# Whole-Heart Assessment of Turbulent Kinetic Energy in the Repaired Tetralogy of Fallot
## 4 Discussion
TKE, as it is calculated our study, has been validated using experimental models, and CFD reported a good agreement between MRIs, models, and simulations [44]. Some CFD studies evaluated the influence of the pulmonary artery bifurcation angle, pressure distribution, and flow patterns [45; 46]. In particular, [PERSON] et al. reported a framework for computational modeling using cardiac MRI images and 4D-flow MRI in rTOF patients and included the calculation of kinetic energy, vorticity, and TKE in rTOF [47]. However, these studies were mostly exploratory with a limited number of subjects. Furthermore, CFD has also been used to improve the calculation of wall shear stress, as 4D-flow spatial and temporal resolutions can underestimate this measurement [48; 49; 50]. [PERSON] et al. used CFD to assess the impact of spatial resolutions and reported that TKE estimates were accurate and minorly impacted by resolution, while viscous energy dissipation was underestimated and showed resolution dependance [48]. However, 4D-flow in vivo acquisition mostly reflects the macro-scale of turbulence, as data are acquired with a 2-3 mm\({}^{3}\) resolution [49]. CFD can facilitate the TKE understanding of the turbulence cascade at the Kolmogorov micro-scale, which is something that cannot be achieve in-vivo. Exploring hemodynamic data with CFD in such scales can be computationally demanding and time consuming. Some level of feasibility can be achieved using novel 3D Lattice Boltzmann methods, which have proved to be more efficient for clinical applications [50]. Some additional limitations of this study include a small sample size as not many patients and controls were enrolled in this study. The patient's sample size also limited the ability to investigate specific complications (e.g., pulmonary regurgitation). Age and sex matching or propensity score matching could also improve that study design for TKE assessments. In the current study, controls were on average 7 years older than patients, and no personalized analysis was performed. Long acquisition (>10 min) can be an important limitation in clinical settings. In the current study, the 4D-flow acquisition in all subjects was inferior to 10 min. Acceleration techniques such as compressed sensing can reduce up to 50% of the acquisition time, but the effect on TKE accuracy is still unknown. Another limitation of this study may include variability in the segmentations, as each patient has a different characteristic and morphology of vessels. It must be remarked that segmentations were static and were not adjusted to the dynamic motion of the heart. Heart motion can be significant and affect the averaged TKE calculation in ventricles. A future alternative could be the use of automated segmentation using machine learning methods. Similarly to flow-derived parameters, the accuracy of TKE is greatly dependent upon spatial and temporal resolution. The latter must be particularly considered when scanning children. As reported by [PERSON] et al., the level of intra-voxel underestimation will depend on the turbulence level and velocity encoding strategy [51]. Single encoding gradient can vary by 20%. A Lagrangian velocity spectrum on a voxel-by-voxel basis may correct the latter. Although the approach used for this study to calculate TKE has been validated, more larger and longitudinal studies would be beneficial for understanding the observed hemodynamic fluctuations.