The Importance of Sensor Placement During the Measurement of RF Heating of Implants in MRI

Abstract

This study investigates the importance of accurate sensor placement when assessing implant safety during MRI scans experimentally. A combination of measurement and finite element modelling was used to assess sensitivity of measurement to sensor placement, for temperature rises at the ends of a set of calibration cylinders. The simulations used a coupled thermal-electromagnetic model created using COMSOL Multiphysics to replicate the measurement conditions virtually. Thermal gradients in parametric models of cylindrical implants of varying length and diameter were evaluated to quantify the sensor placement accuracy required for the measurement of implant heating within the estimated temperature measurement uncertainty. In this way we aim to enhance the understanding of the requirements for experimental procedures and safety standards dealing with implant heating in MRI.

Applications of Microwaves in Medicine

“…Since these implantable electronics are metallic in nature, unwanted heating and hot spots must be studied to prevent injury to the patient. Significant studies have been conducted showing various techniques such as specific placement or decoy elements and show varying degrees of heating and minimization and image impact [333], [334], [335], [336], [337], [338], [339], [340], [341], [342], [343], [344]. These studies include looking at the impact of electromagnetic interactions with implanted lead wires [337], helical stents [338], catheters [339], as well as a wide range of other implantable devices [342], [343], [344].…”

“…As computing power increases, machine learning, artificial intelligence, and numerical algorithms and techniques are being increasingly applied to electromagnetics problems, and the MRI arena is no exception. As examples of applications for these computing tools to MRI, significant works have recently used these tools as ways to increase the speeds of image processing [344], [345], [346] so that images can be obtained more quickly, with the goal of imaging movement such as blood flow [347], [348] or for correcting body motion occurring during scanning [349]. An example of improved image quality is by using a deep-learning training approach [346].…”

“…Recent papers looked at the intrinsic performance of RF coils through a numerical study that should provide a metric for MRI designers to compare coil quality across manufacturers and customers [321], [323], [342]. Increased computing power also allows researchers to electromagnetically model the human body to predict unwanted effects such as heating, hot spots, or wavelength-dependent light and dark regions in the images [333], [334], [335], [336], [337], [338], [339], [340], [341], [342], [343], [344], [350].…”