Attenuation Coefficient Estimation of Normal Placentas

The Use of Noninvasive Velacur® for Discriminating between Volunteers and Patients with Chronic Liver Disease: A Feasibility Study

Background and Aims

Nonalcoholic fatty liver disease is the leading cause of chronic liver disease globally and can progress to cirrhosis, liver failure, and liver cancer. Current AASLD, AGA, and ADA guidelines recommend assessment for liver fibrosis in all patients with NAFLD. Serum biomarkers for fibrosis, while widely available, have notable limitations. Imaging-based noninvasive testing for liver fibrosis/cirrhosis is more accurate and is becoming more widespread.

Methods

We evaluated the feasibility of a novel shear wave absolute vibroelastography (S-WAVE) modality called Velacur® for assessing liver stiffness measurement (LSM) for fibrosis and attenuation coefficient estimation (ACE) in differentiating patients with chronic liver disease from normal healthy controls.

Results

Fifty-four healthy controls and 89 patients with NAFLD or cured HCV with a prior known LSM of >8 kPa were enrolled, and all subjects were evaluated with FibroScan® and Velacur®. Velacur® was able to discriminate patients with increased liver stiffness as determined by a FibroScan® score of >8 kPa from healthy controls with an AUC of 0.938 (0.88-0.96). For assessment of steatosis in NAFLD patients only, Velacur® could identify patients with steatosis from healthy controls with an AUC of 0.831 (0.777-0.880). The Velacur® scan quality assessment was superior in healthy controls, as compared to patients, and the scan quality, as assessed by the quality factor (QF) and interquartile range (IQR)/median, was affected by BMI. Velacur® was safe and well tolerated by patients, and there were no adverse events.

Conclusion

Velacur® assessment of liver stiffness measurement and liver attenuation is comparable to results obtained by FibroScan® and is an alternative technology for monitoring liver fibrosis progression in patients with chronic liver disease. This trial is registered with NCT03957070.

Project SWAVE 2.0: An overview of the study design for multimodal placental image acquisition and alignment

Development of non-invasive and in utero placenta imaging techniques can potentially identify biomarkers of placental health. Correlative imaging using multiple multiscale modalities is particularly important to advance the understanding of placenta structure, function and their relationship. The objective of the project SWAVE 2.0 was to understand human placental structure and function and thereby identify quantifiable measures of placental health using a multimodal correlative approach. In this paper, we present a multimodal image acquisition protocol designed to acquire and align data from ex vivo placenta specimens derived from both healthy and complicated pregnancies. Qualitative and quantitative validation of the alignment method were performed. The qualitative analysis showed good correlation between findings in the MRI, ultrasound and histopathology images. The proposed protocol would enable future studies on comprehensive analysis of placental anatomy, function and their relationship.

● An overview of a novel multimodal placental image acquisition protocol is presented.

● A co-registration method using surface markers and external fiducials is described.

● A preliminary correlative imaging analysis for a placenta specimen is presented.

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Quantitative ultrasound methods aim to estimate the acoustic properties of the underlying medium, such as the attenuation and backscatter coefficients, and have applications in various areas including tissue characterization. In practice, tissue heterogeneity makes the coefficient estimation challenging. In this work, we propose a computationally efficient algorithm to map spatial variations of the attenuation coefficient. Our proposed approach adopts a fast, linear least-squares strategy to fit the signal model to data from pulse-echo measurements. As opposed to existing approaches, we directly estimate the attenuation map, that is, the local attenuation coefficient at each axial location by solving a joint estimation problem. In particular, we impose a physical model that couples all these local estimates and combine it with a smooth regularization to obtain a smooth map. Compared to the conventional spectral log difference method and the more recent ALGEBRA approach, we demonstrate that the attenuation estimates obtained by our method are more accurate and better correlate with the ground-truth attenuation profiles over a wide range of spatial and contrast resolutions.

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Fast linear least-squares method for ultrasound attenuation and backscatter estimation

The ultrasonic attenuation and backscatter coefficients of tissues are relevant acoustic parameters due to their wide range of clinical applications. In this paper, a linear least-squares method for the estimation of these coefficients in a homogeneous region of interest based on pulse-echo measurements is proposed. The method efficiently fits an ultrasound backscattered signal model to the measurements in both the frequency and depth dimension simultaneously at a low computational cost. It is demonstrated that the inclusion of depth information has a positive effect particularly on the accuracy of the estimated attenuation. The sensitivity of the attenuation and backscatter coefficients' estimates to several predefined parameters such as the window length, window overlap and usable bandwidth of the spectrum is also studied. Comparison of the proposed method with a benchmark approach based on dynamic programming highlights better performance of our method in estimating these coefficients, both in terms of accuracy and computation time. Further analysis of the computation time as a function of the predefined parameters indicates our method's potential to be used in real-time clinical settings.

Amplitude based segmentation of ultrasound echoes for attenuation coefficient estimation

In vivo ultrasound attenuation coefficient measurements are of interest as they can provide insight into tissue pathology. They are also needed so that measurements of the tissue's frequency dependent ultrasound backscattering coefficient may be corrected for attenuation. In vivo measurements of the attenuation coefficient are challenging because it has to be estimated from the depth dependent decay of backscatter signals that display a large degree of magnitude variation. In this study we describe and evaluate an improved backscatter method to estimate ultrasound attenuation which is tolerant to the presence of some backscatter inhomogeneity. This employs an automated algorithm to segment and remove atypically strong echoes to lessen the potential bias these may introduce on the attenuation coefficient estimates. The benefit of the algorithm was evaluated by measuring the frequency dependent attenuation coefficient of a gelatine phantom containing randomly distributed cellulose scatterers as a homogeneous backscattering component and planar pieces of cooked leek to provide backscattering inhomogeneities. In the phantom the segmentation algorithm was found to improve the accuracy and precision of attenuation coefficient estimates by up to 80% and 90%, respectively. The effect of the algorithm was then measured invivo using 32 radiofrequency B-mode datasets from the breasts of two healthy female volunteers, producing a 5 to 25% reduction in mean attenuation coefficient estimates and a 30 to 50% reduction in standard deviation of attenuation coefficient across different positions within each breast. The results suggest that the segmentation algorithm may improve the accuracy and precision of attenuation coefficient estimates invivo.

Frequency-dependent attenuation reconstruction with an acoustic reflector

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequency-dependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04 dB/cm at 1 MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, consistently from different images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as facilitate attenuation compensation of other ultrasound-based imaging techniques.

Effects of acoustic nonlinearity on pulse-echo attenuation coefficient estimation from tissue-mimicking phantoms

The ultrasonic attenuation coefficient (ACE) can be used to classify tissue state. Pulse-echo spectral-based attenuation estimation techniques, such as the spectral-log-difference method (SLD), account for beam diffraction effects using a reference phantom having a sound speed close to the sound speed of the sample. Methods like SLD assume linear propagation of ultrasound and do not account for potential acoustic nonlinear distortion of the backscattered power spectra in both sample and reference. In this study, the ACE of a sample was computed and compared using the SLD with two independent references (high attenuating and low attenuating phantoms but with similar B/A values) and over several pressure levels. Both numerical and physical tissue-mimicking phantoms were used in the study. The results indicated that the biases in ACE increased when using a reference having low attenuation, whereas the high attenuating reference produced more consistent ACE. Furthermore, increments in ACE vs input pressure were correlated to the log-ratio of Gol'dberg numbers between the sample and reference (R²=0.979 in simulations and R²=0.734 in experiments). Therefore, the results suggest that to reduce bias in ACE using spectral-based methods, both the sound speed and the Gol'dberg number of the reference phantom should be matched to the sample.