Conventional diagnostic ultrasound images of the anatomy (as opposed to blood flow) reveal differences in the acoustic properties of soft tissues (mainly echogenicity but also, to some extent, attenuation), whereas ultrasound-based elasticity images are able to reveal the differences in the elastic properties of soft tissues (e.g., elasticity and viscosity). The benefit of elasticity imaging lies in the fact that many soft tissues can share similar ultrasonic echogenicities but may have different mechanical properties that can be used to clearly visualize normal anatomy and delineate pathologic lesions. Typically, all elasticity measurement and imaging methods introduce a mechanical excitation and monitor the resulting tissue response. Some of the most widely available commercial elasticity imaging methods are 'quasi-static' and use external tissue compression to generate images of the resulting tissue strain (or deformation). In addition, many manufacturers now provide shear wave imaging and measurement methods, which deliver stiffness images based upon the shear wave propagation speed. The goal of this review is to describe the fundamental physics and the associated terminology underlying these technologies. We have included a questions and answers section, an extensive appendix, and a glossary of terms in this manuscript. We have also endeavored to ensure that the terminology and descriptions, although not identical, are broadly compatible across the WFUMB and EFSUMB sets of guidelines on elastography (Bamber et al. 2013; Cosgrove et al. 2013).

The World Federation for Ultrasound in Medicine and Biology (WFUMB) has produced these guidelines for the use of elastography techniques in liver disease. For each available technique, the reproducibility, results, and limitations are analyzed, and recommendations are given. Finally, recommendations based on the international literature and the findings of the WFUMB expert group are established as answers to common questions. The document has a clinical perspective and is aimed at assessing the usefulness of elastography in the management of liver diseases.

The breast section of these Guidelines and Recommendations for Elastography produced under the auspices of the World Federation of Ultrasound in Medicine and Biology (WFUMB) assesses the clinically used applications of all forms of elastography used in breast imaging. The literature on various breast elastography techniques is reviewed, and recommendations are made on evidence-based results. Practical advice is given on how to perform and interpret breast elastography for optimal results, with emphasis placed on avoiding pitfalls. Artifacts are reviewed, and the clinical utility of some artifacts is discussed. Both strain and shear wave techniques have been shown to be highly accurate in characterizing breast lesions as benign or malignant. The relationship between the various techniques is discussed, and recommended interpretation based on a BI-RADS-like malignancy probability scale is provided. This document is intended to be used as a reference and to guide clinical users in a practical way.

Initially, a set of guidelines for the use of ultrasound contrast agents was published in 2004 dealing only with liver applications. A second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some non-liver applications. Time has moved on, and the need for international guidelines on the use of CEUS in the liver has become apparent. The present document describes the third iteration of recommendations for the hepatic use of contrast enhanced ultrasound (CEUS) using contrast specific imaging techniques. This joint WFUMB-EFSUMB initiative has implicated experts from major leading ultrasound societies worldwide. These liver CEUS guidelines are simultaneously published in the official journals of both organizing federations (i.e., Ultrasound in Medicine and Biology for WFUMB and Ultraschall in der Medizin/European Journal of Ultrasound for EFSUMB). These guidelines and recommendations provide general advice on the use of all currently clinically available ultrasound contrast agents (UCA). They are intended to create standard protocols for the use and administration of UCA in liver applications on an international basis and improve the management of patients worldwide.

Objectives: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading. Methods: Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2–3 days before surgery. We proposed a deep convolutional neural network by TL to analyze images of gray scale modality (GM) and elastogram modality (EM). The TL process was used for liver fibrosis classification by Inception-V3 network which pretrained on ImageNet. The diagnostic performance of TL and non-TL was compared. The value of single modalities, including GM and EM alone, and multimodalities, including GM + LSM and GM + EM, was evaluated and compared with that of LSM and serological indexes. Receiver operating characteristic curve analysis was performed to calculate the optimal area under the curve (AUC) for classifying fibrosis of S4, ≥ S3, and ≥ S2. Results: TL in GM and EM demonstrated higher diagnostic accuracy than non-TL, with significantly higher AUCs (all p < .01). Single-modal GM and EM both performed better than LSM and serum indexes (all p < .001). Multimodal GM + EM was the most accurate prediction model (AUCs are 0.950, 0.932, and 0.930 for classifying S4, ≥ S3, and ≥ S2, respectively) compared with GM + LSM, GM and EM alone, LSM, and biomarkers (all p < .05). Conclusions: Liver fibrosis can be staged by a transfer learning modal based on the combination of gray scale and elastogram ultrasound images, with excellent performance. Key Points: • Transfer learning consists in applying to a specific deep learning algorithm that pretrained on another relevant problem, expected to reduce the risk of overfitting due to insufficient medical images. • Liver fibrosis can be staged by transfer learning radiomics with excellent performance. • The most accurate prediction model of transfer learning by Inception-V3 network is the combination of gray scale and elastogram ultrasound images.