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

A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow

OBJECTIVE

The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies.

MATERIALS AND METHODS

This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time ( RT ), mean transit time ( MTT ), peak intensity ( PI ), and area under the curve ( AUC ), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination ( R2 ) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively.

RESULTS

Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT .

CONCLUSIONS

The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit parameters in CEUS data that were not available before. With vital features such as probe stabilization ideally performed with an articulated arm and an automated respiratory gating algorithm, we were able to achieve interinjection repeatability of blood flow parameters that are comparable or surpass levels currently established for clinical 2D CEUS scans. Similar values and interinjection repeatability were achieved between calculations from a fitted curve or directly from the data. This demonstrated not only the strength of the protocol to generate TICs with minimal noise, but also suggests that curve fitting might be avoided for a more standardized approach. Utilizing the imaging protocol and analysis method defined in this study, we aim for this methodology to potentially assist clinicians to assess true perfusion changes for treatment monitoring with CEUS in longitudinal studies.

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.

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.

A Comprehensive Motion Compensation Method for In-Plane and Out-of-Plane Motion in Dynamic Contrast-Enhanced Ultrasound of Focal Liver Lesions

Contrast-enhanced ultrasound (CEUS) acquisitions of focal liver lesions are affected by motion, which has an impact on contrast signal quantification. We therefore developed and tested, in a large patient cohort, a motion compensation algorithm called the Iterative Local Search Algorithm (ILSA), which can correct for both periodic and non-periodic in-plane motion and can reject frames with out-of-plane motion. CEUS cines of 183 focal liver lesions in 155 patients from three hospitals were used to develop and test ILSA. Performance was evaluated through quantitative metrics, including the root mean square error and R2 in fitting time-intensity curves and standard deviation value of B-mode intensities, computed across cine frames), and qualitative evaluation, including B-mode mean intensity projection images and parametric perfusion imaging. The median root mean square error significantly decreased from 0.032 to 0.024 (p < 0.001). Median R2 significantly increased from 0.88 to 0.93 (p < 0.001). The median standard deviation value of B-mode intensities significantly decreased from 6.2 to 5.0 (p < 0.001). B-Mode mean intensity projection images revealed improved spatial resolution. Parametric perfusion imaging also exhibited improved spatial detail and better differentiation between lesion and background liver parenchyma. ILSA can compensate for all types of motion encountered during liver CEUS, potentially improving contrast signal quantification of focal liver lesions.