PURPOSE: Radiofrequency ablation (RFA) is a widely used alternative modality in the treatment of liver tumors. Ultrasound B-mode imaging is an important tool to guide the insertion of the RFA electrode into the tissue. However, it is difficult to visualize the ablation zone because RFA induces the shadow effect in a B-scan. Based on the randomness of ultrasonic backscattering, this study proposes ultrasound Nakagami imaging, which is a well-established method for backscattered statistics analysis, as an approach to complement the conventional B-scan for evaluating the ablation region. METHODS: Porcine liver samples (n = 6) were ablated using a RFA system and monitored by employing an ultrasound scanner equipped with a 7.5 MHz linear array transducer. During the stages of ablation (0-12 min) and postablation (12-24 min), the raw backscattered data were acquired at a sampling rate of 30 MHz for B-mode, Nakagami imaging, and polynomial approximation of Nakagami imaging. The contrast-to-noise ratio (CNR) was also calculated to compare the image contrasts of the B-mode and Nakagami images. RESULTS: The results demonstrated that the Nakagami image has the ability to visualize changes in the backscattered statistics in the ablation zone, including the shadow region during RFA. The average Nakagami parameter increased from 0.2 to 0.6 in the ablation stage, and then decreased to approximately 0.3 at the end of the postablation stage. Moreover, the CNR of the Nakagami image was threefold that of the B-mode image, showing that the Nakagami image has a better image contrast for monitoring RFA. Specifically, the use of the polynomial approximation equips the Nakagami image with an enhanced ability to estimate the range of the ablation region. CONCLUSIONS: This study demonstrated that ultrasound Nakagami imaging based on the analysis of backscattered statistics has the ability to visualize the RFA-induced ablation zone, even if the shadow effect exists in the B-scan.
PURPOSE: Radiofrequency ablation (RFA) is a widely used alternative modality in the treatment of liver tumors. Ultrasound B-mode imaging is an important tool to guide the insertion of the RFA electrode into the tissue. However, it is difficult to visualize the ablation zone because RFA induces the shadow effect in a B-scan. Based on the randomness of ultrasonic backscattering, this study proposes ultrasound Nakagami imaging, which is a well-established method for backscattered statistics analysis, as an approach to complement the conventional B-scan for evaluating the ablation region. METHODS: Porcine liver samples (n = 6) were ablated using a RFA system and monitored by employing an ultrasound scanner equipped with a 7.5 MHz linear array transducer. During the stages of ablation (0-12 min) and postablation (12-24 min), the raw backscattered data were acquired at a sampling rate of 30 MHz for B-mode, Nakagami imaging, and polynomial approximation of Nakagami imaging. The contrast-to-noise ratio (CNR) was also calculated to compare the image contrasts of the B-mode and Nakagami images. RESULTS: The results demonstrated that the Nakagami image has the ability to visualize changes in the backscattered statistics in the ablation zone, including the shadow region during RFA. The average Nakagami parameter increased from 0.2 to 0.6 in the ablation stage, and then decreased to approximately 0.3 at the end of the postablation stage. Moreover, the CNR of the Nakagami image was threefold that of the B-mode image, showing that the Nakagami image has a better image contrast for monitoring RFA. Specifically, the use of the polynomial approximation equips the Nakagami image with an enhanced ability to estimate the range of the ablation region. CONCLUSIONS: This study demonstrated that ultrasound Nakagami imaging based on the analysis of backscattered statistics has the ability to visualize the RFA-induced ablation zone, even if the shadow effect exists in the B-scan.
Authors: Diya Wang; Matthew S Adams; Peter D Jones; Dong Liu; Everette C Burdette; Chris J Diederich Journal: IEEE Trans Biomed Eng Date: 2021-09-20 Impact factor: 4.756