No abstract

This study investigated a combined use of intravoxel incoherent motion (IVIM) parameters, Dslow ( D), PF ( f), and Dfast ( D*), for liver fibrosis evaluation. Sixteen healthy volunteers (F0) and 33 hepatitis-b patients (stage F1 = 15, stage F2-4 = 18) were included. With a 1.5 T MR scanner and respiration gating, IVIM diffusion-weighted imaging was acquired using a single-shot echo-planar imaging sequence with 10 b values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm. Signal measurement was performed on right liver parenchyma. With a three-dimensional tool, Dslow, PF, and Dfast values were placed along the x axis, y axis, and z axis, and a plane was defined to separate healthy volunteers from patients. The three-dimensional tool demonstrated that healthy volunteers and all patients with liver fibrosis could be separated. Classification and regression tree showed that a combination of PF (PF < 12.55%), Dslow (Dslow < 1.152 × 10 mm/s), and Dfast (Dfast < 13.36 × 10 mm/s) could differentiate healthy subjects and all fibrotic livers (F1-4) with an area under the curve of logistic regression (AUC) of 0.986. The AUC for differentiation of healthy livers versus F2-4 livers was 1. PF offered the best diagnostic value, followed by Dslow; however, all three parameters of PF, Dslow, and Dfast contributed to liver fibrosis detection.

This study investigated a combined use of IVIM parameters Dslow (D), PF (f) and Dfast (D*) for liver fibrosis evaluation. 16 healthy volunteers (F0) and 33 hepatitis-b patients (stage F1= 15, stage F2-4 = 18) were included. With a 1.5-T MR scanner and respiration-gating, IVIM diffusion weighted imaging was acquired using a single-shot echo-planar imaging sequence with ten b-values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm2. Signal measurement was performed on right liver parenchyma. With a 3-dimensional tool, Dslow, PF, and Dfast values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate healthy volunteers from patients. 3-dimensional tool demonstrated healthy volunteers and all patients with liver fibrosis could be separated. Classification and Regression Tree showed a combination of PF (PF < 12.55%), Dslow (Dslow < 1.152 ×10−3 mm2/s) and Dfast (Dfast <13.36 ×10−3 mm2/s) could differentiate healthy subjects and all fibrotic livers (F1-F4) with an area under the curve of logistic regression (AUC) of 0.986. The AUC for differentiation of healthy livers vs. F2-4 livers was 1. PF offered the best diagnostic value, followed by Dslow; however, all three parameters of PF, Dslow, and Dfast contributed to liver fibrosis detection.

For segmented-unconstrained analysis, the selection of threshold b-value = 60 s/mm improves IVIM differentiation between healthy livers and fibrotic livers.