liver function or to detect the stage of liver disease, among others. Thus far, the impact of intrahepatic fat on T1 quantification has only been sparsely discussed. Therefore, the aim of this study was to evaluate the potential of water-fat separated T1 mapping of the liver. (2) Methods: A total of 386 patients underwent MRI of the liver at 3 T. In addition to routine imaging techniques, a 3D variable flip angle (VFA) gradient echo technique combined with a two-point Dixon method was acquired to calculate T1 maps from an in-phase (T1_in) and water-only (T1_W) signal. The results were correlated with proton density fat fraction using multi-echo 3D gradient echo imaging (PDFF) and multi-echo single voxel spectroscopy (PDFF_MRS). Using T1_in and T1_W, a novel parameter FF_T1 was defined and compared with PDFF and PDFF_MRS. Furthermore, the value of retrospectively calculated T1_W (T1_W_calc) based on T1_in and PDFF was assessed. Wilcoxon test, Pearson correlation coefficient and Bland-Altman analysis were applied as statistical tools. (3) Results: T1_in was significantly shorter than T1_W and the difference of both T1 values was correlated with PDFF (R = 0.890). FF_T1 was significantly correlated with PDFF (R = 0.930) and PDFF_MRS (R = 0.922) and yielded only minor bias compared to both established PDFF methods (0.78 and 0.21). T1_W and T1_W_calc were also significantly correlated (R = 0.986). (4) Conclusion: T1_W acquired with a water-fat separated VFA technique allows to minimize the influence of fat on liver T1. Alternatively, T1_W can be estimated retrospectively from T1_in and PDFF, if a Dixon technique is not available for T1 mapping.

Survey of water proton longitudinal relaxation in liver in vivo

Objective

To determine the variability, and preferred values, for normal liver longitudinal water proton relaxation rate R₁ in the published literature.

Methods

Values of mean R₁ and between-subject variance were obtained from literature searching. Weighted means were fitted to a heuristic and to a model.

Results

After exclusions, 116 publications (143 studies) remained, representing apparently normal liver in 3392 humans, 99 mice and 249 rats. Seventeen field strengths were included between 0.04 T and 9.4 T. Older studies tended to report higher between-subject coefficients of variation (CoV), but for studies published since 1992, the median between-subject CoV was 7.4%, and in half of those studies, measured R₁ deviated from model by 8.0% or less.

Discussion

The within-study between-subject CoV incorporates repeatability error and true between-subject variation. Between-study variation also incorporates between-population variation, together with bias from interactions between methodology and physiology. While quantitative relaxometry ultimately requires validation with phantoms and analysis of propagation of errors, this survey allows investigators to compare their own R₁ and variability values with the range of existing literature.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00928-x.

Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segmentation, registration, feature extraction and radiomics. In this study, we present an advanced deep learning based INU correction algorithm called residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected magnetic resonance imaging (MRI) images to constrain the model through forcing the calculation of both an INU corrected MRI and a synthetic corrected MRI. A fully convolution neural network integrating residual blocks was applied in the generator of cycle-GAN to enhance end-to-end raw MRI to INU corrected MRI transformation. A cohort of 55 abdominal patients with T1-weighted MR INU images and their corrections with a clinically established and commonly used method, namely, N4ITK were used as a pair to evaluate the proposed res-cycle GAN based INU correction algorithm. Quantitatively comparisons of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were made among the proposed method and other approaches. Our res-cycle GAN based method achieved an NMAE of 0.011 ± 0.002, a PSNR of 28.0 ± 1.9 dB, an NCC of 0.970 ± 0.017, and a SNU of 0.298 ± 0.085. Our proposed method has significant improvements (p < 0.05) in NMAE, PSNR, NCC and SNU over other algorithms including conventional GAN and U-net. Once the model is well trained, our approach can automatically generate the corrected MR images in a few minutes, eliminating the need for manual setting of parameters.