MRI Extracellular Volume Fraction in Liver Fibrosis-A Comparison of Different Time Points and Blood Pool Measurements.

BACKGROUND: Extracellular volume (ECV) correlates with the degree of liver fibrosis. PURPOSE: To analyze the performance of liver MRI-based ECV evaluations with different blood pool measurements at different time points. STUDY TYPE: Prospective. SAMPLE: 73 consecutive patients (n = 31 females, mean age 56 years) with histopathology-proven liver fibrosis. FIELD STRENGTH/SEQUENCE: 3T acquisition within 90 days of biopsy, including shortened modified look-locker inversion recovery T1 mapping. ASSESSMENT: Polygonal regions of interest were manually drawn in the liver, aorta, vena cava, and in the main, left and right portal vein on four slices before and after Gd-DOTA administration at 5/10/15 minutes. ECV was calculated 1) on one single slice on portal bifurcation level, and 2) averaged over all four slices. STATISTICAL TESTS: Parameters were compared between patients with fibrosis grades F0-2 and F3-F4 with the Mann-Whitney U and fishers exact test. ROC analysis was used to assess the performance of the parameters to predict F3-4 fibrosis. A P-value <0.05 was considered statistically significant. RESULTS: ECV was significantly higher in F3-4 fibrosis (35.4% [33.1%-37.6%], 36.1% [34.2%-37.5%], and 37.0% [34.8%-39.2%] at 5/10/15 minutes) than in patients with F0-2 fibrosis (33.3% [30.8%-34.8%], 33.7% [31.6%-34.7%] and 34.9% [32.2%-36.0%]; AUC = 0.72-0.75). Blood pool T1 relaxation times in the aorta and vena cava were longer on the upper vs. lower slices at 5 minutes, but not at 10/15 minutes. AUC values were similar when measured on a single slice (AUC = 0.69-0.72) or based on blood pool measurements in the cava or portal vein (AUC = 0.63-0.67 and AUC = 0.65-0.70). DATA CONCLUSION: Liver ECV is significantly higher in F3-4 fibrosis compared to F0-2 fibrosis with blood pool measurements performed in the aorta, inferior vena cava, and portal vein at 5, 10, and 15 minutes. However, a smaller variability was observed for blood pool measurements between slices at 15 minutes. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

T1 mapping of the liver and the spleen in patients with liver fibrosis-does normalization to the blood pool increase the predictive value?

PURPOSE: To analyze whether the T1 relaxation time of the liver is a good predictor of significant liver fibrosis and whether normalization to the blood pool improves the predictive value. METHODS: This prospective study was conducted between 03/2016 and 02/2018. One hundred seventy-three patients underwent multiparametric liver MRI at 3 T. The T1 relaxation time was measured in the liver and the spleen, in the aorta, the portal vein, and the inferior vena cava (IVC). T1 relaxation times with and without normalization to the blood pool were compared between patients with (n = 26) and without (n = 141) significant liver fibrosis, based on a cutoff value of 3.5 kPa in MRE as the noninvasive reference standard. For statistics, Student's t test, receiver operating characteristic (ROC) curve analysis, and Pearson's correlation were used. RESULTS: The T1 relaxation time of the liver was significantly longer in patients with liver fibrosis, both with and without blood pool normalization (p < 0.001). T1 relaxation time of the liver allowed prediction of significant liver fibrosis (AUC = 0.88), while normalization to the IVC resulted in a slightly lower performance (AUC = 0.82). The lowest performance was achieved when the T1 relaxation times of the liver were normalized to the aorta (AUC = 0.66) and to the portal vein (AUC = 0.62). The T1 relaxation time of the spleen detected significant liver fibrosis with an AUC of 0.68, and 0.51-0.64 with normalization to the blood pool. CONCLUSION: The T1 relaxation time of the liver is a good predictor of significant liver fibrosis. However, normalization of the blood pool did not improve the predictive value. KEY POINTS: • The T1 relaxation time of the liver is a good predictor of significant liver fibrosis. • Normalization to the blood pool did not improve the predictive value of T1 mapping. • If the blood pool normalization was weighted 30% to the aorta and 70% to the portal vein, the performance was better than normalization to the aorta alone but still lower than normalization to the IVC.

Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning.

PURPOSE: To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline. METHOD: A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation. Liver segmental volumes without liver vessels were retrieved and LSVR was calculated as the liver segmental volumes I-III divided by the liver segmental volumes IV-VIII. LSVR was compared with the expert manual LSVR calculation and the LSVR calculated on CT scans in 30 patients with CT and MRI within 6 months. RESULTS: Theconvolutional neural networkclassified the Couinaud segments I-VIII with an average Dice score of 0.770 ± 0.03, ranging between 0.726 ± 0.13 (segment IVb) and 0.810 ± 0.09 (segment V). The calculated mean LSVR with liver MRI unseen by the model was 0.32 ± 0.14, as compared with manually quantified LSVR of 0.33 ± 0.15, resulting in a mean absolute error (MAE) of 0.02. A comparable LSVR of 0.35 ± 0.14 with a MAE of 0.04 resulted with the LSRV retrieved from the CT scans. The automated LSVR showed significant correlation with the manual MRI LSVR (Spearman r = 0.97, p < 0.001) and CT LSVR (Spearman r = 0.95, p < 0.001). CONCLUSIONS: A convolutional neural network allowed for accurate automated liver segmental volume quantification and calculation of LSVR based on a non-contrast T1-vibe Dixon sequence.

T1 reduction rate with Gd-EOB-DTPA determines liver function on both 1.5 T and 3 T MRI.

Magnetic resonance T1 mapping before and after Gd-EOB-DTPA administration allows quantification of the T1 reduction rate as a non-invasive surrogate marker of liver function. A major limitation of T1 relaxation time measurement is its dependency on MRI field strengths. Since T1 reduction rate is calculated as the relative shortening of T1 relaxation time before and after contrast administration, we hypothesized that the T1 reduction rate is comparable between 1.5 and 3 T. We thus compared liver T1 relaxation times between 1.5 and 3 T in a total of 243 consecutive patients (124, 1.5 T and 119, 3 T) between 09/2018 and 07/2019. T1 reduction rates were compared between patients with no cirrhosis and patients with cirrhosis Child-Pugh A-C. There was no significant difference of T1 reduction rate between 1.5 and 3 T in any patient group (p-value 0.126-0.861). On both 1.5 T and 3 T, T1 reduction rate allowed to differentiate between patients with no cirrhosis and patients with liver cirrhosis Child A-C (p < 0.001). T1 reduction rate showed a good performance to predict liver cirrhosis Child A (AUC = 0.83, p < 0.001), Child B (AUC = 0.83, p < 0.001) and Child C (AUC = 0.92, p < 0.001). In conclusion, T1 reduction rate allows to determine liver function on Gd-EOB-DTPA MRI with comparable values on 1.5 T and 3 T.

Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions.

We evaluated the effectiveness of automated segmentation of the liver and its vessels with a convolutional neural network on non-contrast T1 vibe Dixon acquisitions. A dataset of non-contrast T1 vibe Dixon liver magnetic resonance images was labelled slice-by-slice for the outer liver border, portal, and hepatic veins by an expert. A 3D U-Net convolutional neural network was trained with different combinations of Dixon in-phase, opposed-phase, water, and fat reconstructions. The neural network trained with the single-modal in-phase reconstructions achieved a high performance for liver parenchyma (Dice 0.936 ± 0.02), portal veins (0.634 ± 0.09), and hepatic veins (0.532 ± 0.12) segmentation. No benefit of using multi-modal input was observed (p = 1.0 for all experiments), combining in-phase, opposed-phase, fat, and water reconstruction. Accuracy for differentiation between portal and hepatic veins was 99% for portal veins and 97% for hepatic veins in the central region and slightly lower in the peripheral region (91% for portal veins, 80% for hepatic veins). In conclusion, deep learning-based automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon was highly effective. The single-modal in-phase input achieved the best performance in segmentation and differentiation between portal and hepatic veins.

Noninvasive assessment of clinically significant portal hypertension using ΔT1 of the liver and spleen and ECV of the spleen on routine Gd-EOB-DTPA liver MRI.

PURPOSE: To analyze the predictive value of ΔT1 of the liver and spleen as well as the extracellular volume fraction (ECV) of the spleen as noninvasive biomarkers for the determination of clinically significant portal hypertension (CSPH) on routine Gd-EOB-DTPA liver MRI. METHOD: 195 consecutive patients with known or suspected chronic liver disease from 9/2018 to 7/2019 with Gd-EOB-DTPA liver MRI and abdominal T1 mapping were retrospectively included. Based on the presence of splenomegaly with thrombocytopenia, ascites and portosystemic collaterals, the patients were divided into noCSPH (n = 113), compensated CSPH (cCSPH, ≥1 finding without ascites; n = 55) and decompensated CSPH (dCSPH, ascites ± other findings; n = 27). T1 times were measured in the liver, spleen and abdominal aorta in the unenhanced and contrast-enhanced T1 maps. Native T1 times and ΔT1 of the liver and spleen as well as ECV of the spleen were compared between groups using the Kruskal-Wallis test with Dunn's post hoc test. Furthermore, cutoff values for group differentiation were calculated using ROC analysis with Youden's index. RESULTS: ΔT1 of the liver was significantly lower in patients with cCSPH and dCSPH (p < 0.001) compared to patients with noCSPH. In the ROC analyses for differentiation between noCSPH and CSPH (cCSPH + dCSPH), a cutoff of < 0.67 for ΔT1 of the liver (AUC = 0.79) performed better than ΔT1 (AUC = 0.69) and ECV (AUC = 0.63) of the spleen with cutoffs of > 0.29 and > 41.9, respectively. CONCLUSION: ΔT1 of the liver and spleen in addition to ECV of the spleen allow for determination of CSPH on routine Gd-EOB-DTPA liver MRI.