Ultrasound-based sensors to monitor physiological motion.

Ultrasound-based sensors to monitor physiological motion.
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DOI:
10.1002/mp.14949
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发表时间:
2021-07
期刊:
影响因子:
3.8
通讯作者:
Cheng CC
Cheng CC
中科院分区:
医学3区
文献类型:
--
作者:
Madore B;Preiswerk F;Bredfeldt JS;Zong S;Cheng CC

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在不断移动的解剖结构上进行医疗手术可能很困难。呼吸使内脏器官相对于身体表面移位达几厘米,并且患者屏住呼吸的能力通常有限。在诊断和治疗过程中补偿运动的策略需要提供可靠的信息。然而,当前的设备通常通过身体轮廓的变化来间接监测呼吸,并且它们可能固定在地板或天花板上,因此无法跟踪特定患者的不同位置。在这里,我们展示了被称为“器官配置运动”(OCM)传感器的小型超声波传感器可以固定在腹部和/或胸部,并提供信息丰富的呼吸相关信号。通过设计,所提出的传感器相对便宜。呼吸波形是从不同深度的组织和/或使用不同的传感器位置获得的。分别对五名和八名志愿者的来自 MRI 和光学跟踪信号的呼吸波形进行了验证。对不同模式的呼吸波形进行了缩放,以便可以直接进行比较。与典型呼吸的幅度相比,波形之间的差异以百分比的形式表示。以这种方式表达,对于浅层组织,OCM 衍生的波形平均与 MRI 和光学跟踪结果分别相差 13.1% 和 15.5%。目前的结果表明,所提出的传感器提供了正确表征呼吸状态的测量结果。虽然来自浅层组织的基于 OCM 的波形在信息内容方面与来自 MRI 或光学跟踪的波形相似,但 OCM 进一步捕获了深度相关和位置相关(即胸部和腹部)信息。随着时间的推移,基于 OCM 的波形的更丰富的信息内容可能会实现更好的呼吸门控,从而使诊断和治疗设备发挥最佳性能。
Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as ‘organ configuration motion’ (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing related signals. By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from MRI and optical tracking signals, in five and eight volunteers, respectively. Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.
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