Automatic image-based gating for 4D ultrasound

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

Objective:

Ultrasound is an effective tool for rapid non-invasive 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 is 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.

Methods:

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 super-resolution.

Results:

We validated our methods using 2D echocardiography videos and electrocardiogram (ECG) recordings of 6 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.

Conclusion and Significance:

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.

Manifold learning for image-based breathing gating in ultrasound and MRI

Respiratory motion is a challenging factor for image acquisition and image-guided procedures in the abdominal and thoracic region. In order to address the issues arising from respiratory motion, it is often necessary to detect the respiratory signal. In this article, we propose a novel, purely image-based retrospective respiratory gating method for ultrasound and MRI. Further, we apply this technique to acquire breathing-affected 4D ultrasound with a wobbler probe and, similarly, to create 4D MR with a slice stacking approach. We achieve the gating with Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional image space. Since Laplacian eigenmaps assign to each image frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. We perform the image-based gating on several 2D and 3D ultrasound datasets over time, and quantify its very good performance by comparing it to measurements from an external gating system. For MRI, we perform the manifold learning on several datasets for various orientations and positions. We achieve very high correlations by a comparison to an alternative gating with diaphragm tracking.