Deep learning in ultrasound elastography imaging: A review. Issue 9 (30th July 2022)

• Record Type: Journal Article
• Authors: Li, Hongliang; Bhatt, Manish; Qu, Zhen; Zhang, Shiming; Hartel, Martin C.; Khademhosseini, Ali; Cloutier, Guy
• Is Part Of: Medical physics. Volume 49:Issue 9(2022)
• Page Start: 5993
• Page End: 6018
• Publication Date: 2022-07-30
• Subjects: artificial intelligence -- deep learning -- elastography -- ultrasound; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15856
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi‐static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayered perceptron, convolutional neural network, and recurrent neural network, are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.