Automatic respiratory gating for contrast ultrasound evaluation of liver lesions

Quantification of Hepatocellular Carcinoma Vascular Dynamics With Contrast-Enhanced Ultrasound for LI-RADS Implementation

OBJECTIVE

The aim of this study is to describe a comprehensive contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to implement CEUS LI-RADS (Liver Imaging Reporting and Data System) in a quantifiable manner. The methods that are validated with a prospective single-center study aim to simplify CEUS LI-RADS evaluation, remove observer bias, and potentially improve the sensitivity of CEUS LI-RADS.

MATERIALS AND METHODS

This prospective single-center study enrolled patients with hepatocellular carcinoma (April 2021-June 2022; N = 31; mean age ± SD, 67 ± 6 years; 24 men/7 women). For each patient, at least 2 CEUS loops spanning over 5 minutes were collected for different lesion scan planes using an articulated arm to hold the transducer. Automatic respiratory gating and motion compensation algorithms removed errors due to breathing motion. The long axis of the lesion was measured in the contrast and fundamental images to capture nodule size. Parametric processing of time-intensity curve analysis on linearized data provided quantifiable information of the wash-in and washout dynamics via rise time ( RT ) and degree of washout ( DW ) parameters extracted from the time-intensity curve, respectively. A Welch t test was performed between lesion and parenchyma RT for each lesion to confirm statistically significant differences. P values for bootstrapped 95% confidence intervals of the relative degree of washout ( rDW ), ratio of DW between the lesion and surrounding parenchyma, were computed to quantify lesion washout. Coefficient of variation (COV) of RT , DW , and rDW was calculated for each patient between injections for both the lesion and surrounding parenchyma to gauge reproducibility of these metrics. Spearman rank correlation tests were performed among size, RT , DW , and rDW values to evaluate statistical dependence between the variables.

RESULTS

The mean ± SD lesion diameter was 23 ± 8 mm. The RT for all lesions, capturing arterial phase hyperenhancement, was shorter than that of surrounding liver parenchyma ( P < 0.05). All lesions also demonstrated significant ( P < 0.05) but variable levels of washout at both 2-minute and 5-minute time points, quantified in rDW . The COV of RT for the lesion and surrounding parenchyma were both 11%, and the COV of DW and rDW at 2 and 5 minutes ranged from 22% to 31%. Statistically significant relationships between lesion and parenchyma RT and between lesion RT and lesion DW at the 2- and 5-minute time points were found ( P < 0.05).

CONCLUSIONS

The imaging protocol and analysis method presented provide robust, quantitative metrics that describe the dynamic vascular patterns of LI-RADS 5 lesions classified as hepatocellular carcinomas. The RT of the bolus transit quantifies the arterial phase hyperenhancement, and the DW and rDW parameters quantify the washout from linearized CEUS intensity data. This unique methodology is able to implement the CEUS-LIRADS scheme in a quantifiable manner for the first time and remove its existing issues of currently being qualitative and suffering from subjective evaluations.

EFSUMB Technical Review - Update 2023: Dynamic Contrast-Enhanced Ultrasound (DCE-CEUS) for the Quantification of Tumor Perfusion

Dynamic contrast-enhanced ultrasound (DCE-US) is a technique to quantify tissue perfusion based on phase-specific enhancement after the injection of microbubble contrast agents for diagnostic ultrasound. The guidelines of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) published in 2004 and updated in 2008, 2011, and 2020 focused on the use of contrast-enhanced ultrasound (CEUS), including essential technical requirements, training, investigational procedures and steps, guidance regarding image interpretation, established and recommended clinical indications, and safety considerations. However, the quantification of phase-specific enhancement patterns acquired with ultrasound contrast agents (UCAs) is not discussed here. The purpose of this EFSUMB Technical Review is to further establish a basis for the standardization of DCE-US focusing on treatment monitoring in oncology. It provides some recommendations and descriptions as to how to quantify dynamic ultrasound contrast enhancement, and technical explanations for the analysis of time-intensity curves (TICs). This update of the 2012 EFSUMB introduction to DCE-US includes clinical aspects for data collection, analysis, and interpretation that have emerged from recent studies. The current study not only aims to support future work in this research field but also to facilitate a transition to clinical routine use of DCE-US.

An Automatic Detection of Liver Tumor from CT Abdominal Images—A Comparative Approach

The liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.

Irregular Respiratory Motion Compensation for Liver Contrast-Enhanced Ultrasound via Transport-Based Motion Estimation

Contrast-enhanced ultrasound (CEUS) imaging has been widely applied for the detection and characterization of focal liver lesions (FLLs), for its ability to visualize the blood flow in real time. However, cyclic liver motion poses a great challenge to the recovery of perfusion curves as well as quantitative kinetic parameters estimation. Recently, a few gating methods have been proposed to eliminate unexpected intensity fluctuations by the breathing phase estimation, with the assumption that each breathing phase corresponds to a specific lesion position strictly. While practical liver motion tends to be irregular due to changes in the patient's underlying physiologic status, that is, the same phase might not correspond to the same position. To tackle the challenge of motion irregularity, we present a novel motion estimation-based respiratory compensation method, named RCME, which first estimates salient motion information through the framework of optimal transport (OT) by jointly modeling pixel intensity as well as their locations and then employs sparse subspace clustering (SSC) to identify the subset of frames acquired at the same position. Our proposed method is evaluated on 15 clinical CEUS sequences in both qualitative and quantitative manners. Experimental results demonstrate good performance on irregular liver motion compensation.

Imaging Methods for Ultrasound Contrast Agents

Microbubble contrast agents were introduced more than 25 years ago with the objective of enhancing blood echoes and enabling diagnostic ultrasound to image the microcirculation. Cardiology and oncology waited anxiously for the fulfillment of that objective with one clinical application each: myocardial perfusion, tumor perfusion and angiogenesis imaging. What was necessary though at first was the scientific understanding of microbubble behavior in vivo and the development of imaging technology to deliver the original objective. And indeed, for more than 25 years bubble science and imaging technology have evolved methodically to deliver contrast-enhanced ultrasound. Realization of the basic bubbles properties, non-linear response and ultrasound-induced destruction, has led to a plethora of methods; algorithms and techniques for contrast-enhanced ultrasound (CEUS) and imaging modes such as harmonic imaging, harmonic power Doppler, pulse inversion, amplitude modulation, maximum intensity projection and many others were invented, developed and validated. Today, CEUS is used everywhere in the world with clinical indications both in cardiology and in radiology, and it continues to mature and evolve and has become a basic clinical tool that transforms diagnostic ultrasound into a functional imaging modality. In this review article, we present and explain in detail bubble imaging methods and associated artifacts, perfusion quantification approaches, and implementation considerations and regulatory aspects.

Respiratory motion correction for liver contrast-enhanced ultrasound by automatic selection of a reference image

PURPOSE

Respiratory motion correction is necessary for the quantitative analysis of liver contrast-enhanced ultrasound (CEUS) image sequences. Most respiratory motion correction methods are based on the dual mode of CEUS image sequences, including contrast and grayscale image sequences. Due to free-breathing motion, the acquired two-dimensional (2D) ultrasound cine might show the in-plane and out-of-plane motion of tumors. The registration of an entire 2D ultrasound contrast image sequence based on out-of-plane images is ineffective. For the respiratory motion correction of CEUS sequences, the reference image is usually considered the standard for the deletion of any out-of-plane images. Most methods used for the selection of the reference image are subjective in nature. Here, a quantitative selection method for an optimal reference image from CEUS image sequences in the B mode and contrast mode was explored.

METHODS

The original high-dimensional ultrasound grayscale image data were mapped into a two-dimensional space using Laplacian Eigenmaps (LE), and K-means clustering was adopted. The center image of the larger cluster with a near-peak contrast intensity was considered the optimal ultrasound reference image. In the ultrasound grayscale image sequence, the images with the maximum correlations to the reference image in the same time interval were selected as the corrected image sequence. The effectiveness of this proposed method was then validated on 18 CEUS cases of VX2 tumors in rabbit livers.

RESULTS

Correction smoothed the time-intensity curves (TICs) extracted from the region of interest of the CEUS image sequences. Before correction, the average of the total mean structural similarity (TMSSIM) and the average of the mean correlation coefficient (MCC) from the image sequences were 0.45 ± 0.11 and 0.67 ± 0.16, respectively, and after correction, the average TMSSIM and MCC increased (P < 0.001) by 31% to 0.59 ± 0.11 and by 21% to 0.81 ± 0.11, respectively. The average deviation value (DV) index of the TICs from the image sequences prior to correction was 92.16 ± 18.12, and correction reduced the average to 31.71 ± 7.31. The average TMSSIM and MCC values after correction using the mean frame of the reference image (MBMFRI) were clearly lower than those after correction using the proposed method (P < 0.001). Moreover, the average DV after correction using the MBMFRI was obviously higher than that after correction using the proposed method (P < 0.001).

CONCLUSIONS

The breathing frequency of rabbits is notably faster than that of human beings, but the proposed correction method could reduce the effect of the respiratory motion in the CEUS image sequences. The reference image was selected quantitatively, which could improve the accuracy of the quantitative analysis of rabbit liver CEUS sequences using the reference image method based on the current standard of manual selection and the MBMFRI. This easy-to-operate method can potentially be used in both animal studies and clinical applications.

In-vitro evaluation of accuracy of dynamic contrast-enhanced ultrasound (DCEUS)-based parametric perfusion imaging with respiratory motion-compensation

PURPOSE

The accuracy of multi-parametric perfusion imaging (PPI) based on dynamic contrast-enhanced ultrasound is disturbed by the respiratory motion in some cases, especially during characterizing hemodynamic features of abdominal tumor angiogenesis. This study aimed to effectively remove those disturbances on PPI and evaluate its accuracy.

METHOD

The respiratory motion-compensation (rMoCo) strategy in PPI was modified by employing non-negative matrix factorization combined with phase-by-phase compensation. According to the known and controllable ground truths in in-vitro perfusion experiments, the accuracy of the modified rMoCo strategy was further evaluated from multiple perspectives in a simulated dual-vessel flow phantom.

RESULTS

Compared with that of PPIs without rMoCo, the mean correlation coefficient between six PPIs with rMoCo and the corresponding static PPIs was up to 0.98 ± 0.01 and improved by 0.17 ± 0.04 (P < 0.05). The estimated error of vascular diameter decreased from 87.85% (P < 0.05) to 7.25% (P < 0.05) after rMoCo. PPIs with rMoCo were significantly consistent with static PPIs without respiratory motion disturbances.

CONCLUSIONS

These quantitative results illustrated the disturbances induced by respiratory motion were effectively removed and the accuracy of PPIs was significantly improved. The partial parabolic and bimodal hemodynamic characteristics and the anatomical structures and sizes were accurately quantified and depicted by PPIs with rMoCo. The modified method can benefit physicians in providing accurate diagnoses and in developing appropriate therapeutic strategies for abdominal diseases.

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

Confidence in the accuracy of dynamic contrast enhanced ultrasound (DCEUS) quantification parameters is imperative for the correct diagnosis of liver lesion perfusion characteristics. An important source of uncertainty in liver DCEUS acquisitions is artifacts introduced by respiratory motion. The objective of this study is to construct a respiratory motion simulation model (RMSM) of dual contrast imaging mode acquisitions of liver lesions in order to evaluate an algorithm for automatic respiratory gating (ARG). The respiratory kinetics as well as the perfusion models of the liver lesion and parenchyma used by the RMSM were solely derived from clinical data. The quality of fit (of the DCEUS data onto the bolus kinetics model) depends on the respiration amplitude. Similar trends in terms of quality of fit as a function of respiration amplitude were observed from RMSM and clinical data. The errors introduced on the DCEUS quantification under the influence of respiration were evaluated. The RMSM revealed that the error in the liver lesion DCEUS quantification parameters significantly decreased (p < 0.001) from a maximum of 32.3% to 6.2% when ARG was used. The use of RMSM clearly demonstrates the capability of the ARG algorithm in significantly reducing errors introduced from both in-plane and out-of-plane respiratory motion.

Automatic motion estimation using flow parameters for dynamic contrast-enhanced ultrasound

Dynamic contrast-enhanced ultrasound (DCE-US) sequences are subject to motion which can disturb functional flow quantification. This can make estimated parameters more variable or unreliable. Methods that compensate for motion are therefore desirable. The most commonly used motion correction techniques in DCE-US register the images in the sequence with respect to a user-selected reference image. However, this image may not include all features that are representative of the whole sequence. Moreover, image-based registration neglects pertinent, functional-flow information contained in the DCE-US sequence. An operator-free method is proposed that combines the motion estimation and flow-parameter quantification (M/Q method) in a single mathematical framework. This method is based on a realistic multiplicative model of the DCE-US noise. By computing likelihood in this model, motion and flow parameters are both estimated iteratively. First, the maximization is accomplished by estimating functional and motion parameters. Then, a final registration based on a non-parametric temporal smoothing of the sequence is performed. This method is compared to a conventional (mutual information) registration method where all the images of the sequence are registered with respect to a reference image chosen by an expert. The two methods are evaluated on simulated sequences and DCE-US sequences acquired in patients (N = 15). The M/Q method demonstrates significantly (p < 0.05) lower Dice coefficients and Hausdorff distance than the conventional method on the simulated data sets. On the in vivo sequences analysed, the M/Q methods outperformed the conventional method in terms of mean Dice and Hausdorff distance on 80% of the sequences, and in terms of standard deviation of Dice and Hausdorff distance on 87% of the sequences.