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

Abstract

The differentiation between benign and malignant focal liver lesions plays an important role in diagnosis of liver disease and therapeutic planning of local or general disease. This differentiation, based on characterization, relies on the observation of the dynamic vascular patterns (DVP) of lesions with respect to adjacent parenchyma, and may be assessed during contrast-enhanced ultrasound imaging after a bolus injection. For instance, hemangiomas (i.e., benign lesions) exhibit hyper-enhanced signatures over time, whereas metastases (i.e., malignant lesions) frequently present hyperenhanced foci during the arterial phase and always become hypo-enhanced afterwards. The objective of this work was to develop a new parametric imaging technique, aimed at mapping the DVP signatures into a single image called a DVP parametric image, conceived as a diagnostic aid tool for characterizing lesion types. The methodology consisted in processing a time sequence of images (DICOM video data) using four consecutive steps: (1) pre-processing combining image motion correction and linearization to derive an echo-power signal, in each pixel, proportional to local contrast agent concentration over time; (2) signal modeling, by means of a curve-fitting optimization, to compute a difference signal in each pixel, as the subtraction of adjacent parenchyma kinetic from the echopower signal; (3) classification of difference signals; and (4) parametric image rendering to represent classified pixels as a support for diagnosis. DVP parametric imaging was the object of a clinical assessment on a total of 146 lesions, imaged using different medical ultrasound systems. The resulting sensitivity and specificity were 97% and 91%, respectively, which compare favorably with scores of 81 to 95% and 80 to 95% reported in medical literature for sensitivity and specificity, respectively.

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

“…can be derived. One can process the images pixel by pixel in this way and generate a map for each of the indices [13,14]. It should be noted that amplitude-derived features are more likely to be affected by system changes than temporally derived features.…”

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

“…19,20 Previous P Â P based reports handled the problem of noise by either discarding misbehaving pixels, 34 fitting their data to simple mathematical models, 24,25 or by employing variables that were relatively insensitive to fluctuations within the signal. 27 In this report, noise was handled by two key preprocessing steps: (1) optimal selection of the reference frame independently for each video for accurate motion correction and (2) using noise filters optimized for each parameter.…”

“…These observations were similar to those of Rognin et al where malignant focal liver lesions were found to initially hyperenhance prior to becoming hypoenhanced in the later portal-venous phase. 27 However, Zhao et al found that contrast medium persisted in the late phase in malignant human breast tumors and washed out in benign tumors. 42 Although the animal model employed here was a breast tumor model, the characteristic enhancement pattern did not match that of its human counterpart.…”

“…Although these techniques avoid the difficulties associated with bolus injections, tumor wash-in and wash-out characteristics cannot be observed with these methods, and as other researchers have shown, these characteristics can be powerful for tumor differentiation. 27,42,46 …”

“…26 Comparing the relative enhancement patterns of focal liver lesions to normal liver tissue, Rognin et al were able to improve a clinician's diagnostic accuracy to 97% sensitivity and 91% specificity. 27 In this study, CEUS time-intensity curves were analyzed on a pixel-by-pixel basis to measure many hemodynamic parameters associated with the wash-in, peak enhancement, and wash-out phases of the microbubble bolus injection to detect the aberrant functionality of tumor vasculature. Linear discriminant analysis (LDA) was employed to semiautomatically classify CEUS videos as benign or malignant on the basis of these P Â P TIC measurements.…”