1356829 - 4D aortic motion maps from high isotropic resolution 3D CINE balanced steady state free precession at 3T and automated segmentations

In this study we propose 4D aortic displacement and diameter change derivations based on isotropic high resolution 3D CINE CMR as surrogates for aortic stiffness metrics and pulse wave velocity,  which can be hard to quantify in case of stiff aortas, such as those found in severe Marfan syndrome.

Methods:

Fifteen healthy control subjects (7 woman, ranging in age between 25-44 years, mean: 29 ± 5 years) were enrolled and underwent three MRI examinations at 3T (Ingenia, Philips, Best, NL) separated by 2-5 minutes (test, retest)  and 2 weeks (rescan) respectively. The aorta was visualized using a free-running 3D CINE balanced steady state free precession (bSSFP) scan with retrospective cardiac binning and respiratory binning and correction. Scan parameters were: isotropic spatial resolution of (1.6 mm)³, FOV: 256 x 256 x 88 mm³, TE/TR: 1.7/3.4 ms, Flip Angle: 30°, scan time of ~3min:48s and reconstructed temporal resolution of ~67msec (15 cardiac frames). Manual segmentations of this 3D volume in two cardiac phases, end-systole and mid-diastole, allowed for the training of a neural network that subsequently automatically segmented all cardiac phases of the 3D CINE scan. nnUNet¹ was adopted for this task and trained on the manual segmentations of 11 volunteers, and tested on 4 volunteers.
Time-resolved segmentations were used to derive 4D displacement (in mm) of the ascending aorta (AAo) using an iterative closest point (ICP) registration method² of a single reference mid-diastolic phase to all timeframes. The midpoint of the aortic arch was determined on this reference frame and used as boundary for AAo region. Variability of measurements per volunteer were measured as the range (disp_max - disp_min) and all continuous parameters are expressed as median ± interquartile range. 

Results:

The nnUNet segmentations of the test set resulted in a mean Dice score of 0.931 ± 0.017. Figure 1 shows the segmentations for three cardiac frames with corresponding diameters (top), displacements (middle) and diameter changes (bottom). Figure 2 shows the test-retest-rescan comparisons of maximum and mean AAo displacement over the cardiac cycle for all volunteers. Across all volunteers, the systolic (t=10) maximum and mean AAo displacement of the first scan was 10.0 ± 3.4 mm and 3.3 ± 1.1 mm. The maximum individual variability over the three scans of maximum and mean AAo displacement was respectively 3.08  ± 2.28 mm and 1.13 ± 0.79 mm. Figure 3 demonstrates the mean and max displacement curves averaged (± standard error) over all subjects for the test scan.
 


Conclusion:

We have presented a novel method based on 3D cine CMR combined with machine learning segmentation and registration algorithms to visualize and quantify aortic diameter changes and bulk displacement over the cardiac cycle. In future work we will apply this method to patients with Marfan syndrome to investigate whether this genetic disease causes a decrease in aortic diameter change and bulk motion compared to healthy controls.