湖南文理学院数理学院
纸质出版:2024
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刘备, 许克晖, 杨德智, 等. 基于超声时频分析与残差网络的生物组织变性识别[J]. 传感器与微系统, 2024,(1):40-43.
刘备, 许克晖, 杨德智, 等. 基于超声时频分析与残差网络的生物组织变性识别[J]. 传感器与微系统, 2024,(1):40-43. DOI: 10.13873/J.1000-9787(2024)01-0040-04.
为了能对高强度聚焦超声(HIFU)治疗过程中的生物组织进行实时准确的变性识别,提出了一种基于超声时频分析与残差网络(ResNet)的生物组织变性识别方法。首先,采用广义S变换(GST)方法对生物组织超声回波信号进行时频分析,得到二维时频图;然后,通过迁移学习,将在ImageNet数据集上训练得到的参数应用于超声回波信号数据集;最后,利用ResNet101模型从生物组织变性前后的时频图中学习和提取有效的变性信息,并可视化变性特征轨迹,实时地完成生物组织变性识别。实验结果表明:相较于现有基于信号能量,AR系数以及熵特征的变性识别方法,GST-ResNet方法无需人为经验选取特征参数,具有更高的识别率,可以实时准确地完成生物组织的变性识别。
刘备,杨江河,蔡剑华,彭梓齐,黎小琴.基于AR模型与椭圆控制理论的生物组织变性检测方法[J].传感器与微系统,2022(12).
钱骏,谢伟,周小伟,谭坚文,王智彪,杜永洪,李雁浩.基于换能器驱动信号特征的高强度聚焦超声焦域损伤实时监测[J].物理学报,2022(03).
胡伟鹏,邹孝,刘备,赵新民,钱盛友.基于多迭代变分模态分解与复合多尺度散布熵的生物组织变性识别方法[J].传感技术学报,2019(12).
刘备,胡伟鹏,邹孝,丁亚军,钱盛友.基于变分模态分解与多尺度排列熵的生物组织变性识别[J].物理学报,2019(02).
明文,钱盛友,赵新民,丁亚军.基于超声散射回波的组织损伤程度监测方法[J].测试技术学报,2014(03).
Bei Liu,Xian Zhang,2.Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment[J].Computer Modeling in Engineering & Sciences,2022.
Tonguc Tolga,Strunk Holger,GonzalezCarmona Maria A,Recker Florian,Lütjohann Dieter,Thudium Marcus,Conrad Rupert,Becher Marc U,Savchenko Oleksandr,Davidova Darya,Luechters Guido,Mustea Alexander,Strassburg Christian P,Attenberger Ulrike,Pieper Claus C,Jenne Jürgen,Marinova Milka.US-guided high-intensity focused ultrasound (HIFU) of abdominal tumors: outcome, early ablation-related laboratory changes and inflammatory reaction. A single-center experience from Germany.[J].International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group,2021.
Sanchez M.,Barrere V.,Treilleux I.,Chopin N.,Melodelima D..Development of a noninvasive HIFU treatment for breast adenocarcinomas using a toroidal transducer based on preliminary attenuation measurements[J].Ultrasonics,2021.
Yuldashev Petr V,Karzova Maria M,Kreider Wayne,Rosnitskiy Pavel B,Sapozhnikov Oleg A,Khokhlova Vera A."HIFU beam": a Simulator for Predicting Axially Symmetric Nonlinear Acoustic Fields Generated by Focused Transducers in a Layered Medium.[J].IEEE transactions on ultrasonics, ferroelectrics, and frequency control,2021.
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Yanling Zhu, Chunguang Xu, Dingguo Xiao.Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition[J].Traitement du Signal,2019.
Cai Jianhua,Xiao Yongliang.Filtering of nuclear magnetic resonance logging signal based on the generalized S transform and singular value decomposition[J].Arabian Journal of Geosciences,2018.
Chen Hui,Chen Yuanchun,Sun Shaotong,Hu Ying,Feng Jun.A High-Precision Time-Frequency Entropy Based on Synchrosqueezing Generalized S-Transform Applied in Reservoir Detection[J].Entropy,2018.
Monfared Maryam Mohammadi,Behnam Hamid,Rangraz Parisa,Tavakkoli Jahan.High-Intensity Focused Ultrasound Thermal Lesion Detection Using Entropy Imaging of Ultrasound Radio Frequency Signal Time Series.[J].Journal of medical ultrasound,2018.
Visa Suomi,Yang Han,Elisa Konofagou,Robin O Cleveland.The effect of temperature dependent tissue parameters on acoustic radiation force induced displacements[J].Physics in Medicine and Biology,2016.
Piotr Karwat,Tamara Kujawska,Peter A. Lewin,Wojciech Secomski,Barbara Gambin,Jerzy Litniewski.Determining temperature distribution in tissue in the focal plane of the high (>100<ce:hsp sp="0.25"/>W/cm 2 ) intensity focused ultrasound beam using phase shift of ultrasound echoes[J].Ultrasonics,2016.
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