Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: A clinical study on right and left ventricles

Among screening modalities, echocardiography is the fastest, least expensive and least invasive method for imaging the heart. A new generation of three-dimensional (3-D) ultrasound (US) technology has been developed with real-time 3-D (RT3-D) matrix phased-array transducers. These transducers allow interactive 3-D visualization of cardiac anatomy and fast ventricular volume estimation without tomographic interpolation as required with earlier 3-D US acquisition systems. However, real-time acquisition speed is performed at the cost of decreasing spatial resolution, leading to echocardiographic data with poor definition of anatomical structures and high levels of speckle noise. The poor quality of the US signal has limited the acceptance of RT3-D US technology in clinical practice, despite the wealth of information acquired by this system, far greater than with any other existing echocardiography screening modality. We present, in this work, a clinical study for segmentation of right and left ventricular volumes using RT3-D US. A preprocessing of the volumetric data sets was performed using spatiotemporal brushlet denoising, as presented in previous articles Two deformable-model segmentation methods were implemented in 2-D using a parametric formulation and in 3-D using an implicit formulation with a level set implementation for extraction of endocardial surfaces on denoised RT3-D US data. A complete and rigorous validation of the segmentation methods was carried out for quantification of left and right ventricular volumes and ejection fraction, including comparison of measurements with cardiac magnetic resonance imaging as the reference. Results for volume and ejection fraction measurements report good performance of quantification of cardiac function on RT3-D data compared with magnetic resonance imaging with better performance of semiautomatic segmentation methods than with manual tracing on the US data. (laine@columbia.edu)