Single-heartbeat electromechanical wave imaging with optimal strain estimation using temporally unequispaced acquisition sequences

Abstract

Electromechanical Wave Imaging (EWI) is a non-invasive, ultrasound-based imaging method capable of mapping the electromechanical wave (EW) in vivo, i.e., the transient deformations occurring in response to the electrical activation of the heart. Achieving the optimal imaging frame rates, in terms of the elastographic signal-to-noise ratio, to capture the EW in a full-view of the heart poses a technical challenge due to the limitations of conventional imaging sequences, in which the frame rate is low and tied to the imaging parameters. To achieve higher frame rates, EWI is typically performed in multiple small regions of interest acquired over separate heartbeats, which are then combined into a single view. However, the reliance on multiple heartbeats has previously precluded the method from its application in non-periodic arrhythmias such as fibrillation. Moreover, the frame rates achieved remain sub-optimal, because they are determined by the imaging parameters rather than being optimized to image the EW. In this paper, we develop a temporally-unequispaced acquisition sequence (TUAS) for which a wide range of frame rates are achievable independently of the imaging parameters, while maintaining a full view of the heart at high beam density. TUAS is first used to determine the optimal frame rate for EWI in a paced canine heart in vivo. The feasibility of performing single-heartbeat EWI during ventricular fibrillation is then demonstrated. These results indicate that EWI can be performed optimally, within a single heartbeat, during free breathing, and implemented in real time for periodic and non-periodic cardiac events.

High-contrast ultrafast imaging of the heart

“…The electromechanical activation has been imaged at typical frame rates of 800-2000 images/s using single diverging waves [35], [36]. Using 5 waves, we have shown in vivo in the human heart a strong improvement of both the B-mode and the velocity estimation quality compared to one diverging wave imaging.…”

Analysis of 2-D Ultrasound Cardiac Strain Imaging Using Joint Probability Density Functions

“…The minimum frame rate needed to successfully recover cardiac strain has been studied by several groups (Langeland et al 2005; Chen et al 2009; Provost et al 2012). Chen et al (2009) conducted uniformly elastic tissue-mimicking (TM) phantom studies using a cyclic compression platform; their phantom experiment results obtained using a two-step one-dimensional (1D) cross-correlation method (Shi and Varghese 2007) showed that frame rates higher than 10 times the cyclic compression frequency was necessary to maintain high elastographic signal-to-noise ratio (SNR e ) levels (≈16dB) using radiofrequency (RF) signals.…”

“…However, they did not explore the impact of lower frame rates in their study (Langeland et al 2005). Recently, Provost et al (2012) proposed the use of a joint probability density function (pdf) map of the SNR e and strain probability distribution to analyze the frame rate problem for cardiac applications. They reported a sharp decline in the SNR e values in the joint pdf maps in the vicinity of 4% strain in their open-chest canine heart study using a phased array transducer operated at a 3.3 MHz center frequency (Provost et al 2012).…”

“…Recently, Provost et al (2012) proposed the use of a joint probability density function (pdf) map of the SNR e and strain probability distribution to analyze the frame rate problem for cardiac applications. They reported a sharp decline in the SNR e values in the joint pdf maps in the vicinity of 4% strain in their open-chest canine heart study using a phased array transducer operated at a 3.3 MHz center frequency (Provost et al 2012). Their results showed that a minimum frame rate of 350 Hz was needed to eliminate distortions in the strain probability distribution and to obtain a unimodal distribution in the joint pdf.…”

“…In this paper, we utilize the same joint pdf concept proposed by Provost et al (2012), to assess the frame rate requirement for cardiac strain imaging using a multi-level 2D cross-correlation algorithm previously developed in our laboratory (Shi and Varghese 2007). The joint pdf was first demonstrated for an inclusion phantom by Varghese and Ophir (Varghese and Ophir 1998) (see Figure 12 in Varghese and Ophir (1998)).…”