Related Experiment Video
Updated: Jul 10, 2026

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
Published on: March 24, 2023
Characterization of focal liver lesions by ADC measurements using a respiratory triggered diffusion-weighted
Melanie Bruegel1, Konstantin Holzapfel, Jochen Gaa
1Department of Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany. mbruegel@roe.med.tum.de
Abstract:
The aim of this study was to determine apparent diffusion coefficients (ADCs) of focal liver lesions on the basis of a respiratory triggered diffusion-weighted single-shot echo-planar MR imaging sequence (DW-SS-EPI) and to evaluate whether ADC measurements can be used to characterize lesions. One hundred and two patients with focal liver lesions [11 hepatocellular carcinomas (HCC), 82 metastases, 4 focal nodular hyperplasias (FNH), 56 hemangiomas and 51 cysts; mean size, 16.6 mm; range 5-92 mm] were examined on a 1.5-T system using respiratory triggered DW-SS-EPI (b-values: 50, 300, 600 s/mm2). Results were correlated with histopathologic data and follow-up imaging. The ADCs of different lesion types were compared, and lesion discrimination using optimal thresholds for ADCs was evaluated. Mean ADCs (x10(-3)mm2/s) were 1.24 and 1.04 for normal and cirrhotic liver parenchyma and 1.05, 1.22, 1.40, 1.92 and 3.02 for HCCs, metastases, FNHs, hemangiomas and cysts, respectively. Mean ADCs differed significantly for all lesion types except for comparison of metastases with HCCs and FNHs. Overall, 88% of lesions were correctly classified as benign or malignant using a threshold value of 1.63 x 10(-3)mm2/s. Measurements of the ADCs of focal liver lesions on the basis of a respiratory triggered DW-SS-EPI sequence may constitute a useful supplementary method for lesion characterization.
More Related Videos
05:37A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging
Published on: October 20, 2023
15:48Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014