Image-Based Methods for Phase Estimation, Gating, and Temporal Superresolution of Cardiac Ultrasound

Ultrasound is an effective tool for rapid noninvasive assessment of cardiac structure and function. Determining the cardiorespiratory phases of each frame in the ultrasound video and capturing the cardiac function at a much higher temporal resolution are essential in many applications. Fulfilling these requirements is particularly challenging in preclinical studies involving small animals with high cardiorespiratory rates, requiring cumbersome and expensive specialized hardware.

We present a novel method for the retrospective estimation of cardiorespiratory phases directly from the ultrasound videos. It transforms the videos into a univariate time series preserving the evidence of periodic cardiorespiratory motion, decouples the signatures of cardiorespiratory motion with a trend extraction technique, and estimates the cardiorespiratory phases using a Hilbert transform approach. We also present a robust nonparametric regression technique for respiratory gating and a novel kernel-regression model for reconstructing images at any cardiac phase facilitating temporal superresolution.

We validated our methods using two-dimensional echocardiography videos and electrocardiogram (ECG) recordings of six mice. Our cardiac phase estimation method provides accurate phase estimates with a mean-phase-error range of 3%-6% against ECG derived phase and outperforms three previously published methods in locating ECGs R-wave peak frames with a mean-frame-error range of 0.73-1.36. Our kernel-regression model accurately reconstructs images at any cardiac phase with a mean-normalized-correlation range of 0.81-0.85 over 50 leave-one-out-cross-validation rounds.

Our methods can enable tracking of cardiorespiratory phases without additional hardware and reconstruction of respiration-free single cardiac-cycle videos at a much higher temporal resolution.