Robust automated calcification meshing for personalized cardiovascular biomechanics.

Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics.

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning.

Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh labels for precise spatial accuracy and the simultaneous optimization of isotropic and anisotropic deformation energies for volumetric mesh quality. Mesh generation takes only 0.13 seconds/scan during inference, and each mesh can be directly used for finite element analyses without any manual post-processing. Calcification meshes can also be subsequently incorporated for increased simulation accuracy. Numerous stent deployment simulations validate the viability of our approach for large-batch analyses. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.

Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training.

We report a toolbox for exploring the modular tuning of genetic circuits, which has been specifically optimized for widespread deployment in STEM environments through a combination of bacterial strain engineering and distributable hardware development. The transfer functions of 16 genetic switches, programmed to express a GFP reporter under the regulation of the (acyl-homoserine lactone) AHL-sensitive luxR transcriptional activator, can be parametrically tuned by adjusting high/low degrees of transcriptional, translational, and post-translational processing. Strains were optimized to facilitate daily large-scale preparation and reliable performance at room temperature in order to eliminate the need for temperature controlled apparatuses, which are both cost-limiting and space-constraining. The custom-designed, automated, and web-enabled fluorescence documentation system allows time-lapse imaging of AHL-induced GFP expression on bacterial plates with real-time remote data access, thereby requiring trainees to only be present for experimental setup. When coupled with mathematical models in agreement with empirical data, this toolbox expands the scalability and scope of reliable synthetic biology experiments for STEM training.