Assessing aortic motion with automated 3D cine balanced steady state free precession cardiovascular magnetic resonance segmentation.

purposeTo apply a free-running three-dimensional (3D) cine balanced steady state free precession (bSSFP) cardiovascular magnetic resonance (CMR) framework in combination with artificial intelligence (AI) segmentations to quantify time-resolved aortic displacement, diameter and diameter change.

methodsIn this prospective study, we implemented a free-running 3D cine bSSFP sequence with scan time of approximately 4 min facilitated by pseudo-spiral Cartesian undersampling and compressed-sensing reconstruction. Automated segmentation of the aorta in all cardiac timeframes was applied through the use of nnU-Net. Dynamic 3D motion maps were created for three repeated scans per volunteer, leading to the detailed quantification of aortic motion, as well as the measurement and change in diameter of the ascending aorta.

resultsA total of 14 adult healthy volunteers (median age, 28 years (interquartile range [IQR]: 26.0-31.3), 6 females) were included. Automated segmentation compared to manual segmentation of the aorta test set showed a Dice score of 0.93 ± 0.02. The median (IQR) over all volunteers for the largest maximum and mean ascending aorta (AAo) displacement in the first scan was 13.0 (4.4) mm and 5.6 (2.4) mm, respectively. Peak mean diameter in the AAo was 25.9 (2.2) mm and peak mean diameter change was 1.4 (0.5) mm. The maximum individual variability over the three repeated scans of maximum and mean AAo displacement was 3.9 (1.6) mm and 2.2 (0.8) mm, respectively. The maximum individual variability of mean diameter and diameter change were 1.2 (0.5) mm and 0.9 (0.4) mm.

conclusionA free-running 3D cine bSSFP CMR scan with a scan time of four minutes combined with an automated nnU-net segmentation consistently captured the aorta's cardiac motion-related 4D displacement, diameter, and diameter change.