Limb muscle sound speed estimation by ultrasound computed tomography excluding receivers in bone shadow
Limb muscle sound speed estimation by ultrasound computed tomography excluding receivers in bone shadow
复制标题
通过超声计算机断层扫描估计肢体肌肉声速，排除骨影中的接收器
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发表时间：
2017
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通讯作者：
I. Sakuma
中科院分区：
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作者：
Xiaolei Qu;T. Azuma;Hongxiang Lin;H. Takeuchi;K. Itani;S. Tamano;S. Takagi;I. Sakuma
肌肉减少症是与衰老相关的骨骼肌能力的退行性丧失。原因之一是肌肉的脂肪比例增加，这可以通过音速（SOS）来估计，因为肌肉和脂肪的SOS不同（约7%）。对于 SOS 成像，传统的弯曲射线方法迭代地寻找射线路径并通过传播时间校正沿路径的 SOS。然而，对于内部有骨骼的软组织，由于速度变化较大，迭代很难收敛。在本研究中，对弯曲射线方法进行了修改，以生成内部有骨骼的肢体肌肉的 SOS 图像。修改后的方法包括三个步骤。首先，通过提出的 Akaike 信息准则 (AIC) 和能量项 (AICE) 方法来获取旅行时间。能量项用于检测和丢弃穿过骨骼的透射波（低能量波）。它导致骨骼重建失败，但使迭代收敛并为骨骼肌提供正确的 SOS。其次，利用费马原理追踪光线路径。最后，采用同时代数重建技术（SART）来校正沿射线路径的SOS，但排除可能穿过骨骼的低能量波路径。仿真评估是通过k-wave工具箱使用上臂模型来实现的。结果，肌肉SOS为1572.0±7.3 m/s，接近模型中的1567.0 m/s。为了进行体内评估，使用环形传感器原型来扫描健康志愿者的下臂和腿部的横截面。骨骼肌SOS分别为1564.0±14.8 m/s和1564.1±18.0 m/s。
Sarcopenia is the degenerative loss of skeletal muscle ability associated with aging. One reason is the increasing of adipose ratio of muscle, which can be estimated by the speed of sound (SOS), since SOSs of muscle and adipose are different (about 7%). For SOS imaging, the conventional bent-ray method iteratively finds ray paths and corrects SOS along them by travel-time. However, the iteration is difficult to converge for soft tissue with bone inside, because of large speed variation. In this study, the bent-ray method is modified to produce SOS images for limb muscle with bone inside. The modified method includes three steps. First, travel-time is picked up by a proposed Akaike Information Criterion (AIC) with energy term (AICE) method. The energy term is employed for detecting and abandoning the transmissive wave through bone (low energy wave). It results in failed reconstruction for bone, but makes iteration convergence and gives correct SOS for skeletal muscle. Second, ray paths are traced using Fermat’s principle. Finally, simultaneous algebraic reconstruction technique (SART) is employed to correct SOS along ray paths, but excluding paths with low energy wave which may pass through bone. The simulation evaluation was implemented by k-wave toolbox using a model of upper arm. As the result, SOS of muscle was 1572.0±7.3 m/s, closing to 1567.0 m/s in the model. For vivo evaluation, a ring transducer prototype was employed to scan the cross sections of lower arm and leg of a healthy volunteer. And the skeletal muscle SOSs were 1564.0±14.8 m/s and 1564.1±18.0 m/s, respectively.