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

项目摘要：手持三圈线圈无创检测颅内出血 便携式涡流减振成像装置 中风和创伤性脑损伤是世界范围内导致死亡和永久性残疾的常见原因， 美国医疗系统每年损失的生产力和医疗费用超过710亿美元。现有 中风/脑外伤诊断的范例需要计算机断层扫描(CT)或磁共振(MR)成像来 在干预前对缺血型和出血性变异型进行分类，因为这些情况的治疗方法各不相同 广泛地。诊断延误和运送与诊断相关的不稳定患者的问题 成像增加了神经损伤和死亡的可能性。加速的转化型医疗设备 在现场或医院环境下的治疗时间可能有助于降低中风/脑外伤患者的发病率和死亡率 在全球层面上。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 关于涡流衰减(ECD)传感器，可以检测与中风和脑外伤有关的脑出血，以及 已经在台式电脑、人体身体和临床患者环境中证明了可行性。该设备可以 在几分钟内通过精确的空间定位潜在地诊断和分类出血性中风/脑损伤亚型， 而不是几个小时，从而指导早期响应人员和医疗提供者制定时间敏感的医疗 临床干预的决定，如对缺血性中风给予组织型纤溶酶原激活剂。我们的 总体目标是证明这种新的卒中检测设备在快速分诊卒中/脑损伤方面的有效性 并达到能够指导临床干预的诊断准确率水平。我们假设 这是：1)使用便携式ECD可以对脑组织的局部电导率变化进行成像和检测 传感器；2)出血性卒中和脑外伤相关出血将增加区域电导率，而 缺血性卒中将降低受影响缺血区的依赖脑电导率；以及3)便携式卒中 成像可能会减少中风/脑外伤相关的治疗和诊断时间。为了检验这些假设，我们 目的：1)进行台式实验室实验，以进一步阐明 测量的电导率变化可以区分中风的亚型和部位，2)使用经过验证的人类身体 卒中模拟模型优化ECD三线圈阵列传感器检测出血深度、体积和 定位，3)利用机器学习算法快速高精度地对脑部病变进行分类，以及4) 实施早期临床中风/脑损伤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。床边中风/脑外伤快速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医生，从而改善患者 生活质量和生存。由此获得的知识将有助于减少治疗时间和指导分诊 和干预，这很可能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.