Semi-Automatic Planning and Three-Dimensional Electrospinning of Patient-Specific Grafts for Fontan Surgery

Abstract

This paper proposes a semi-automatic Fontan surgery planning method for designing and manufacturing hemodynamically optimized patient-specific grafts. Fontan surgery is a palliative procedure for patients with a single ventricle heart defect by creating a new path using a vascular graft for the deoxygenated blood to be directed to the lungs, bypassing the heart. However, designing patient-specific grafts with optimized hemodynamic performance is a complex task due to the variety of patient-specific anatomies, confined surgical planning space, and the requirement of simultaneously considering multiple design criteria for vascular graft optimization. To address these challenges, we used parameterized Fontan pathways to explore patient-specific vascular graft design spaces and search for optimal solutions by formulating a nonlinear constrained optimization problem, which minimizes indexed power loss (iPL) of the Fontan model by constraining hepatic flow distribution (HFD), percentage of abnormal wall shear stress (%WSS) and geometric interference between Fontan pathways and the heart models (InDep) within clinically acceptable thresholds. Gaussian process regression was employed to build surrogate models of the hemodynamic parameters as well as InDep and [Formula: see text] (conduit model smoothness indicator) for optimization by pattern search. We tested the proposed method on two patient-specific models (n=2). The results showed the automatically optimized (AutoOpt) Fontan models hemodynamically outperformed or at least are comparable to manually optimized Fontan models with significantly reduced surgical planning time (15 hours versus over 2 weeks). We also demonstrated feasibility of manufacturing the AutoOpt Fontan conduits by using electrospun nanofibers.

Fig. 1.

Illustration of a single ventricle heart and a Fontan pathway. The Fontan conduit connects the PA and the inferior vena cava (IVC). Deoxygenated blood from the superior vena cava (SVC) and IVC is directed to the lungs.

Fig. 2.

Schematic workflow diagram of the semi-automatic Fontan surgical planning, patient-specific graft manufacturing, and implantation. Starting with three-dimensional (3D) contrast-enhanced magnetic resonance angiography (MRA) data, 3D models of the superior vena cava (SVC), the pulmonary arteries (PA), the inferior vena cava (IVC), the aorta and the heart are reconstructed by image segmentation. Fontan surgical planning is initialized by the 3D models and the blood flow data and includes conduit parameterization, development of surrogate models, and constrained optimization. A patient-specific optimized graft is manufactured by electrospun nanofibers before implantation.

Fig. 3.

Illustration of model preparation for automatic Fontan pathway planning. (A) 3D reconstruction of the Fontan model for revision with the heart model by applying image segmentation on the patient’s MRI data. (B)-(C) Preparing Fontan revision model for hemodynamic simulation by making clean cuts and extensions at inlets and outlets for prescribing the BC. (D) Completed preparation of a superior cavopulmonary connection (SCPC) model by removing the native Fontan pathway.

Fig. 4.

Fontan conduit parameterization. The conduit starts from the inferior vena cava (IVC) cutting surface, ends at the superior cavopulmonary connection (SCPC) ellipse which moves along the centerline between the left pulmonary artery (LPA) and the right pulmonary artery (RPA). The design parameters in the red boxes explore the geometry of a Fontan pathway.

Fig. 5.

Fontan conduit modeling and evaluation of conduit model quality. (A) Construction of the conduit’s surface points based on a set of design parameters. (B) Conduit quality evaluation by comparing the conduit’s centerline curvature radii ${\widehat{\mathbf{r}}}_i$ and the conduit’s radii r_i. (C) Case illustration of $\Vert {\mathbf{r}}_i\Vert >\Vert {\widehat{\mathbf{r}}}_i\Vert$.

Fig. 6.

Mesh preparation for Fontan hemodynamic simulation. (A) Illustration of the SCPC model, the inferior vena cava (IVC) model, and the conduit model as individual surface meshes. (B) Merging of the surface meshes from different models into an integrated Fontan surface mesh. (C) Generating mesh for computational fluid dynamics (CFD) simulation and specifying boundary areas and %WSS measurement areas.

Fig. 7.

Computation of geometric interference between a Fontan conduit and the heart model. (A) Illustration of feasible and infeasible Fontan pathways in Fontan surgical planning. (B) 3D Illustration of the geometric interference with the highlighted intersection volume. (C) Illustration of computing intersection depth (InDep) based on the intersection volume.

Fig. 8.

Hemodynamic performance comparison of Fontan pathway designs of Case 1 and Case 2 among native models, the surgeon’s unconstrained modeling (SUM), the models from engineer’s manual optimization (ManuOpt) and automatic optimization. The values highlighted in red are outside their normal ranges or constraints (iPL<0.03, %WSS<10%, 0.67≤HFD≤1.5). The red rectangles indicate the areas for computing %WSS.

Fig. 9.

Sensitivity analysis of AutoOpt graft implantation. The top row and the bottom row demonstrate how the graft connection angle offsets and the graft connection displacements affect the hemodynamic performance respectively.

Fig. 10.

Hemodynamic performance of AutoOpt grafts under uncertain BC.

Fig. 11.

Geometry changes of AutoOpt grafts by introducing uncertain LPA/RPA flow splits, and their resulted hemodynamic performance.

Fig. 12.

Hemodynamic performance of AutoOpt grafts under exercise conditions. Original indicates the BC in Table I. The BC of 2×Q_IVC and 3×Q_IVC are indicated in the last two rows of Table III.

Fig. 13.

3D printed mandrels and AutoOpt TEVG for Case 1 and Case 2.