Real-time 2-D temperature imaging using ultrasound

Dalong Liu, Emad S. Ebbini

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153 Scopus citations


We have previously introduced methods for noninvasive estimation of temperature change using diagnostic ultrasound. The basic principle was validated both in vitro and in vivo by several groups worldwide. Some limitations remain, however, that have prevented these methods from being adopted in monitoring and guidance of minimally invasive thermal therapies, e.g., RF ablation and high-intensity-focused ultrasound (HIFU). In this letter, we present first results from a real-time system for 2-D imaging of temperature change using pulse-echo ultrasound. The front end of the system is a commercially available scanner equipped with a research interface, which allows the control of imaging sequence and access to the RF data in real time. A high-frame-rate 2-D RF acquisition mode, M2D, is used to capture the transients of tissue motion/deformations in response to pulsed HIFU. The M2D RF data is streamlined to the back end of the system, where a 2-D temperature imaging algorithm based on speckle tracking is implemented on a graphics processing unit. The real-time images of temperature change are computed on the same spatial and temporal grid of the M2D RF data, i.e., no decimation. Verification of the algorithm was performed by monitoring localized HIFU-induced heating of a tissue-mimicking elastography phantom. These results clearly demonstrate the repeatability and sensitivity of the algorithm. Furthermore, we present in vitro results demonstrating the possible use of this algorithm for imaging changes in tissue parameters due to HIFU-induced lesions. These results clearly demonstrate the value of the real-time data streaming and processing in monitoring, and guidance of minimally invasive thermotherapy.

Original languageEnglish (US)
Article number5306150
Pages (from-to)12-16
Number of pages5
JournalIEEE Transactions on Biomedical Engineering
Issue number1
StatePublished - Jan 2010

Bibliographical note

Funding Information:
Manuscript received May 2, 2009; revised July 16, 2009. First published October 30, 2009; current version published January 4, 2010. This work was supported in part by the National Institutes of Health under Grant EB009750. Asterisk indicates corresponding author. D. Liu is with the Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: ∗E. S. Ebbini is with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: Color versions of one or more of the figures in this paper are available online at Digital Object Identifier 10.1109/TBME.2009.2035103


  • GPU
  • HIFU
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