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.

项目摘要在美国和全球，使用导电医学侵入的使用稳定增长。目前，更多超过1200万美国人携带一种骨科，心脏或神经调节装置的形式，数量每年增长100,000。据估计，有50-75％的玻璃患者将受益于磁性共振成像（MRI）一生，有些重复检查。不幸的是，互动在MRI的射频（RF）磁场和导电性降低之间导致了由于RF加热而导致致命伤害大多数患者的MRI无法访问。作为回应，大量的努力已致力于量化和减轻MR诱导的RF加热问题。按照监管建议，这些努力在很大程度上依赖全波电磁（EM）模拟，这些模拟模拟了MRI RF线圈的细节，人体和植入物，因此众所周知。甚至利用当今的高级电源计算集群通常需要数十个小时才能完成单个模拟。我们的长期目标是能够实时应用硅胶医学来实时和患者的螺想症评估 - 副患者。我们的主要假设是测试先进深度学习（DL）方法是否可以迅速而准确预测伸长的刷子的RF加热（例如铅），仅当背景电场的电场 MRI RF线圈和植入物的轨迹在手中。背景RF字段是存在于在没有植入装置的情况下，可以在任何已知的MRI线圈中预先计算。同样，只需几分钟即可从常规的医疗图像中提取植入物的轨迹。在此处，我们建议开发，优化和实验验证一种预测RF的深度学习方法 MRI期间的DBS系统在1.5 t和3 t的MRI过程中加热，误差<2 t和3 t。我们将建立培训来自500个患者衍生的DBS铅模型的数据集，应用EM模拟来计算地面真相RF加热使用MRI RF线圈的供应商提供的模型，并开发深度学习算法以预测RF加热具有2℃精度，仅了解植入物的轨迹（基于CT）和线圈的特征（供应商 - 具体的）。如果成功，我们的工作将引入MRI RF供暖评估实践的范式转变，将模拟时间从数十小时减少到几分钟。这将民主化目前的做法仅由少数资源丰富的公司提供，并为大量新型植入物打开了大门设计和特定于患者的安全指南。重要的是，这项创新工作中获得的知识可以将其转化为具有其他类型的直接的患者，尤其是那些具有心脏植入电子的患者设备和脊髓刺激剂。