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

项目摘要 在美国和全球范围内，导电医疗植入物的使用稳步增长。目前，更多 超过1200万美国人携带某种形式的矫形、心脏或神经调节装置， 每年增长10万。据估计，50-75%的植入患者将受益于磁 核磁共振成像（MRI），有些人反复检查。不幸的是， MRI的射频（RF）场和导电植入物之间的相互作用导致了因RF加热而造成的致命伤害 这使得大多数患者无法使用MRI。作为回应，已作出广泛努力， 量化和减轻MR感应RF加热的问题。根据监管建议， 这些努力严重依赖于对MRI RF线圈的细节进行建模的全波电磁（EM）模拟， 人体和植入物，因此是非常麻烦。即使利用今天的高点- 功率计算集群通常需要几十个小时来完成单个模拟。我们的长期目标是 为了能够将计算机医学应用于真实的植入物的RF加热评估和患者- 患者基础。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， 准确预测细长植入物（如电极导线）的射频致热，当只有 核磁共振射频线圈和植入物的轨迹都在掌握中背景RF场是存在于电磁场中的场。 在没有植入设备的情况下，可以很容易地预先计算出任何已知的MRI线圈。 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。在此， 我们建议开发、优化和实验验证一种深度学习方法， 在1.5 T和3 T下使用体线圈进行MRI期间DBS系统发热，误差<2℃。我们将建立培训 来自500个患者DBS电极导线模型的数据集，应用EM模拟计算真实射频产热 使用供应商提供的MRI RF线圈模型，并开发深度学习算法来预测RF发热 仅了解植入物轨迹（基于CT）和线圈特征（供应商- 具体）。如果成功，我们的工作将在MRI射频致热评估实践中引入范式转变， 将模拟时间从数十小时缩短至几分钟。这将使一种目前 只有少数资源充足的公司才能提供，并为大量新颖的植入物打开了大门。 设计和患者特定的安全指南。重要的是，在这项创新工作中获得的知识可以 将其转化为其他类型植入物的患者，特别是心脏植入式电子植入物的患者 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.