Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound

Quantitative perfusion analysis of malignant liver tumors: dynamic computed tomography and contrast-enhanced ultrasound

OBJECTIVE

To prospectively analyze the correlation between quantitative parameters of perfusion derived from dynamic contrast-enhanced CT (DCE-CT) and contrast-enhanced ultrasound (DCE-US) in patients with malignant liver tumors.

MATERIALS AND METHODS

Thirty patients (mean age: 59.4 ± 12.3 years) with primary malignant liver tumors or hepatic metastases of various origin underwent DCE-CT (4D spiral mode, scan range, 14.8 cm; 15 scans; cycle time, 3 seconds) and DCE-US (low mechanical index, <0.1, 2.4 mL microbubbles). DCE-CT and DCE-US images were evaluated by 2 radiologists regarding quantitative perfusion parameters including arterial liver perfusion (ALP), portal-venous perfusion (PVP), and total perfusion (P = ALP + PVP) from DCE-CT, as well as blood inflow velocity (B) and the normalized slope within the calculation range (CVan) from DCE-US.

RESULTS

Quantitative assessment was possible with DCE-CT in 12/30 (40%) patients before and in all patients after automated motion correction. With DCE-US, quantitative assessment could not be performed in 9/30 (30.0%) patients due to respiratory motion. Interreader agreements for quantitative perfusion analysis were good with DCE-CT (r = 0.640-0.892, each P < 0.001) and DCE-US (r = 0.761-0.909, each P < 0.001). Moderate significant correlations were found between the perfusion parameters from DCE-CT (P, ALP) and DCE-US (B, CVan) (r = 0.446-0.621, each P < 0.05). No significant correlations were found between PVP from CT and perfusion parameters from DCE-US (B, CVan; each P = nonsignificant).

CONCLUSIONS

Quantitative evaluation of DCE-CT data was feasible in all patients after automated motion correction, whereas DCE-US data could not be quantitatively evaluated in 30% of patients due to respiratory motion and lack of motion correction software. Quantitative arterial perfusion analysis showed moderate significant correlations for blood flow parameters among modalities.

Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics

Contrast-enhanced ultrasound (CEUS) enables highly specific time-resolved imaging of vasculature by intravenous injection of ∼2 μm gas filled microbubbles. To develop a quantitative automated diagnosis of breast tumors with CEUS, breast tumors were induced in rats by administration of N-ethyl-N-nitrosourea. A bolus injection of microbubbles was administered and CEUS videos of each tumor were acquired for at least 3 min. The time-intensity curve of each pixel within a region of interest (ROI) was analyzed to measure kinetic parameters associated with the wash-in, peak enhancement, and wash-out phases of microbubble bolus injections since it was expected that the aberrant vascularity of malignant tumors will result in faster and more diverse perfusion kinetics versus those of benign lesions. Parameters were classified using linear discriminant analysis to differentiate between benign and malignant tumors and improve diagnostic accuracy. Preliminary results with a small dataset (10 tumors, 19 videos) show 100% accuracy with fivefold cross-validation testing using as few as two choice variables for training and validation. Several of the parameters which provided the best differentiation between malignant and benign tumors employed comparative analysis of all the pixels in the ROI including enhancement coverage, fractional enhancement coverage times, and the standard deviation of the envelope curve difference normalized to the mean of the peak frame. Analysis of combinations of five variables demonstrated that pixel-by-pixel analysis produced the most robust information for tumor diagnostics and achieved 5 times greater separation of benign and malignant cases than ROI-based analysis.

Quantitative contrast-enhanced ultrasound imaging: a review of sources of variability

Ultrasound provides a valuable tool for medical diagnosis offering real-time imaging with excellent spatial resolution and low cost. The advent of microbubble contrast agents has provided the additional ability to obtain essential quantitative information relating to tissue vascularity, tissue perfusion and even endothelial wall function. This technique has shown great promise for diagnosis and monitoring in a wide range of clinical conditions such as cardiovascular diseases and cancer, with considerable potential benefits in terms of patient care. A key challenge of this technique, however, is the existence of significant variations in the imaging results, and the lack of understanding regarding their origin. The aim of this paper is to review the potential sources of variability in the quantification of tissue perfusion based on microbubble contrast-enhanced ultrasound images. These are divided into the following three categories: (i) factors relating to the scanner setting, which include transmission power, transmission focal depth, dynamic range, signal gain and transmission frequency, (ii) factors relating to the patient, which include body physical differences, physiological interaction of body with bubbles, propagation and attenuation through tissue, and tissue motion, and (iii) factors relating to the microbubbles, which include the type of bubbles and their stability, preparation and injection and dosage. It has been shown that the factors in all the three categories can significantly affect the imaging results and contribute to the variations observed. How these factors influence quantitative imaging is explained and possible methods for reducing such variations are discussed.

Quantification of enhancement of focal liver lesions during contrast-enhanced ultrasound (CEUS). Analysis of ten selected frames is more simple but as reliable as the analysis of the entire loop for most parameters

The aim of the study was to evaluate the reliability of the analysis of only 10 frames rather than of a whole clip in performing quantitative assessment of tumor enhancement of focal liver lesions (FLLs) following ultrasound contrast injection. Contrast-enhanced ultrasonography (CEUS) examinations of 31 FLLs (median diameter: 30mm) were performed. All clips were analyzed and quantified with an early prototype of the SonoLiver software (TomTec GmbH, Munich and Bracco Research SA, Geneva), first evaluating the entire clip then selecting only 10 frames at different time intervals. Enhancement measurements obtained from the analysis of the entire clip or of only 10 frames were closely correlated (r=0.931 and p<0.0001 for Area Under the Curve; r=0.944 and p<0.0001 for Perfusion Index). In conclusion, enhancement quantification of FLLs can be reliably obtained from only 10 frames, rather than the entire clip, at least for most parameters, making such procedure easier for potential routine use.

Nonlinear contrast imaging with an array-based micro-ultrasound system

The main goal of this study was to determine the optimal strategy for a real-time nonlinear contrast mode for small-animal imaging at high frequencies, on a new array-based micro-ultrasound system. Previously reported contrast imaging at frequencies above 15 MHz has primarily relied on subtraction schemes involving B-mode image data. These approaches provide insufficient contrast to tissue ratios under many imaging conditions. In this work, pulse inversion, amplitude modulation and combinations of these were systematically investigated for the detection of nonlinear fundamental and subharmonic signal components to maximize contrast-to-tissue ratio (CTR) in the 18-24 MHz range. From in vitro and in vivo measurements, nonlinear fundamental detection with amplitude modulation provided optimal results, allowing an improvement in CTR of 13 dB compared with fundamental imaging. Based on this detection scheme, in vivo parametric images of murine kidneys were generated using sequences of nonlinear contrast images after intravenous bolus injections of microbubble suspensions. Initial parametric images of peak enhancement (PE), wash-in rate (WiR) and rise time (RT) are presented. The parametric images are indicative of blood perfusion kinetics, which, in the context of preclinical imaging with small animals, are anticipated to provide valuable insights into the progression of human disease models, where blood perfusion plays a critical role in either the diagnosis or treatment of the disease.