Neural Network Based Bone Density Estimation from the Ultrasound Waveforms Inside Cancellous Bone Derived by FDTD Simulations
Neural Network Based Bone Density Estimation from the Ultrasound Waveforms Inside Cancellous Bone Derived by FDTD Simulations
复制标题
基于 FDTD 模拟得出的松质骨内部超声波形的基于神经网络的骨密度估计
DOI：
10.1109/ultsym.2018.8580010
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Shuqiong Wu
中科院分区：
文献类型：
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作者：
Yoshiki Nagatani;Shigeaki Okumura;Shuqiong Wu
定量超声骨评估技术由于其无创性、便携性和低诊断费用而引起了广泛的研究关注。对沿着松质骨传播的超声信号的分析是重要的，因为它强烈地反映了骨质量。然而，由于松质骨具有复杂的多孔结构，很难解析地理解波的行为。因此，基于神经网络的方法被用于骨密度的估计。通过时域有限差分法（FDTD）模拟得到了在松质骨内的传播波形。结果表明，基于神经网络的骨密度估计方法比传统方法更有潜力。
Quantitative ultrasound techniques for bone assessment now attract strong research attentions because of their non-invasiveness, portability, and the low diagnosis expense. The analysis of the ultrasonic signals propagating along the cancellous bone is important because it strongly reflects the bone quality. However, it is difficult to analytically understand the wave behavior because the cancellous bone has complexed porous structure. Therefore, the neural network-based approaches were used for the estimation of the bone density. The waveforms propagating inside the cancellous bone were derived by the FDTD simulation. As a result, the neural network-based method showed a potential to estimate the bone density better than the traditional method.