Application of machine learning for fast prediction of MRI-induced RF heating in patients with implanted conductive leads

项目摘要 在美国和全球，传导性医疗植入物的使用正在稳步增长。目前，更多 超过1200万美国人携带整形外科、心脏或神经调节设备，而这一数字 以每年10万的速度增长。据估计，50%-75%的植入物患者将受益于磁力 在他们的一生中进行磁共振成像(MRI)，有些需要反复检查。不幸的是，这种互动 磁共振射频(RF)场和导电植入物之间由于射频加热而导致致命伤害 这使得大多数患者无法获得核磁共振成像。作为回应，我们付出了广泛的努力来 量化和缓解MR引起的射频加热问题。根据监管机构的建议， 这些努力在很大程度上依赖于模拟磁共振射频线圈细节的全波电磁(EM)模拟， 人体和植入物，因此是出了名的繁琐。即使利用今天的高价- 计算集群通常需要几十个小时才能完成一次模拟。我们的长期目标是 为了能够对植入物进行实时和在患者身上进行射频加热评估的硅内医学应用- 以病人为单位。我们的主要假设是测试高级深度学习(DL)方法是否可以快速和 准确预测细长植入体(如引线)的射频加热，当仅当 核磁共振射频线圈和植入物的轨迹已经掌握。背景射频场是存在于 在没有植入装置的情况下，可以很容易地为任何已知的MRI线圈预先计算。 同样，植入物的轨迹可以在几分钟内从常规医学图像中提取出来。在这里， 我们建议开发、优化和实验验证一种预测射频的深度学习方法 在磁共振成像过程中使用1.5T和3T的体线圈加热DBS系统，并有&lt；2℃误差。我们将建立培训 来自500名患者的DBS导联模型的数据集，应用EM模拟计算地面真实射频加热 使用供应商提供的磁共振射频线圈模型，并开发深度学习算法来预测射频加热 2℃精度，只知道植入物的轨迹(基于CT)和线圈的特征(供应商- 具体)。如果成功，我们的工作将在磁共振射频加热评估的实践中引入一种范式转变， 将模拟时间从几十个小时减少到几分钟。这将使目前的一种做法民主化 仅由少数资源充足的公司提供，并为过多的新型植入物打开了大门 设计和针对患者的安全指南。重要的是，从这项创新工作中获得的知识可以 翻译给其他类型的植入物的患者，特别是那些心脏可植入电子设备的患者 设备和脊髓刺激器。

Project Summary There is a steady growth in the use of conductive medical implants in the US and globally. Currently, more than 12 million Americans carry a form of orthopedic, cardiac, or neuromodulation device, and the number grows by 100,000 annually. It is estimated that 50-75% of patients with implants would benefit from magnetic resonance imaging (MRI) during their lifetime, some with repeated examinations. Unfortunately, the interaction between MRI's radiofrequency (RF) fields and conductive implants have led to fatal injuries due to RF heating of implants, making MRI inaccessible to most patients. In response, extensive effort has been dedicated to quantifying and mitigating the problem of MR-induced RF heating. Following regulatory recommendations, these efforts heavily rely on full-wave electromagnetic (EM) simulations that model details of MRI RF coils, human body, and implant, and as such are notoriously cumbersome. Even taking advantage of today's high- power computing clusters it typically takes tens of hours to complete a single simulation. Our long-term goal is to enable application of in-silico medicine for RF heating assessment of implants in real time and on a patient- by-patient basis. Our main hypothesis is to test whether advanced deep learning (DL) methods can rapidly and accurately predict RF heating of elongated implants (such as leads), when only the background electric field of the MRI RF coil and the implant's trajectory are in hand. The background RF field is the field that exists in the body in the absence of the implanted device and can be easily calculated in advance for any known MRI coil. Similarly, the implant's trajectory can be extracted from routine medical images in only a few minutes. Herein, we propose to develop, optimize, and experimentally validate a deep learning approach that predicts RF heating of DBS systems during MRI with body coils at both 1.5 T and 3 T with <2℃ error. We will build training datasets from 500 patient-derived DBS lead models, apply EM simulations to calculate ground truth RF heating using vendor-provided models of MRI RF coils, and develop deep learning algorithms to predict the RF heating with 2℃ accuracy with knowledge of only the implant's trajectory (CT-based) and the coil's features (vendor- specific). If successful, our work will introduce a paradigm shift in the practice of MRI RF heating assessment, reducing simulation times from tens of hours to a few minutes. This will democratize a practice that is currently afforded by only a handful of well-resourced companies and opens the door to a plethora of novel implant designs and patient-specific safety guidelines. Importantly, the knowledge gained in this innovative work can be translated to patients with other types of implants, especially those with cardiac implantable electronic devices and spinal cord stimulators.