Automated Cardiac Valve Tracking for Flow Quantification with Four-dimensional Flow MRI

Purpose: To compare four-dimensional flow MRI with automated valve tracking to manual valve tracking in patients with acquired or congenital heart disease and healthy volunteers. Materials and Methods: In this retrospective study, data were collected from 114 patients and 46 volunteers who underwent four-dimensional flow MRI at 1.5 T or 3.0 T from 2006 through 2017. Among the 114 patients, 33 had acquired and 81 had congenital heart disease (median age, 17 years; interquartile range [IQR], 13-49 years), 51 (45%) were women, and 63 (55%) were men. Among the 46 volunteers (median age, 28 years; IQR, 22-36 years), there were 19 (41%) women and 27 (59%) men. Two orthogonal cine views of each valve were used for valve tracking. Wilcoxon signed-rank test was used to compare analysis times, net forward volumes (NFVs), and regurgitant fractions. Intra-and interobserver variability was tested by using intraclass correlation coefficients (ICCs). Results: Analysis time was shorter for automated versus manual tracking (all patients, 14 minutes [IQR, 12-15 minutes] vs 25 minutes [IQR, 20-25 minutes]; P<.001). Although overall differences in NFV and regurgitant fraction were comparable between both methods, NFV variation over four valves was smaller for automated versus manual tracking (all patients, 4.9% [IQR, 3.3%-6.7%] vs 9.8% [IQR, 5.1%-14.7%], respectively; P<.001). Regurgitation severity was discordant for seven pulmonary valves, 22 mitral valves, and 21 tricuspid valves. Intra-and interobserver agreement for automated tracking was excellent for NFV assessment (intra-and interobserver, ICC >= 0.99) and strong to excellent for regurgitant fraction assessment (intraobserver, ICC >= 0.94; interobserver, ICC >= 0.89). Conclusion: Automated valve tracking reduces analysis time and improves reliability of valvular flow quantification with four-dimensional flow MRI in patients with acquired or congenital heart disease and in healthy volunteers. (c) RSNA, 2018