Noninvasive transcranial detection of intracranial hemorrhage using a tri-coil handheld portable eddy current damping imaging device

项目总结：使用三线圈手持设备进行颅内出血的无创经颅检测 便携式涡流阻尼成像装置 中风和创伤性脑损伤（TBI）是全球死亡和永久残疾的常见原因， 美国卫生系统每年损失超过710亿美元的生产力和医疗费用。现有 中风/TBI诊断的范例需要计算机断层扫描（CT）或磁共振（MR）成像， 在介入治疗前对缺血性与出血性变异进行分类，因为这些疾病的治疗方法不同 广泛地。诊断延迟和与诊断相关的不稳定患者的运输相关的问题 影像学检查会增加神经损伤和死亡的可能性。转化型医疗器械加速 在现场或医院环境中的治疗时间可能有助于降低卒中/TBI患者的发病率和死亡率 在全球范围内。我们的团队开发了一种便携式，快速和非侵入性的成像和检测设备， 涡流阻尼（ECD）传感器可以检测与中风和TBI相关的脑出血，以及 已经证明了在台式、人尸体和临床患者环境中的可行性。这个装置可以 在几分钟内通过准确的空间定位潜在地诊断和分类出血性中风/TBI亚型， 而不是几个小时，从而指导早期响应者和医疗提供者进行时间敏感的医疗 临床干预的决定，如缺血性中风的组织纤溶酶原激活剂的管理。我们 总体目标是证明这种新型卒中检测设备在卒中/TBI快速分类中的有效性 患者，并达到能够指导临床干预的诊断准确性水平。我们假设 1）使用便携式ECD可以成像和检测脑组织中的区域电导率变化 2）出血性中风和TBI相关的脑血管病将增加区域传导性，而 缺血性中风将降低受影响的缺血区域中的依赖性脑电导率;以及3）便携式中风 成像可以缩短与中风/TBI相关的治疗和诊断时间。为了验证这些假设，我们 目的：1）进行台式实验室实验，以进一步阐明如何方向和幅度 测量的电导率变化可以区分中风亚型和位置，2）使用经验证的人尸体 卒中模拟模型，以优化ECD三线圈阵列传感器检测出血深度、体积和 定位，3）利用机器学习算法以高精度快速分类脑病变，以及4） 实施早期临床中风/TBI ECD传感器设备测试，以评估活体人类患者的有效性， 与CT/MR成像相比。快速床边卒中/TBI诊断方法的开发将提供 具有快速实施救生治疗所需知识的从业人员，从而改善患者 生活质量和生存。从中获得的知识将有助于缩短治疗时间并指导分诊 和干预，这可能会转化为与这些常见疾病相关的发病率和死亡率的改善。 条件

Project Summary: Noninvasive transcranial detection of intracranial hemorrhage using a tri-coil handheld portable eddy current damping imaging device Stroke and traumatic brain injury (TBI) are common causes of death and permanent disability worldwide, costing the U.S. health system more than $71 billion per year in lost productivity and medical expenses. Existing paradigms for stroke/TBI diagnosis require computed tomography (CT) or magnetic resonance (MR) imaging to classify ischemic versus hemorrhagic variants prior to intervention, as treatment for these conditions varies widely. Delays in diagnosis and issues related to transport of unstable patients associated with diagnostic imaging increase the likelihood of neurological injury and death. Translational medical devices that accelerate time-to-treatment in the field or hospital setting may help to reduce morbidity and mortality in stroke/TBI patients on a global level. Our team has developed a portable, rapid and noninvasive imaging and detection device based on eddy current damping (ECD) sensors that can detect brain hemorrhages associated with stroke and TBI, and have demonstrated feasibility in a benchtop, human cadaver and clinical patient setting. This device can potentially diagnose and classify hemorrhagic stroke/TBI subtypes with accurate spatial localization in minutes, rather than hours, thereby guiding early responders and medical providers in making time-sensitive medical decisions for clinical intervention, such as administration of tissue plasminogen activator for ischemic stroke. Our overall goal is to demonstrate the effectiveness of this novel stroke detection device in rapidly triaging stroke/TBI patients and achieving a level of diagnostic accuracy capable of guiding clinical intervention. We hypothesize that: 1) Regional conductivity changes in brain tissue can be imaged and detected using the portable ECD sensor; 2) Hemorrhagic stroke and TBI-related hemorrhages will increase regional conductivity, whereas ischemic stroke will decrease dependent brain conductivity in affected ischemic regions; and 3) Portable stroke imaging may reduce time-to-treatment and diagnosis associated with stroke/TBI. To test these hypotheses, we aim to: 1) Perform benchtop laboratory experiments to further elucidate how direction and magnitude of measured conductivity changes can differentiate stroke subtype and location, 2) Use validated human cadaver stroke simulation models to optimize ECD tri-coil array sensor detection of hemorrhage depth, volume, and location, 3) Utilize machine learning algorithms to quickly classify brain lesions with high accuracy, and 4) Implement early clinical stroke/TBI ECD sensor device testing to gauge effectiveness in live human patients, compared to CT/MR imaging. Development of methods for rapid bedside stroke/TBI diagnosis will provide practitioners with knowledge required to rapidly administer life-saving treatments, thereby improving patient quality of life and survival. Knowledge derived from this will help to reduce time-to-treatment and guide triage and intervention, which is likely to translate to improved morbidity and mortality associated with these common conditions.