Surgical planning of the total cavopulmonary connection: robustness analysis.

In surgical planning of the Fontan connection for single ventricle physiologies, there can be differences between the proposed and implemented options. Here, we developed a surgical planning framework that help determine the best performing option and ensures that the results will be comparable if there are slight geometrical variations. Eight patients with different underlying anatomies were evaluated in this study; surgical variations were created for each connection by changing either angle, offset or baffle diameter. Computational fluid dynamics were performed and the energy efficiency (indexed power loss-iPL) and hepatic flow distribution (HFD) computed. Differences with the original connection were evaluated: iPL was not considerably affected by the changes in geometry. For HFD, the single superior vena cava (SVC) connections presented less variability compared to the other anatomies. The Y-graft connection was the most robust overall, while the extra-cardiac connections showed dependency to offset. Bilateral SVC and interrupted inferior vena cava with azygous continuation showed high variability in HFD. We have developed a framework to assess the robustness of a surgical option for the TCPC; this will be useful to assess the most complex cases where pre-surgery planning could be most beneficial to ensure an efficient and robust hemodynamic performance.

Grouper: a compact, streamable triangle mesh data structure.

We present Grouper: an all-in-one compact file format, random-access data structure, and streamable representation for large triangle meshes. Similarly to the recently published SQuad representation, Grouper represents the geometry and connectivity of a mesh by grouping vertices and triangles into fixed-size records, most of which store two adjacent triangles and a shared vertex. Unlike SQuad, however, Grouper interleaves geometry with connectivity and uses a new connectivity representation to ensure that vertices and triangles can be stored in a coherent order that enables memory-efficient sequential stream processing. We present a linear-time construction algorithm that allows streaming out Grouper meshes using a small memory footprint while preserving the initial ordering of vertices. As a part of this construction, we show how the problem of assigning vertices and triangles to groups reduces to a well-known NP-hard optimization problem, and present a simple yet effective heuristic solution that performs well in practice. Our array-based Grouper representation also doubles as a triangle mesh data structure that allows direct access to vertices and triangles. Storing only about two integer references per triangle--i.e., less than the three vertex references stored with each triangle in a conventional indexed mesh format--Grouper answers both incidence and adjacency queries in amortized constant time. Our compact representation enables data-parallel processing on multicore computers, instant partitioning and fast transmission for distributed processing, as well as efficient out-of-core access. We demonstrate the versatility and performance benefits of Grouper using a suite of example meshes and processing kernels.