PDF(Pubmed)
Abstract：
UNASSIGNED: Noninvasive therapies such as focused ultrasound were developed to be used for cancer therapies, vessel bleeding, and drug delivery. The main purpose of focused ultrasound therapy is to affect regions of interest (ROI) of tissues without any injuries to surrounding tissues. In this regard, an appropriate monitoring method is required to control the treatment.
UNASSIGNED: This study is aimed to develop a noninvasive monitoring technique of focused ultrasound (US) treatment using sparse representation of US radio frequency (RF) echo signals. To this end, reasonable results in temperature change estimation in the tissue under focused US radiation were obtained by utilizing algorithms related to sparse optimization as orthogonal matching pursuit (OMP) and accompanying Shannon\'s entropy. Consequently, ex vivo tissue experimental tests yielded two datasets, including low-intensity focused US (LIFU) and high-intensity focused US (HIFU) data. The proposed processing method analyzed the ultrasonic RF echo signal and expressed it as a sparse signal and calculated the entropy of each frame.
UNASSIGNED: The results indicated that the suggested approach could noninvasively estimate temperature changes between 37°C and 47°C during LIFU therapy. In addition, it represented temperature changes during HIFU ablation at various powers, ranging from 10 to 130 W. The normalized mean square error of the proposed method is 0.28, approximately 2.15 on previous related methods.
UNASSIGNED: These results demonstrated that this novel proposed approach, including the combination of sparsity and Shanoon\'s entropy, is more feasible and effective in temperature change estimation than its predecessors.
UNASSIGNED: This study is aimed to develop a noninvasive monitoring technique of focused ultrasound (US) treatment using sparse representation of US radio frequency (RF) echo signals. To this end, reasonable results in temperature change estimation in the tissue under focused US radiation were obtained by utilizing algorithms related to sparse optimization as orthogonal matching pursuit (OMP) and accompanying Shannon\'s entropy. Consequently, ex vivo tissue experimental tests yielded two datasets, including low-intensity focused US (LIFU) and high-intensity focused US (HIFU) data. The proposed processing method analyzed the ultrasonic RF echo signal and expressed it as a sparse signal and calculated the entropy of each frame.
UNASSIGNED: The results indicated that the suggested approach could noninvasively estimate temperature changes between 37°C and 47°C during LIFU therapy. In addition, it represented temperature changes during HIFU ablation at various powers, ranging from 10 to 130 W. The normalized mean square error of the proposed method is 0.28, approximately 2.15 on previous related methods.
UNASSIGNED: These results demonstrated that this novel proposed approach, including the combination of sparsity and Shanoon\'s entropy, is more feasible and effective in temperature change estimation than its predecessors.
摘要：
■非侵入性治疗如聚焦超声被开发用于癌症治疗，血管出血,和药物输送。聚焦超声治疗的主要目的是影响组织的感兴趣区域(ROI)而不对周围组织造成任何损伤。在这方面,需要适当的监测方法来控制治疗。
■这项研究旨在开发一种使用美国射频（RF）回波信号的稀疏表示进行聚焦超声（US）治疗的无创监测技术。为此,通过利用与稀疏优化相关的算法作为正交匹配追踪（OMP）和伴随的Shannon熵，获得了聚焦US辐射下组织温度变化估计的合理结果。因此,离体组织实验测试产生了两个数据集，包括低强度聚焦美国（LIFU）和高强度聚焦美国（HIFU）数据。所提出的处理方法对超声射频回波信号进行分析,将其表示为稀疏信号,并计算每帧的熵。
■结果表明，建议的方法可以无创估计LIFU治疗期间37°C至47°C之间的温度变化。此外,它代表不同功率下HIFU消融过程中的温度变化，范围从10到130W。所提出的方法的归一化均方误差为0.28，约为以前相关方法的2.15。
■这些结果表明，这种新提出的方法，包括稀疏性和Shanoon熵的组合，在温度变化估计方面比其前辈更可行和有效。
■这项研究旨在开发一种使用美国射频（RF）回波信号的稀疏表示进行聚焦超声（US）治疗的无创监测技术。为此,通过利用与稀疏优化相关的算法作为正交匹配追踪（OMP）和伴随的Shannon熵，获得了聚焦US辐射下组织温度变化估计的合理结果。因此,离体组织实验测试产生了两个数据集，包括低强度聚焦美国（LIFU）和高强度聚焦美国（HIFU）数据。所提出的处理方法对超声射频回波信号进行分析,将其表示为稀疏信号,并计算每帧的熵。
■结果表明，建议的方法可以无创估计LIFU治疗期间37°C至47°C之间的温度变化。此外,它代表不同功率下HIFU消融过程中的温度变化，范围从10到130W。所提出的方法的归一化均方误差为0.28，约为以前相关方法的2.15。
■这些结果表明，这种新提出的方法，包括稀疏性和Shanoon熵的组合，在温度变化估计方面比其前辈更可行和有效。
