Quantitative imaging of ultrasound backscattered signals with information entropy for bone microstructure characterization.

Osteoporosis is a critical problem during aging. Ultrasound signals backscattered from bone contain information associated with microstructures. This study proposed using entropy imaging to collect the information in bone microstructures as a possible solution for ultrasound bone tissue characterization. Bone phantoms with different pounds per cubic foot (PCF) were used for ultrasound scanning by using single-element transducers of 1 (nonfocused) and 3.5 MHz (nonfocused and focused). Clinical measurements were also performed on lumbar vertebrae (L3 spinal segment) in participants with different ages (n = 34) and postmenopausal women with low or moderate-to-high risk of osteoporosis (n = 50; identified using the Osteoporosis Self-Assessment Tool for Taiwan). The signals backscattered from the bone phantoms and subjects were acquired for ultrasound entropy imaging by using sliding window processing. The independent t-test, one-way analysis of variance, Spearman correlation coefficient rs, and the receiver operating characteristic (ROC) curve were used for statistical analysis. The results indicated that ultrasound entropy imaging revealed changes in bone microstructures. Using the 3.5-MHz focused ultrasound, small-window entropy imaging (side length: one pulse length of the transducer) was found to have high performance and sensitivity in detecting variation among the PCFs (rs = - 0.83; p < 0.05). Small-window entropy imaging also performed well in discriminating young and old participants (p < 0.05) and postmenopausal women with low versus moderate-to-high osteoporosis risk (the area under the ROC curve = 0.80; cut-off value = 2.65; accuracy = 86.00%; sensitivity = 71.43%; specificity = 88.37%). Ultrasound small-window entropy imaging has great potential in bone tissue characterization and osteoporosis assessment.

Ultrasound Detection of Liver Fibrosis in Individuals with Hepatic Steatosis Using the Homodyned K Distribution.

Acoustic structure quantification (ASQ) based on the analysis of ultrasound backscattered statistics has been reported to detect liver fibrosis without significant hepatic steatosis. This study proposed using ultrasound parametric imaging based on the parameter α of the homodyned K (HK) distribution for staging liver fibrosis in patients with significant hepatic steatosis. Raw ultrasound image data were acquired from patients (n = 237) to construct B-mode and HK α parametric images, which were compared with the focal disturbance (FD) ratio obtained from ASQ on the basis of histologic evidence (METAVIR fibrosis score and hepatic steatosis severity). The data were divided into group I (n = 173; normal to mild hepatic steatosis) and group II (n = 64; with moderate to severe hepatic steatosis) for statistical analysis through one-way analysis of variance and receiver operating characteristic (ROC) curve analysis. The results showed that the HK α parameter monotonically decreased as the liver fibrosis stage increased (p < .05); concurrently, the FD ratio increased (p < .05). For group I, the areas under the ROC (AUROCs) obtained using the FD ratio and the α parameter (AUROCFD and AUROCα) were, respectively, 0.56 and 0.55, 0.68 and 0.68, 0.64 and 0.64 and 0.62 and 0.62 for diagnosing liver fibrosis ≥F1, ≥F2, ≥F3 and ≥F4. The values of AUROCFD and AUROCα for group II were, respectively, 0.88 and 0.91, 0.81 and 0.81, 0.77 and 0.76 and 0.78 and 0.73 for diagnosing liver fibrosis ≥F1, ≥F2, ≥F3 and ≥F4. As opposed to previous studies, ASQ was found to fail in characterizing liver fibrosis in group I; however, it was workable for identifying liver fibrosis in patients with significant hepatic steatosis (group II). Compared with ASQ, HK imaging provided improved diagnostic performance in the early detection of liver fibrosis coexisting with moderate to severe hepatic steatosis. Ultrasound HK imaging is recommended as a strategy to evaluate early fibrosis risk in patients with significant hepatic steatosis.

Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis.

Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.

Frequency-domain CBE imaging for ultrasound localization of the HIFU focal spot: a feasibility study.

High-intensity focused ultrasound (HIFU) is a well-accepted tool for noninvasive thermal therapy. To control the quality of HIFU treatment, the focal spot generated in tissues must be localized. Ultrasound imaging can monitor heated regions; in particular, the change in backscattered energy (CBE) allows parametric imaging to visualize thermal information in the tissue. Conventional CBE imaging constructed in the spatial domain may be easily affected by noises when the HIFU focal spot is visualized. This study proposes frequency-domain CBE imaging to improve noise tolerance and image contrast in HIFU focal spot monitoring. Phantom experiments were performed in a temperature-controlled environment. HIFU of 2.12 MHz was applied to the phantoms, during which a clinical scanner equipped with a 3-MHz convex array transducer was used to collect raw image data consisting of backscattered signals for B-mode, spatial-, and frequency-domain CBE imaging. Concurrently, temperature changes were measured at the focal spot using a thermocouple for comparison with CBE values by calculating the correlation coefficient r. To further analyze CBE image contrast levels, a contrast factor was introduced, and an independent t-test was performed to calculate the probability value p. Experimental results showed that frequency-domain CBE imaging performed well in thermal distribution visualization, enabling quantitative detection of temperature changes. The CBE value calculated in the frequency domain also correlated strongly with that obtained using the conventional spatial-domain approach (r = 0.97). In particular, compared with the image obtained through the conventional method, the contrast of the CBE image obtained using the method based on frequency-domain analysis increased by 2.5-fold (4 dB; p < 0.05). Frequency-domain computations may constitute a new strategy when ultrasound CBE imaging is used to localize the focal spot in HIFU treatment planning.

Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: An animal study.

Ultrasound is the first-line tool for screening hepatic steatosis. Statistical distributions can be used to model the backscattered signals for liver characterization. The Nakagami distribution is the most frequently adopted model; however, the homodyned K (HK) distribution has received attention due to its link to physical meaning and improved parameter estimation through X- and U-statistics (termed "XU"). To assess hepatic steatosis, we proposed HK parametric imaging based on the α parameter (a measure of the number of scatterers per resolution cell) calculated using the XU estimator. Using a commercial system equipped with a 7-MHz linear array transducer, phantom experiments were performed to suggest an appropriate window size for α imaging using the sliding window technique, which was further applied to measuring the livers of rats (n = 66) with hepatic steatosis induced by feeding the rats a methionine- and choline-deficient diet. The relationships between the α parameter, the stage of hepatic steatosis, and histological features were verified by the correlation coefficient r, one-way analysis of variance, and regression analysis. The phantom results showed that the window side length corresponding to five times the pulse length supported a reliable α imaging. The α parameter showed a promising performance for grading hepatic steatosis (p < 0.05; r2 = 0.68). Compared with conventional Nakagami imaging, α parametric imaging provided significant information associated with fat droplet size (p < 0.05; r2 = 0.53), enabling further analysis and evaluation of severe hepatic steatosis.

Considerations of Ultrasound Scanning Approaches in Non-alcoholic Fatty Liver Disease Assessment through Acoustic Structure Quantification.

Non-alcoholic fatty liver disease (NAFLD) is a risk factor for hepatic fibrosis and cirrhosis. Acoustic structure quantification (ASQ), based on statistical analysis of ultrasound echoes, is an emerging technique for hepatic steatosis diagnosis. A standardized measurement protocol for ASQ analysis was suggested previously; however, an optimal ultrasound scanning approach has not been concluded thus far. In this study, the suitability of scanning approaches for the ASQ-based evaluation of hepatic steatosis was investigated. Hepatic fat fractions (HFFs; liver segments VIII, III and VI) of 70 living liver donors were assessed with magnetic resonance spectroscopy. A clinical ultrasound machine equipped with a 3-MHz convex transducer was used to scan each participant using the intercostal, epigastric and subcostal planes to acquire raw data for estimating two ASQ parameters (Cm2 and focal disturbance [FD] ratio) of segments VIII, III and VI, respectively. The parameters were plotted as functions of the HFF for calculating the values of the correlation coefficient (r) and probability value (p). The diagnostic performance of the parameters in discriminating between the normal and steatotic (≥5 and ≥10%) groups was also compared using receiver operating characteristic (ROC) curves. The Cm2 and FD ratio values measured using the epigastric and subcostal planes did not correlate with the severity of hepatic steatosis. However, intercostal imaging exhibited a higher correlation between the ASQ parameters and HFF (r = -0.64, p < 0.001). The diagnostic performance of Cm2 and FD ratio in detecting hepatic steatosis using intercostal imaging was also satisfactory (areas under ROC curves >0.8). Intercostal imaging is an appropriate scanning approach for ASQ analysis of the liver.

Artifact reduction of ultrasound Nakagami imaging by combining multifocus image reconstruction and the noise-assisted correlation algorithm.

Several studies have investigated Nakagami imaging to complement the B-scan in tissue characterization. The noise-induced artifact and the parameter ambiguity effect can affect performance of Nakagami imaging in the detection of variations in scatterer concentration. This study combined multifocus image reconstruction and the noise-assisted correlation algorithm (NCA) into the algorithm of Nakagami imaging to suppress the artifacts. A single-element imaging system equipped with a 5 MHz transducer was used to perform the brightness/depth (B/D) scanning of agar phantoms with scatterer concentrations ranging from 2 to 32 scatterers/mm(3). Experiments were also carried out on a mass with some strong point reflectors in a breast phantom using a commercial scanner with a 7.5 MHz linear array transducer operated at multifocus mode. The multifocus radiofrequency (RF) signals after the NCA process were used for Nakagami imaging. In the experiments on agar phantoms, an increasing scatterer concentration from 2 to 32 scatterers/mm(3) led to backscattered statistics ranging from pre-Rayleigh to Rayleigh distributions, corresponding to the increase in the Nakagami parameter measured in the focal zone from 0.1 to 0.8. However, the artifacts in the far field resulted in the Nakagami parameters of various scatterer concentrations to be close to 1 (Rayleigh distribution), making Nakagami imaging difficult to characterize scatterers. In the same scatterer concentration range, multifocus Nakagami imaging with the NCA simultaneously suppressed two types of artifacts, making the Nakagami parameter increase from 0.1 to 0.8 in the focal zone and from 0.18 to 0.7 in the far field, respectively. In the breast phantom experiments, the backscattered statistics of the mass corresponded to a high degree of pre-Rayleigh distribution. The Nakagami parameter of the mass before and after artifact reduction was 0.7 and 0.37, respectively. The results demonstrated that the proposed method for artifact reduction allows a sensitive and effective scatterer characterization by Nakagami imaging.