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Accurate geometric quantification of the human heart is a key step in thediagnosis of numerous cardiac diseases, and in the management of cardiacpatients. Ultrasound imaging is the primary modality for cardiac imaging,however acquisition requires high operator skill, and its interpretation andanalysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3Dcan enable discovery of new biomarkers and make imaging less dependent onoperator expertise, however most ultrasound systems only have 2D imagingcapabilities. We propose both a simple alteration to the Pix2Vox++ networks fora sizeable reduction in memory usage and computational complexity, and apipeline to perform reconstruction of 3D anatomy from 2D standard cardiacviews, effectively enabling 3D anatomical reconstruction from limited 2D data.We evaluate our pipeline using synthetically generated data achieving accurate3D whole-heart reconstructions (peak intersection over union score > 0.88) fromjust two standard anatomical 2D views of the heart. We also show preliminaryresults using real echo images.

Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views