Deep brain stimulation (DBS) of the human brain is a remarkable technique that can treat the symptoms of several major debilitating neurological and psychiatric disorders. While the clinical utilities of DBS have grown exponentially, its underlying therapeutic mechanisms of action remain controversial. There are uncertainties about the circuits that are affected, the exact fiber bundles that need to be targeted, and the most effective stimulation protocol. Due to its superb soft tissue contrast and high-resolution visualization of the brain's anatomy, magnetic resonance imaging (MRI) is excellently poised to address these questions about the targeting and mechanisms of DBS. Unfortunately, the interactions between the radiofrequency (RF) fields of MRI scanners and DBS leads can result in restrictive safety hazards that limit the accessibility of MRI for patients with DBS implants. One major safety concern is known as the ?antenna effect,? wherein the electric field of a MRI transmit coil couples with conductive implanted leads and amplifies energy deposition in the tissue. In turn, this can lead to excessive heating that can damage the examined tissue. While novel approaches have been suggested to reduce these safety risks through the modification of MRI hardware and the optimization of DBS implantation path, the clinical implementation of these practices has been hindered due to a lack of quantitative measurements on tissue heating in realistic patient populations. This proposal aims to develop, optimize, and validate computational methodologies that can consistently and reliably predict tissue heating in patients with DBS implants during high-field MRI scanning. The methodologies that will be developed and validated in this work will provide a toolset for the holistic exploration of millions of variable combinations and allow for the analyses of parameter values that exist outside the bounds of normal clinical practice. These resources will prove critical to the assessment of safety margins and to the evaluation of sensitivities of specific results to individual variables. As a first step, we will develop a repository of 20 patient- derived realistic models of DBS leads that incorporate detailed features of lead structures and trajectories, and register these items in a high-resolution and anatomically detailed model of the human head and neck. We will then use these patient-derived head and lead models, along with experimentally validated models of MRI transmit coils, and perform full-wave electromagnetic simulations to calculate temperature increases in brain tissue during MRI scans at 1.5 T and 3.0 T. The dependency and sensitivity of the results to certain parameters, such as the RF coil's frequency (64 MHz vs. 127 MHz), geometry (body coil vs. head coil), and mode of operation (linear vs. quadrature), will be evaluated. The effect of the head models' characteristics (number of tissues and electric and thermal properties) and DBS lead trajectory (looped vs. straight) will be examined. We anticipate the formation of a new approach to MRI safety assessments based on computational modeling that will be employed in a broad spectrum of applications, including MRI safety assessments for patients with cardiac leads and spinal cord stimulators.
This project aims to develop computational methodologies that consistently and reliably predict the heating of brain tissue during magnetic resonance imaging of patients with deep brain stimulation (DBS) implants. Results will produce highly relevant data that will support the development of standards and regulatory guidelines to determine safe range of imaging parameters and optimize clinical imaging protocols for patients with DBS implants.
