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中文摘要
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 描述(由申请人提供):脑损伤的预测指标,如头部损伤标准,依赖于头部运动的外部测量。直接测量大脑运动可以更准确地预测脑损伤。目前估计脑变形的方法不能提供必要的高帧速率来表征与创伤性脑损伤相关的快速瞬变事件。我们开发了新的超声波方法和运动跟踪算法,可以生成高帧速率(高达10,000图像/秒)的电影,以高位移灵敏度(优于1微米)量化大脑运动。我们建议使用这项技术来成像和量化剪切冲击波在体外和体内大脑中的传播。给出了初步数据,首次显示了剪切冲击波在大脑中的传播。剪切冲击波的强烈梯度可能会撕裂和损伤神经元,从而导致弥漫性轴突损伤。我们建议描述大脑的非线性特性,并发展剪切冲击波在大脑中传播的非线性模拟。我们建议动物实验与组织学分析相结合,以确定这些快速事件与损伤之间的联系。我们建议模拟结合头部加速度的测量来预测损伤和脑震荡。如果成功,这项研究可能会改变我们对大脑创伤机制的看法,并将其应用于身体任何部位的创伤。
英文摘要
 DESCRIPTION (provided by applicant): Predictors of brain injury, such as the head injury criterion, rely on external measurements of head motion. Direct measurements of brain motion could more accurately predict brain injury. Current methods of estimating brain deformation fail to provide the high frame-rates necessary to characterize the fast transient events asso- ciated with traumatic brain injury. We have developed novel ultrasonic methods and motion tracking algorithms that can generate high frame-rate (up to 10,000 images/second) movies that quantify brain motion with a high displacement sensitivity (better than 1 micron). We propose to use this technique to image and quantify shear shock wave propagation in the ex vivo and in vivo brain. Preliminary data is presented, showing for the ¿rst time, shear shock wave propagation in the brain. The violent gradients in shear shock waves may tear and damage neurons thus causing diffuse axonal injuries. We propose to characterize the nonlinear properties of the brain and to develop nonlinear simulations of shear shock wave propagation in the brain. We propose animal experiments in conjunction with histological analysis to establish a link between these rapid events and injury. We propose simulations in conjunction with measurements of head acceleration to predict injuries and concussions. If successful, this research could transform how we view the mechanics of trauma in the brain and be applied to traumatic injuries anywhere in the body.
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Lung-specific ultrasound beamforming for diagnostic imaging
Lung-specific ultrasound beamforming for diagnostic imaging
A machine learning ultrasound beamformer based on realistic wave physics for high body mass index imaging
A machine learning ultrasound beamformer based on realistic wave physics for high body mass index imaging
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