An automatic respiratory gating method for the improvement of microcirculation evaluation: application to contrast-enhanced ultrasound studies of focal liver lesions

Abstract

Contrast-enhanced ultrasound (CEUS), with the recent development of both contrast-specific imaging modalities and microbubble-based contrast agents, allows noninvasive quantification of microcirculation in vivo. Nevertheless, functional parameters obtained by modeling contrast uptake kinetics could be impaired by respiratory motion. Accordingly, we developed an automatic respiratory gating method and tested it on 35 CEUS hepatic datasets with focal lesions. Each dataset included fundamental mode and cadence contrast pulse sequencing (CPS) mode sequences acquired simultaneously. The developed method consisted in (1) the estimation of the respiratory kinetics as a linear combination of the first components provided by a principal components analysis constrained by a prior knowledge on the respiratory rate in the frequency domain, (2) the automated generation of two respiratory-gated subsequences from the CPS mode sequence by detecting end-of-inspiration and end-of-expiration phases from the respiratory kinetics. The fundamental mode enabled a more reliable estimation of the respiratory kinetics than the CPS mode. The k-means algorithm was applied on both the original CPS mode sequences and the respiratory-gated subsequences resulting in clustering maps and associated mean kinetics. Our respiratory gating process allowed better superimposition of manually drawn lesion contours on k-means clustering maps as well as substantial improvement of the quality of contrast uptake kinetics. While the quality of maps and kinetics was satisfactory in only 11/35 datasets before gating, it was satisfactory in 34/35 datasets after gating. Moreover, noise amplitude estimated within the delineated lesions was reduced from 62 ± 21 to 40 ± 10 (p < 0.01) after gating. These findings were supported by the low residual horizontal (0.44 ± 0.29 mm) and vertical (0.15 ± 0.16 mm) shifts found during manual motion correction of each respiratory-gated subsequence. The developed technique could be used as a basis for accurate quantification of perfusion parameters for the evaluation and follow-up of patients under antiangiogenic therapies.

Evaluation of the Accuracy of Liver Lesion DCEUS Quantification With Respiratory Gating

“…That study concluded that image registration based algorithms for respiratory motion compensation were inadequate in removing out-of-plane motion from liver DCEUS acquisitions. The independent component analysis (ICA) method proposed by Renault et al and the principal component analysis (PCA) examined by Mule et al [19] have been shown to remove both in-plane and out-of-plane motion from DCEUS loops. However only the end phases of the respiratory cycle can be extracted using ICA and PCA analysis, and since the acquisition is two dimensional there is the uncertainty whether a portion or any part of the lesion will be present on the extracted frames.…”

“…Disadvantages of these methods include the need for user intervention [20], [21], [23], extraction of only the end-phases of the respiration cycle [19], [20], and speed of execution [23].…”

Improvement of the accuracy of liver lesion DCEUS quantification with the use of automatic respiratory gating

“…Studies on methods for automatic respiratory motion correction have been published in the literature [11][12][13][14]21]. A technique based on independent component analysis (ICA) was introduced by Renault et al [12] where the respiratory kinetics curve could be manually extracted from the components derived from the ICA.…”

“…Frames of the loop that were part of the end phases of the respiratory cycle could be isolated by using a threshold on the respiratory kinetics curve. Furthermore, Mule et al [11] developed a fully automatic algorithm that utilizes principal component analysis (PCA) to perform respiratory gating and extract frames that belong to the end phases of the respiratory cycle. Although the ICA and PCA algorithms proposed can correct for both in-plane and out-of-plane respiratory motion, they are limited in extracting only the end phases of the respiratory cycle.…”

“…Respiratory gating algorithms have been suggested in the literature as a possible solution to limiting the effect of breathing motion [11][12][13][14]. Here we evaluated an algorithm for automatic respiratory gating (ARG) in a clinical study for efficiency in reducing the effect of respiratory motion from DCEUS loops [15].…”