Estimation of intra-operator variability in perfusion parameter measurements using DCE-US

AIM:To investigate intra-operator variability of semiquantitative perfusion parameters using dynamic contrast-enhanced ultrasonography(DCE-US),following bolus injections of SonoVue.METHODS:The in vitro experiments were conducted using three in-house sets up based on pumping a fluid through a phantom placed in a water tank.In the in vivo experiments,B16F10 melanoma cells were xenografted to five nude mice.Both in vitro and in vivo,images were acquired following bolus injections of the ultrasound contrast agent SonoVue(Bracco,Milan,Italy) and using a Toshiba Aplio ultrasound scanner connected to a 2.9-5.8 MHz linear transducer(PZT,PLT 604AT probe)(Toshiba,Japan) allowing harmonic imaging("Vascular Recognition Imaging") involving linear raw data.A mathematical model based on the dye-dilution theory was developed by the Gustave Roussy Institute,Villejuif,France and used to evaluate seven perfusion parameters from time-intensity curves.Intra-operator variability analyses were based on determining perfusion parameter coefficients of variation(CV).RESULTS:In vitro,different volumes of SonoVue were tested with the three phantoms:intra-operator variability was found to range from 2.33% to 23.72%.In vivo,experiments were performed on tumor tissues and perfusion parameters exhibited values ranging from 1.48% to 29.97%.In addition,the area under the curve(AUC) and the area under the wash-out(AUWO) were two of the parameters of great interest since throughout in vitro and in vivo experiments their variability was lower than 15.79%.CONCLUSION:AUC and AUWO appear to be the most reliable parameters for assessing tumor perfusion using DCE-US as they exhibited the lowest CV values.

Impact of the arterial input function on microvascularization parameter measurements using dynamic contrast-enhanced ultrasonography

AIM: To evaluate the sources of variation influencing the microvascularization parameters measured by dynamic contrast-enhanced ultrasonography (DCE-US). METHODS: Firstly, we evaluated, in vitro , the impact of the manual repositioning of the ultrasound probe and the variations in flow rates. Experiments were conducted using a custom-made phantom setup simulating a tumor and its associated arterial input. Secondly, we evaluated, in vivo , the impact of multiple contrast agent injections and of examination day, as well as the influence of the size of region of interest (ROI) associated with the arterial input function (AIF). Experiments were conducted on xenografted B16F10 female nude mice. For all of the experiments, an ultrasound scanner along with a linear transducer was used to perform pulse inversion imaging based on linear raw data throughout the experiments. Semi-quantitative and quantitative analyses were performed using two signal-processing methods. RESULTS:In vitro , no microvascularization parameters, whether semi-quantitative or quantitative, were significantly correlated (P values from 0.059 to 0.860) with the repositioning of the probe. In addition, all semiquantitative microvascularization parameters were correlated with the flow variation while only one quantitative parameter, the tumor blood flow, exhibited P value lower than 0.05 (P = 0.004). In vivo , multiple contrast agent injections had no significant impact (P values from 0.060 to 0.885) on microvascularization parameters. In addition, it was demonstrated that semi-quantitative microvascularization parameters were correlated with the tumor growth while among the quantitative parameters, only the tissue blood flow exhibited P value lower than 0.05 (P = 0.015). Based on these results, it was demonstrated that the ROI size of the AIF had significant influence on microvascularization parameters: in the context of larger arterial ROI (from 1.17 ± 0.6 mm 3 to 3.65 ± 0.3 mm 3 ), tumor blood flow and tumor blood volume were correlated with the tumor growth, exhibiting P values lower than 0.001. CONCLUSION: AIF selection is an essential aspect of the deconvolution process to validate the quantitative DCE-US method.