Chen, B.-T.B.-T.ChenShieh, J.J.ShiehHuang, C.-W.C.-W.HuangWEN-SHIANG CHENChen, S.-R.S.-R.ChenTZONG-LIN JAY SHIEHCHUIN-SHAN CHEN2020-03-302020-03-3020140301-5629https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888440229&doi=10.1016%2fj.ultrasmedbio.2013.09.011&partnerID=40&md5=8eba4969802ad3afc2ea549afc018490https://scholars.lib.ntu.edu.tw/handle/123456789/481417Non-invasive temperature measurement of tissues deep inside the body has great potential for clinical applications, such as temperature monitoring during thermal therapy and early diagnosis of diseases. We developed a novel method for both temperature estimation and thermal mapping that uses ultrasound B-mode radiofrequency data. The proposed method is a hybrid that combines elements of physical and statistical models to achieve higher precision and resolution of temperature variations and distribution. We propose a dimensionless combined index (CI) that combines the echo shift differential and signal intensity difference with a weighting factor relative to the distance from the heat source. Invitro experiments verified that the combined index has a strong linear relationship with temperature variation and can be used to effectively estimate temperature with an average relative error <5%. This algorithm provides an alternative for imaging guidance-based techniques during thermal therapy and could easily be integrated into existing ultrasound systems. ? 2014 World Federation for Ultrasound in Medicine & Biology.[SDGs]SDG3Diagnosis; Disease control; Temperature; Temperature distribution; Temperature measurement; Ultrasonic imaging; Ultrasonics; Average relative error; Clinical application; Physical model; Radio-frequency datum; Statistical modeling; Temperature estimation; Temperature monitoring; Temperature variation; Models; accuracy; algorithm; animal tissue; article; B scan; echography; image processing; in vitro study; microwave therapy; non invasive measurement; nonhuman; physical model; priority journal; process development; radiofrequency radiation; signal processing; statistical model; temperature measurement; three dimensional imaging; tissue characterization; validation process; Physical model; Statistical model; Temperature; Ultrasonic imaging; Algorithms; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Models, Biological; Models, Statistical; Phantoms, Imaging; Reproducibility of Results; Sensitivity and Specificity; Thermography; Ultrasonographyjournal article10.1016/j.ultrasmedbio.2013.09.011242108562-s2.0-84888440229