AI in EE

AI IN DIVISIONS

AI in Circuit Division

AI in EE

AI IN DIVISIONS

AI in Circuit Division ​

AI in Circuit Division

Myeong-Gee Kim, Seok-Hwan Oh, Youngmin Kim, Hyuksool Kwon, Hyeon-Min Bae, “Learning-based attenuation quantification in abdominal ultrasound”, International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), Sept. 2021

Myeong-Gee Kim, Seok-Hwan Oh, Youngmin Kim, Hyuksool Kwon, Hyeon-Min Bae, “Learning-based attenuation quantification in abdominal ultrasound”, International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), Sept. 2021

Abstract: The attenuation coefficient (AC) of tissue in medical ultrasound has great potential as a quantitative biomarker due to its high sensitivity to pathological properties. In particular, AC is emerging as a new quantitative biomarker for diagnosing and quantifying hepatic steatosis. In this paper, a learning-based technique to quantify AC from pulse-echo data obtained through a single convex probe is presented. In the proposed method, ROI adaptive transmit beam focusing (TxBF) and envelope detection schemes are employed to increase the estimation accuracy and noise resilience, respectively. In addition, the proposed network is designed to extract accurate AC of the target region considering attenuation/sound speed/scattering of the propagating waves in the vicinities of the target region. The accuracy of the proposed method is verified through simulation and phantom tests. In addition, clinical pilot studies show that the estimated liver AC values using the proposed method are correlated strongly with the fat fraction obtained from magnetic resonance imaging (R2=0.89R2=0.89, p<0.001p<0.001). Such results indicate the clinical validity of the proposed learning-based AC estimation method for diagnosing hepatic steatosis.