Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by <mml:msubsup>R2*-MRI

Background: Extraction of liver parenchyma is an important step in the evaluation of R-2(*)-based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole-liver contouring and T-2(*)--thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time-consuming, and susceptible to interreviewer variability. Purpose: To implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of R-2(*)--based HIC. Study Type: Retrospective analysis of clinical data. Subjects: Data from 511 MRI exams performed on 257 patients were analyzed. Field Strength/Sequence: All patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC. Assessment: An automated method based on a multiscale vessel enhancement filter was investigated for three input data types contrast-optimized composite image, T-2(*) map, and R-2(*) map to segment blood vessels and extract liver tissue for R-2(*)-based HIC assessment. Segmentation and R-2(*) results obtained using this automated technique were compared with those from a reference T-2(*)-thresholding technique performed by a radiologist. Statistical Tests: The Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland-Altman analyses were performed to compare the R-2(*) results, obtained with the automated and reference techniques. Results: Mean liver R-2(*) values estimated from all three filter-based methods showed excellent agreement with the reference method (slopes 1.04-1.05, R-2 > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87-88%. The T-2(*)-thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R-2(*) values compared to the automated method. Data Conclusion: The excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management.