Speed of sound index for liver steatosis estimation: a reliability study in normal subjects.

PURPOSE Non-alcoholic fatty liver disease (NAFLD) is the most widespread type of chronic liver disease in the Western countries. Ultrasound (US) is widely used for NAFLD staging. The Resona 7 US system (Mindray Bio-Medical Electronics Co., Ltd.) includes an image optimization and speed of ultrasound-related feature, Sound Speed Index (SSI). SSI is applied in a region of interest (ROI) that could potentially aid in tissue characterization. The purpose of this study is to evaluate the reliability of SSI on various examination parameters on normal subjects. METHODS Twenty normal subjects were examined by two radiologists performing SSI measurements in the liver in different ROI depths and sizes. Intraclass correlation coefficient (ICC) was calculated to measure intra- and inter-observer variability and inter-ROI variability. RESULTS For all ROIs and both radiologists, the mean inter-observer ICC was 0.62 and the mean intraobserver ICC was 0.52 and 0.79. The mean SSI values for all ROIs and examiners were in the range 1528.79-1540.16 m/s. CONCLUSION The results indicate that SSI can lead to reliable measurements on normal subjects, independent of ROI size but dependent on ROI placement. More studies processing NAFLD patients, utilizing reference methods of liver fat quantification either for reliability or correlation with SSI, should be performed to further investigate the relevance of the SSI as a potential biomarker in clinical practice for liver steatosis grading.

Lateral Position-Dependent Velocity Estimation Error in Plane-Wave Doppler Ultrasound Systems.

Doppler ultrasound has become a standard method used to diagnose and grade vascular diseases and monitor their progression. Conventional focused-beam color Doppler imaging is routinely used in clinical practice, but suffers from inherent trade-offs between spatial, temporal and velocity resolution. Newer, plane-wave Doppler imaging offers rapid simultaneous acquisition of B-mode, color and spectral Doppler information across large fields of view, making it a potentially useful method for quantitative estimation of blood flow velocities in the clinic. However, plane-wave imaging can lead to a substantial error in velocity estimation, which is dependent on the lateral location within the image. This is seen in both clinical and experimental plane-wave systems. In the work described in this article, we quantified this velocity error under different geometric and beamforming conditions using numerical simulation and experimental phantoms. We found that the lateral-dependent velocity errors are caused by asymmetrical geometric spectral broadening, and outline a correction algorithm that can mitigate these errors.

Computer vision approach for ultrasound Doppler angle estimation.

Doppler ultrasound is an important noninvasive diagnostic tool for cardiovascular diseases. Modern ultrasound imaging systems utilize spectral Doppler techniques for quantitative evaluation of blood flow velocities, and these measurements play a crucial rule in the diagnosis and grading of arterial stenosis. One drawback of Doppler-based blood flow quantification is that the operator has to manually specify the angle between the Doppler ultrasound beam and the vessel orientation, which is called the Doppler angle, in order to calculate flow velocities. In this paper, we will describe a computer vision approach to automate the Doppler angle estimation. Our approach starts with the segmentation of blood vessels in ultrasound color Doppler images. The segmentation step is followed by an estimation technique for the Doppler angle based on a skeleton representation of the segmented vessel. We conducted preliminary clinical experiments to evaluate the agreement between the expert operator's angle specification and the new automated method. Statistical regression analysis showed strong agreement between the manual and automated methods. We hypothesize that the automation of the Doppler angle will enhance the workflow of the ultrasound Doppler exam and achieve more standardized clinical outcome.

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

PURPOSE: To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs). MATERIALS AND METHODS: Two hundred SWE image sequences from 88 healthy individuals (F0 fibrosis stage) and 112 CLD patients (46 with mild fibrosis (F1), 16 with significant fibrosis (F2), 22 with severe fibrosis (F3), and 28 with cirrhosis (F4)) were analyzed to detect temporal stiffness stability between frames. An inverse Red, Green, Blue (RGB) colormap-to-stiffness process was performed for each image sequence, followed by a wavelet transform and fuzzy c-means clustering algorithm. This resulted in a binary mask depicting areas of high and low stiffness temporal stability. The mask was then applied to the first image of the SWE sequence, and the derived, masked SWE image was used to estimate its impact in standard clinical examination and CNN classification. Regarding the impact of the masked SWE image in clinical examination, one measurement by two radiologists was performed in each SWE image and two in the corresponding masked image measuring areas with high and low stiffness temporal stability. Then, stiffness stability parameters, interobserver variability evaluation and diagnostic performance by means of ROC analysis were assessed. The masked and unmasked sets of SWE images were fed into a CNN scheme for comparison. RESULTS: The clinical impact evaluation study showed that the masked SWE images decreased the interobserver variability of the radiologists' measurements in the high stiffness temporal stability areas (interclass correlation coefficient (ICC) = 0.92) compared to the corresponding unmasked ones (ICC = 0.76). In terms of diagnostic accuracy, measurements in the high-stability areas of the masked SWE images (area-under-the-curve (AUC) ranging from 0.800 to 0.851) performed similarly to those in the unmasked SWE images (AUC ranging from 0.805 to 0.893). Regarding the measurements in the low stiffness temporal stability areas of the masked SWE images, results for interobserver variability (ICC = 0.63) and diagnostic accuracy (AUC ranging from 0.622 to 0.791) were poor. Regarding the CNN classification, the masked SWE images showed improved accuracy (ranging from 82.5% to 95.5%) compared to the unmasked ones (ranging from 79.5% to 93.2%) for various CLD stage combinations. CONCLUSION: Our detection algorithm excludes unreliable areas in SWE images, reduces interobserver variability, and augments CNN's accuracy scores for many combinations of fibrosis stages.

Muscle thickness changes during abdominal hollowing: an assessment of between-day measurement error in controls and patients with chronic low back pain.

Spine stabilization exercises, in which patients are taught to perform isolated contractions of the transverses abdominus (TrA) during "abdominal hollowing", are a popular physiotherapeutic treatment for low back pain (LBP). Successful performance is typically judged by the relative increase in TrA thickness compared with that of the internal (OI) and external (OE) oblique muscles, measured using ultrasound. The day-to-day measurement error (imprecision) associated with these indices of preferential activation has not been assessed but is important to know since it influences the interpretation of changes after treatment. On 2 separate days, 14 controls and 14 patients with chronic LBP (cLBP) performed abdominal hollowing exercises in hook-lying, while M-mode ultrasound images superimposed with tissue Doppler imaging (TDI) data were recorded from the abdominal muscles (N = 5 on each side). The fascial lines bordering the TrA, OI and OE were digitized, and muscle thicknesses were calculated. The between-day error (intra-observer) was expressed as the standard error of measurement, SEM; SEM as a percentage of the mean gave the coefficient of variation (CV). There were no significant between-day differences for the mean values of resting or maximal thickness for any muscle, in either group (P > 0.05). The median SEM and CV of all thickness variables was 0.71 mm and 10.9%, respectively for the controls and 0.80 mm or 11.3%, respectively for the cLBP patients. For the contraction ratios (muscle thickness contracted/thickness at rest), the CVs were 3-11% (controls) and 5-12% (patients). The CVs were unacceptably high (30-50%, both groups) for the TrA preferential activation ratio (TrA proportion of the total lateral abdominal muscle thickness when contracted minus at rest). In both the controls and patients, the precision of measurement of absolute muscle thickness and relative change in thickness during abdominal hollowing was acceptable, and commensurate with that typical of biological measurements. The TrA preferential activation ratio is too imprecise to be of clinical use. Knowledge of the SEM for these indices is essential for interpreting the clinical relevance of any changes observed following physiotherapy.

Improved Contrast-Enhanced Power Doppler Using a Coherence-Based Estimator.

While plane-wave imaging can improve the performance of power Doppler by enabling much longer ensembles than systems using focused beams, the long-ensemble averaging of the zero-lag autocorrelation R(0) estimates does not directly decrease the mean noise level, but only decreases its variance. Spatial variation of the noise due to the time-gain compensation and the received beamforming aperture ultimately limits sensitivity. In this paper, we demonstrate that the performance of power Doppler imaging can be improved by leveraging the higher lags of the autocorrelation [e.g., R(1), R(2),…] instead of the signal power (R(0)). As noise is completely uncorrelated from pulse-to-pulse while the flow signal remains correlated significantly longer, weak signals just above the noise floor can be made visible through the reduction of the noise floor. Finally, as coherence decreases proportionally with respect to velocity, we demonstrate how signal coherence can be targeted to separate flows of different velocities. For instance, we show how long-time-range coherence of microbubble contrast-enhanced flow specifically isolates slow capillary perfusion (as opposed to conduit flow).

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography.

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.

A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.

PURPOSE: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. METHODS: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. RESULTS: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. CONCLUSIONS: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.

Ultrafast compound Doppler imaging: providing full blood flow characterization.

Doppler-based flow analysis methods require acquisition of ultrasound data at high spatio-temporal sampling rates. These rates represent a major technical challenge for ultrasound systems because a compromise between spatial and temporal resolution must be made in conventional approaches. Consequently, ultrasound scanners can either provide full quantitative Doppler information on a limited sample volume (spectral Doppler), or averaged Doppler velocity and/or power estimation on a large region of interest (Doppler flow imaging). In this work, we investigate a different strategy for acquiring Doppler information that can overcome the limitations of the existing Doppler modes by significantly reducing the required acquisition time. This technique is called ultrafast compound Doppler imaging and is based on the following concept: instead of successively insonifying the medium with focused beams, several tilted plane waves are sent into the medium and the backscattered signals are coherently summed to produce high-resolution ultrasound images. We demonstrate that this strategy allows reduction of the acquisition time by a factor of up to of 16 while keeping the same Doppler performance. Depending on the application, different directions to increase performance of Doppler analysis are proposed and the improvement is quantified: the ultrafast compound Doppler method allows faster acquisition frame rates for high-velocity flow imaging, or very high sensitivity for low-flow applications. Full quantitative Doppler flow analysis can be performed on a large region of interest, leading to much more information and improved functionality for the physician. By leveraging the recent emergence of ultrafast parallel beamforming systems, this paper demonstrates that breakthrough performances in flow analysis can be reached using this concept of ultrafast compound Doppler.