Deep learning in ultrasound elastography imaging: A review.

Journal: Medical physics
PMID: 35842833

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.

Authors

  • Hongliang Li
  • Manish Bhatt
    Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.
  • Zhen Qu
    Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center, Montréal, Québec, Canada.
  • Shiming Zhang
    California Nanosystems Institute, University of California, Los Angeles, Los Angeles, California, USA.
  • Martin C Hartel
    California Nanosystems Institute, University of California, Los Angeles, Los Angeles, California, USA.
  • Ali Khademhosseini
    Center for Minimally Invasive Therapeutics, University of California, Los Angeles, CA, 90095, USA.
  • Guy Cloutier

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