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

应用机器学习快速预测植入导电导线患者的 MRI 引起的射频加热

基本信息

批准号：
10611468
负责人：
Ulas Bagci
金额：
$ 7.07万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10611468
关键词：
Address Algorithms American Cardiac Coiled Bodies Consumption Data Set Dedications Deep Brain Stimulation Democracy Devices Electromagnetic Energy Electromagnetics Electronics Fatal injury Friends Goals Grant Growth Guidelines Hand Head Heating Hour Human body Image Implant Knowledge Lead Length Machine Learning Magnetic Resonance Imaging Measurement Measures Medical Imaging Medicine Memory Methodology Modeling Orthopedics Outcome Output Patients Pilot Projects Postoperative Period Procedures Public Health Recommendation Resources Risk Assessment Safety Sampling Spinal Cord Structure System Techniques Temperature Testing Time Training Translating Uncertainty United States National Institutes of Health Validation Vendor Work X-Ray Computed Tomography blind capsule cardiac implant cluster computing deep learning deep learning algorithm electric field implant design implantable device in silico innovation learning strategy machine learning algorithm medical implant models and simulation neuroregulation novel radio frequency response simulation tool

项目摘要

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.

项目概要目前，导电医疗植入物的使用在美国和全球范围内稳步增长。超过 1200 万美国人携带某种形式的矫形、心脏或神经调节设备，并且数量据估计，50-75% 的植入患者将受益于磁性。磁共振成像（MRI）在他们的一生中，有些与重复的检查不幸地相互作用。 MRI 射频 (RF) 场和导电植入物之间的射频加热导致致命伤害植入物的数量过多，使得大多数患者无法进行 MRI 扫描。为此，人们付出了巨大的努力。遵循监管建议，量化并缓解 MR 引起的射频加热问题。这些在很大程度上依赖于对 MRI RF 线圈的细节进行建模的全波电磁 (EM) 模拟，即使利用当今的高技术，人体和植入物也是出了名的麻烦。电力计算集群通常需要数十个小时才能完成一次模拟，我们的长期目标是。能够应用计算机模拟医学对植入物进行实时射频加热评估，并对患者进行评估我们的主要假设是测试先进的深度学习 (DL) 方法是否可以快速且有效地进行。当只有背景电场时，准确预测细长植入物（例如引线）的射频加热 MRI 射频线圈和植入物的轨迹尽在掌握。背景射频场是存在于体内的场。在没有植入设备的情况下，可以轻松地提前计算出任何已知的 MRI 线圈。同样，只需几分钟即可从常规医学图像中提取植入物的轨迹。我们建议开发、优化并通过实验验证一种预测 RF 的深度学习方法在 MRI 期间使用 1.5 T 和 3 T 的身体线圈加热 DBS 系统，误差 <2℃ 我们将建立训练。来自 500 个患者衍生的 DBS 主要模型的数据集，应用电磁模拟来计算地面真实射频加热使用供应商提供的 MRI 射频线圈模型，并开发深度学习算法来预测射频加热精度为 2℃，仅了解植入物的轨迹（基于 CT）和线圈的特征（供应商）如果成功，我们的工作将在 MRI 射频加热评估实践中引入范式转变，将模拟时间从几十小时减少到几分钟，这将使当前的实践民主化。仅由少数资源充足的公司提供，并为大量新颖的植入物打开了大门重要的是，在这项创新工作中获得的知识可以。可以转化为具有其他类型植入物的患者，特别是那些具有心脏植入式电子设备的患者设备和脊髓刺激器。