Ultrasound Computed Tomography using physical-informed Neural Network

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

Ultrasound computed tomography (USCT) is an important diagnostic technique for improved imaging in medical area. The reconstruction of tissue properties, such as attenuation, mass density, and speed of sound (SoS), can be used to monitor and analyze the tissue condition. In this paper, an optimized physical-based architecture called physics-informed neural network (PINN) is used to solve the inverse problems of USCT. This neural network leverages the physical information by adding the residuals of a system of Partial Differential Equations (PDE) to the loss function in the learning process. By incorporating all the information including the PDE, initial and boundary conditions, proposed nerual network can learn solutions of the wave equation. The results showed that PINN can extract the SoS value within an acceptable error.

Original languageEnglish (US)
JournalIEEE International Ultrasonics Symposium, IUS
DOIs
StatePublished - 2021
Event2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, China
Duration: Sep 11 2011Sep 16 2011

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics

Fingerprint

Dive into the research topics of 'Ultrasound Computed Tomography using physical-informed Neural Network'. Together they form a unique fingerprint.

Cite this