Imaging Science MS Thesis Defense: Dawei Liu
MS Thesis Defense
Volume-based Estimates of Left Ventricular Blood Pool Volume and Ejection Fraction from Multi-plane 2D Ultrasound Images
Imaging Science MS Candidate
Chester F. Carlson Center for Imaging Science, RIT
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Accurate estimations of left ventricular (LV) blood pool volume and left ventricular ejection fraction (LVEF) are crucial for the clinical diagnosis of cardiac disease, patient management, or other therapeutic treatment decisions, especially given a patient's LVEF often affects his or her candidacy for cardiovascular intervention. Ultrasound (US) imaging is the most common and least expensive imaging modalities used to non-invasively image the heart to estimate the LV blood pool volume and assess LVEF. Despite advances in 3D US imaging, 2D US images are routinely used by cardiologists to image the heart and their interpretation is inherently based on the 2D LV blood pool area information immediately available in the US images, rather than 3D LV blood pool volume information. This work proposes a method to reconstruct the 3D geometry of the LV blood pool from three tri-plane 2D US images to estimate the LV blood pool volume and subsequently the LVEF. This technique uses statistical shape model (SSM) of the LV blood pool characterized by several anchor points – the mitral valve hinges, apex, and apex-to-mitral valve midpoints – identified from the three multi-plane 2D US images. Given a new patient image dataset, the diastolic and systolic LV blood pool volumes are estimated using the SSM either as a linear combination of n-closest LV geometries according to the Mahalanobis distance or based on the n-most dominant principal components identified after projecting the new patient into the principal component space defined by the training dataset. The performance of the proposed method was assessed by comparing the estimated LV blood pool volume and LVEF to those measured using the EchoPac PC clinical software on a dataset consisting of 66 patients, and several combinations of 50-16 used for training and validation, respectively. The studies show the proposed method achieves LV volume and LVEF estimates within 5% of those computed using the clinical software. Lastly, this work proposes an approach that required minimal user interaction to obtain accurate 3D estimates of LV blood pool volume and LVEF using multi-plane 2D US images and confirms its performance similar to the ground truth clinical measurements.
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