Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2‐MRI

• Published in: Journal of magnetic resonance imaging Vol. 47; no. 6; pp. 1542 - 1551
• Main Authors: Tipirneni‐Sajja, Aaryani, Song, Ruitian, McCarville, M. Beth, Loeffler, Ralf B., Hankins, Jane S., Hillenbrand, Claudia M.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.06.2018
• Subjects: Automation; Blood vessels; Contouring; Data processing; Field strength; Frangi vesselness filter; Image contrast; Image processing; Image segmentation; Iron; iron quantification; Liver; liver iron overload; Magnetic resonance imaging; Methods; Multiscale analysis; Parenchyma; Patients; R2 MRI; Regression analysis; Statistical analysis; Statistical tests; T2 thresholding; Time dependence; whole‐liver ROI; Workflow; Frangi vesselness filter; iron quantification; R2 MRI; liver iron overload; whole-liver ROI; T2 thresholding
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.25880

Background Extraction of liver parenchyma is an important step in the evaluation of R2*‐based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole‐liver contouring and T2*‐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 R2*‐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, T2* map, and R2* map—to segment blood vessels and extract liver tissue for R2*‐based HIC assessment. Segmentation and R2* results obtained using this automated technique were compared with those from a reference T2*‐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 R2* results, obtained with the automated and reference techniques. Results Mean liver R2* values estimated from all three filter‐based methods showed excellent agreement with the reference method (slopes 1.04–1.05, R2 > 0.99, P < 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87–88%. The T2*‐thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in R2* 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. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1542–1551.