Transcutaneous vs. intraoperative quantitative ultrasound for staging bovine hepatic steatosis.

The aim of this study was to test the hypothesis that quantitative analysis of transcutaneous (Transc) ultrasound (US) images can predict the liver fat content with similar accuracy and precision as using intraoperative (Intraop) US. The second goal was to investigate if a tissue mimicking phantom (TMP) might be used as reference for automatic gain compensation (AGC) vs. depth instead of using the data of a set of cows without hepatic alterations. A study was performed in post partum dairy cows (N = 151), as an animal model of human nonalcoholic fatty liver disease (NAFLD), to test these hypotheses. Five Transc and five Intraop US liver images were acquired in each animal and a liver biopsy was taken. In liver tissue samples, triacylglycerol (TAG) content was measured by biochemical analysis and hepatic alterations, other than hepatic steatosis, were excluded by clinical examination. Several preprocessing steps were performed before the ultrasound tissue characteristics (UTC) parameters of B-mode images were derived. Stepwise multiple linear regression analysis was performed on a training set (N = 76) and the results were used on the test group (N = 75) to predict the TAG content in the liver. In all cases, the residual attenuation coefficient (ResAtt) was the only selected parameter. Receiver operating characteristics (ROC) analysis was applied to assess the performance and area under the curve (AUC) of predicting TAG and to compare the sensitivity and specificity of the methods used. High ROC values for AUC (95%), sensitivity (87%) and specificity (83%) for both Intraop and Transc applications with control group as well as with phantom-based AGC were obtained. Consequently, it can be concluded that Transc results are equivalent to Intraop results. Furthermore, equivalent ROC values, when using TMP AGC, indicates the potential use of TMP-based corrections instead of normal group-based corrections. The high predictive values indicate that noninvasive quantitative US has a great potential for staging and screening on hepatic steatosis in cows.

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used to predict TAG level in the liver. Receiver-operating-characteristics (ROC) analysis was applied to assess the performance and area under the curve (AUC) of predicting TAG and to compare the sensitivity and specificity of the methods. Best speckle-size estimates and overall performance (R2 = 0.71, AUC = 0.94) were achieved by using an SNR-based adaptive automatic-segmentation method (used TAG threshold: 50 mg/g liver wet weight). Automatic segmentation is thus feasible and profitable.

Computer-aided B-mode ultrasound diagnosis of hepatic steatosis: a feasibility study.

Fatty liver (steatosis) occurs in obese patients, among others, and is related to the development of diabetes type-2. Timely diagnosis of steatosis is therefore of great importance. Steatosis is also the most common liver disease of high-yielding dairy cattle during early lactation. This makes it a suitable animal model for studying liver steatosis. Furthermore, reference of derived ultrasound parameters against a "gold standard" is possible in cattle by taking a liver biopsy for the assessment of fat concentration. The authors undertook this pilot study to investigate the hypothesis that quantitative, computer-aided B-mode ultrasound enables the noninvasive detection of hepatic steatosis. Echographic images were obtained postpartum from dairy cows (n = 12) in transcutaneous and direct (intraoperative) applications using a convex array transducer at 4.2 MHz. During surgery, a biopsy was taken from the caudate lobe to assess the liver fat content (fat score). A custom-designed software package for computer-aided ultrasound diagnosis (CAUS) was developed. After linearizing the post-processing look-up-table (LUT), the image gray levels were transferred into echo levels in decibels relative to the mean echo level in a tissue-mimicking phantom. The quantitative comparison of transcutaneous and intraoperative images enabled the correction for the attenuation effect of skin and subcutaneous fat layer on the mean echo level in the liver, as well as for the effects of the beam formation and attenuation of liver tissue on the echo level vs. depth. The residual attenuation coefficient (dB/cm) in fatty liver vs. normal liver was estimated and compensated for. Finally, echo level was estimated relative to the phantom used for calibration, and echo texture was characterized by the mean axial and lateral speckle size within the regions of interest. In the no fat/low fat group (n = 5) skin plus fat layer attenuation was 3.4 dB/cm. A correlation of skin layer thickness vs. fat score of r = 0.48 was found. The mean transcutaneous liver tissue echo level correlated well with fat score: r = 0.80. A residual liver attenuation coefficient of 0.76 dB/cm and 1.19 dB/cm was found in medium and high fat liver, respectively. In transcutaneous images, correlation of residual attenuation coefficient with fat score was r = 0.69. Axial and lateral speckle sizes were on the order of 0.2 and 1.0 cm, respectively, and no correlation was found with liver fat content. Results for transcutaneous and intraoperative images were similar. The authors conclude that this pilot study shows the feasibility of calibrated, computer-aided ultrasound for noninvasively diagnosing, possibly even screening, steatosis of the liver.