Correlation between renal ultrasound image features and temperature during radiofrequency
ablation(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第9期
- Page:
- 1164-1168
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Correlation between renal ultrasound image features and temperature during radiofrequency
ablation
- Author(s):
- CUI Mengyao1; YING Xiaoting1; ZHAO Xingqun1; YAO Linfang2
- 1. School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China 2. Department of Urology,
Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
- Keywords:
- radiofrequency ablation ultrasound image noninvasive temperature measurement
- PACS:
- R318.6
- DOI:
- 10.3969/j.issn.1005-202X.2020.09.016
- Abstract:
- To explore the correlation between ultrasonic image feature parameters and temperature during renal radiofrequency
ablation, a radiofrequency ablation experiment of fresh pig kidney in vitro is designed and carried out. The temperature measured
by invasive thermocouple and B-Mode images during ablation are collected. The feature parameters such as average gray-level
(AVGL), grayscale standard deviation and grayscale gradient value are calculated after removing the probe in the region of interest
by threshold segmentation method or taking the temperature prove as a circle. ThenMATLAB software is used to performregression
analysis on the obtained AVGL, grayscale standard deviation and temperature, and continuous piecewise function model is used
for data fitting. It is proved that threshold segmentation method can be used for probe removal, and the feature parameters of
ultrasound image in renal radiofrequency ablation are found to be correlated with temperature, and a well-fittedAVGL-temperature
model and a grayscale standard deviation-temperature model are obtained. However, the parameters in the obtained models will
change with the difference of image gain and ablation tissue. Such interference should be excluded in the follow-up research to
obtain a more clinically valuable temperature measurement model.
Last Update: 2020-09-25