Regularized Estimation of Effective Scatterer Size and Acoustic Concentration Quantitative Ultrasound Parameters Using Dynamic Programming

Inter- and Intra-Operator Variability of Regularized Backscatter Quantitative Ultrasound for the Characterization of Breast Masses

OBJECTIVES

Here we report on the intra- and inter-operator variability of the backscatter coefficient (BSC) estimated with a new low-variance quantitative ultrasound (QUS) approach applied to breast lesions in vivo.

METHODS

Radiofrequency (RF) echo signals were acquired from 29 BIRADS 4 and 5 breast lesions in 2 sequential cohorts following 2 imaging protocols: cohort 1) radial and antiradial views, and cohort 2) short- and long-axis views. Protocol 2 was implemented after retraining and discussion on how to improve reproducibility. Each patient was scanned by at least 2 of 3 radiologists; each performed 3 acquisitions with transducer and patient repositioning in between acquisitions. BSC was estimated using a low-variance QUS approach based on regularization. Intra- and inter-operator variability of the intra-lesion median BSC was evaluated with a multifactorial ANOVA test (P-values) and the intraclass correlation coefficient (ICC).

RESULTS

Inter-operator variability was only significant in the first protocol (P < .007); ICCinter  = .77 (95% CI .71-.82), indicating good inter-operator agreement. In the second protocol, the inter-operator variability was not significant (P > .05) and agreement was excellent (ICCinter  = .92 [.89-.94]). In both protocols, the intra-operator variability was not significant.

CONCLUSIONS

Our findings demonstrate the need for standardizing image acquisition protocols for backscatter-based QUS to reduce inter-operator variability and ensure its successful translation to the characterization of suspicious breast masses.

Recent Advances in Attenuation Estimation

This chapter reviews some of the recent advances in the estimation of the local and the total attenuation, with an emphasis on reducing the bias and variance of the estimates. A special focus is put on describing the effect of power spectrum estimation on bias and variance, the introduction of regularization strategies, as well as on eliminating the need to use reference phantoms for compensating for system dependent effects.

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.

Robust Scatterer Number Density Segmentation of Ultrasound Images

Quantitative ultrasound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, scatterer number density is an important property that can affect the estimation of other QUS parameters. Scatterer number density can be classified into high or low scatterer densities. If there are more than ten scatterers inside the resolution cell, the envelope data are considered as fully developed speckle (FDS) and, otherwise, as underdeveloped speckle (UDS). In conventional methods, the envelope data are divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters, such as SNR and skewness, are employed to classify each patch of envelope data. However, these parameters are system-dependent, meaning that their distribution can change by the imaging settings and patch size. Therefore, reference phantoms that have known scatterer number density are imaged with the same imaging settings to mitigate system dependency. In this article, we aim to segment regions of ultrasound data without any patching. A large dataset is generated, which has different shapes of scatterer number density and mean scatterer amplitude using a fast simulation method. We employ a convolutional neural network (CNN) for the segmentation task and investigate the effect of domain shift when the network is tested on different datasets with different imaging settings. Nakagami parametric image is employed for multitask learning to improve performance. Furthermore, inspired by the reference phantom methods in QUS, a domain adaptation stage is proposed, which requires only two frames of data from FDS and UDS classes. We evaluate our method for different experimental phantoms and in vivo data.

Analytic Global Regularized Backscatter Quantitative Ultrasound

Although a variety of techniques have been developed to reduce the appearance of B-mode speckle, quantitative ultrasound (QUS) aims at extracting the hidden properties of the tissue. Herein, we propose two novel techniques to accurately and precisely estimate two important QUS parameters, namely, the average attenuation coefficient and the backscatter coefficient. Both the techniques optimize a cost function that incorporates data and continuity constraint terms, which we call AnaLytical Global rEgularized BackscatteR quAntitative ultrasound (ALGEBRA). We propose two versions of ALGEBRA, namely, 1-D- and 2-D-ALGEBRA. In 1-D-ALGEBRA, the regularized cost function is formulated in the axial direction, and the QUS parameters are calculated for one line of radio frequency (RF) echo data. In 2-D-ALGEBRA, the regularized cost function is formulated for the entire image, and the QUS parameters throughout the image are estimated simultaneously. This simultaneous optimization allows 2-D-ALGEBRA to "see" all the data before estimating the QUS parameters. In both the methods, we efficiently optimize the cost functions by casting it as a sparse linear system of equations. As a result of this efficient optimization, 1-D-ALGEBRA and 2-D-ALGEBRA are, respectively, 600 and 300 times faster than optimization using the dynamic programming (DP) method previously proposed by our group. In addition, the proposed technique has fewer input parameters that require manual tuning. Our results demonstrate that the proposed ALGEBRA methods substantially outperform least-square and DP methods in estimating the QUS parameters in phantom experiments.