Image-based cardiac phase retrieval in intravascular ultrasound sequences

Abstract

Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals.

The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries

“…In this chapter we also showed the usefulness of this estimation for retrieving cardiac phase and we compared the method proposed to other vessel appearance-based models. There are two main advantages in using a dynamic quantity instead of the usual signals computed from image grey-level evolution (Barajas et al, 2007;Hernàndez-Sabaté et al, 2011;Matsumoto et al, 2008;Nadkarni et al, 2005). Firstly, since θ c does not include non-cardiac phenomena (such as breathing) it requires less specific tuning of the band-pass filtering.…”

“…In order to compare the ranges of the approach proposed in this chapter to the ones presented in (Hernàndez-Sabaté et al, 2011) 4.2071 ± 1.8385 0.1402 ± 0.0613 0.0701 ± 0.0306 Table 4. Average Errors of the best set of filters for the image-grey level evolution approach Figure 17 shows the performance of our method for the Butterworth filtering in 4 large longitudinal cuts.…”

“…The significance level for computing and comparing ranks in the Nemenyi test is 0.1. Finally, we have checked the benefits of using dynamic quantities by comparing results to the ones obtained using gray-intensity cardiac signals (Hernàndez-Sabaté et al, 2011).…”

“…As in (Hernàndez-Sabaté et al, 2011), the set of filters scanned, G i for gaussian filters and B i for Butterworth ones is the following. As well, we have added the results of the angle output without filtering.…”

Automated detection of cardiac phase from intracoronary ultrasound image sequences

“…These global similarity measures result in the sensitivity of cardiac phase detection to background noises, useless textures, and non-cardiac dynamic phenomena. The evolution of tissue density of mass is explored in [14]. The validation results show that a local approach can effectively reduce the impact of texture and motionless regions.…”