Virtual Reality Analysis of Three-Dimensional Echocardiographic and Cardiac Computed Tomographic Data Sets

Three-dimensional echocardiographic (3DE) imaging and cardiac computed tomographic (CCT) imaging are important cardiac imaging tools. Despite the three-dimensional nature of these image acquisitions and reconstructions, they are visualized on two-dimensional monitors with shading and coloring to create the illusion of three dimensions. Virtual reality (VR) is a novel tool that allows true three-dimensional visualization and manipulation. The aims of this study were to test the feasibility of converting 3DE and CCT data into three-dimensional VR models, compare the variability of measurements performed in VR and conventional software, assess the diagnostic quality of VR models, and understand the value of VR over conventional viewing.Custom software with clinically relevant postprocessing tools (interactively adjustable visualization parameters, multiplanar reconstructions, cropping planes, and nonplanar measurements) was developed to convert 3DE and CCT data into VR models. Anatomic measurements of 15 3DE and 15 CCT data sets of the mitral valve were compared using conventional software and in the VR environment. Additionally, the diagnostic quality of the VR models created from 3DE and CCT data sets was assessed.The 3DE and CCT data sets were successfully converted into VR models in3 min. The measurement variabilities were reduced by 40% (20.1% vs 12.2%) for 3DE imaging and 34% (15.3% vs 10.1%) for CCT imaging by using VR. The mean time needed for measurements was reduced by 31% (from 61 to 42 sec) for 3DE imaging and 39% (from 37 to 23 sec) for CCT imaging. Most users reported facile manipulation of VR models, diagnostic quality visualization of the anatomy, and high confidence in the measurements.This study demonstrates the feasibility of converting 3DE and CCT data into diagnostic-quality VR models. Compared with conventional imaging, VR analysis is associated with faster navigation and accurate measurements with lower variability.