Quantification of Anatomical and Fluid-Dynamic Anomalies in Fontan Patients Based on Magnetic Resonance Imaging





Magnetic resonance imaging, 4D flow, Fluid dynamics, Fontan procedure


Background. Univentricular diseases are lethal congenital diseases affecting about 2 % of newborns in the western world. Due to these pathologies, only one ventricle pumps blood into the circulatory bed, and arterial and venous blood are mixed, preventing from properly providing tissues and organs with oxygen. These pathologies are currently treated through the so-called Fontan procedure, which is a multi-step and complex surgical approach. The Fontan procedure aims at obtaining the anatomical separation between the systemic and pulmonary circulations, and hence between oxygenated and non-oxygenated blood. However, the only ventricle present in the heart remains the only pumping organ, and blood flow in the pulmonary circulation is merely passive. Also, and importantly, the post-surgical anatomy of the junction between systemic veins and pulmonary arteries is markedly non-physiological. As such, it is associated with altered blood fluid dynamics, undesired energy losses, and, ultimately, sub-optimal quality of life and short life expectancy.

Objective. On this basis, clinicians need tools to 1) quantify the post-surgical anatomical and fluid-dynamic alterations, 2) correlate these anatomies to the patients’ prognosis, and 3) identify criteria to improve Fontan surgery.

Methods. In order to support the pursue of these goals, we developed a computational tool for the processing of 4D flow data, i.e., phase contrast magnetic resonance images yielding information on the velocity of tissues within a 3D domain. The tool allows for reconstructing the 3D geometry of the surgically treated anatomical district and, through a semi-automated user-interface, extracting relevant geometrical scores, as well as quantifying flow rates in the different vessels, energy losses, and wall shear stresses. A numerical method based on the finite element approach was implemented to estimate relative pressures.

Results. The developed tool was preliminarily applied to the analysis of the datasets of six pediatric patients. The analysis of data obtained by two independent users highlighted a good repeatability of geometrical reconstructions, and hence of the quantification of geometrical scores. The method for the quantification of relative pressures was preliminarily tested in a simplified model of the thoracic aorta, with encouraging results.

Conclusions. The developed computational tool, which, to the best of our knowledge, is completely novel, helps clinicians to quantify the post-surgical anatomical and fluid-dynamic alterations. Ongoing activities include its application to the real datasets, and the extension of the analysis to a wider cohort of patients, so to check for correlations between the quantitative geometrical and fluid-dynamic indexes with the patients’ prognosis. Such possible correlations could help identifying criteria to improve Fontan surgery.


[1] S. Attanavanich and P. Lertsithichai, “Extracardiac conduit versus lateral tunnel for total cavopulmonary connections”, J. Med. Assoc. Thai., vol. 90, no. 11, pp. 2513–2518, 2007. doi: 10.1067/mtc.2001.116947

[2] D.A. de Zélicourt and V. Kurtcuoglu, “Patient-specific surgical planning, where do we stand? The example of the fontan procedure”, Annals Biomed. Eng., vol. 44, no. 1, pp. 174–186, 2016. doi: 10.1007/s10439-015-1381-9

[3] A. Roldán-Alzate et al., “Hemodynamic study of TCPC using in vivo and in vitro 4D flow MRI and numerical simulation”, J. Cardiovasc. Magn. Reson., vol. 16 (Suppl. 1): W39, P. 1–3, 2014. doi: 10.1186/1532-429X-16-S1-W39

[4] A. Frydrychowicz et al., “Four-dimensional phase contrast magnetic resonance angiography: Potential clinical applications”, Europ. J. Radiol., vol. 80, no. 1, pp. 24–35, 2011. doi: 10.1016/j.ejrad.2011.01.094

[5] J. Bock et al., “Optimized pre-processing of time-resolved 2D and 3D phase contrast MRI data”, in Proc. 15th Annual Meeting of Int. Soc. Mag. Reson. Med., Berlin, 2007, p. 3138.

[6] F.Y. Shih, Image Processing and Pattern Recognition: Fundamentals and Techniques. New Jersey: Wiley-IEEE Press, 2010.

[7] F. Piattiet al., “Towards the improved quantification of in vivo abnormal wall shear stresses in BAV-affected patients from 4D-flow imaging: Benchmarking and application to real data”, J. Biomech., vol. 50, pp. 93–101, 2017. doi:10.1016/ j.jbiomech.2016.11.044

[8] M. Cibiset al., “The effect of resolution on viscous dissipation measured with 4D flow MRI in patients with Fontan circulation: Evaluation using computational fluid dynamics”, J. Biomech., vol. 48, no. 12, pp. 2984–2989, 2015. doi: 10.1016/ j.jbiomech.2015.07.039

[9] S.B.S. Krittian et al., “A finite-element approach to the direct computation of relative cardiovascular pressure from time-resolved MR velocity data”, Med. Image Anal., vol. 16, no. 5, pp. 1029–1037, 2012. doi: 10.1016/j.media.2012.04.003