The ‘virtual DBS population’: five realistic computational models of deep brain stimulation patients for electromagnetic MR safety studies

Abstract

We design, develop, and disseminate a ‘virtual population’ of five realistic computational models of deep brain stimulation (DBS) patients for electromagnetic (EM) analysis. We found five DBS patients in our institution’ research patient database who received high quality post-DBS surgery computer tomography (CT) examinations of the head and neck. Three patients have a single implanted pulse generator (IPG) and the two others have two IPGs (one for each lead). Moreover, one patient has two abandoned leads on each side of the head. For each patient, we combined the head and neck volumes into a ‘virtual CT’, from which we extracted the full-length DBS path including the IPG, extension cables, and leads. We corrected topology errors in this path, such as self-intersections, using a previously published optimization procedure. We segmented the virtual CT volume into bones, internal air, and soft tissue classes and created two-manifold, watertight surface meshes of these distributions. In addition, we added a segmented model of the brain (grey matter, white matter, eyes and cerebrospinal fluid) to one of the model (nickname Freddie) that was derived from a T1-weighted MR image obtained prior to the DBS implantation. We simulated the EM fields and specific absorption rate (SAR) induced at 3 Tesla by a quadrature birdcage body coil in each of the five patient models using a co-simulation strategy. We found that inter-subject peak SAR variability across models was independent of the target averaging mass and equal to ~45%. In our simulations of the full brain segmentation and six simplified versions of the Freddie model, the error associated with incorrect dielectric property assignment around the DBS electrodes was greater than the error associated with modeling the whole model as a single tissue class. Our DBS patient models are freely available on our lab website (Webpage of the Martinos Center Phantom Resource 2018 https://phantoms.martinos.org/Main_Page).

Parallel transmission to reduce absorbed power around deep brain stimulation devices in MRI: Impact of number and arrangement of transmit channels

“…Our results show that pTx coils with 16 channels allow reduction of the APAE at constant FA uniformity by more than 169× for all slice orientations. Importantly, this result was statistically significant, which we assessed by modeling N = 5 realistic DBS patient models developed previously by our group . Using our estimation of the APAE variability across the N = 5 realistic DBS patient models, we found the following statistically significant coil ranking relationships: 1) Using 2 rows of 8 channels is better than using a single row of 8 pTx channels, and 2) head pTx coils perform better than body pTx coils.…”

“…The IPG was modeled as a rectangular box with size 1 inch × 1 inch × 0.25 inch. The CT volumes were segmented into 3 classes (internal air, soft tissue, and bone) using the open‐source software 3D Slicer followed by manual cleanup . Segmented tissue voxel volumes were then meshed into 2‐manifold, watertight surface meshes using a previously described procedure .…”

“…Tetrahedron edge lengths were constrained to <15 mm inside the body volume (all tissue classes), except in a rectangular volume surrounding both electrode tips and in smaller cylinders closely surrounding the electrode tips, where the maximum edge length constraints were set to 3 mm and 0.5 mm, respectively (see Ref. 42 for additional details). Mesh operations were applied during the initial meshing of the model by Ansys Electronics and then refined iteratively until reaching convergence of the S‐parameters (5% convergence tolerance) as well as the absorbed power around electrodes (APAE) metric (3% convergence tolerance).…”

“…Mesh operations were applied during the initial meshing of the model by Ansys Electronics and then refined iteratively until reaching convergence of the S‐parameters (5% convergence tolerance) as well as the absorbed power around electrodes (APAE) metric (3% convergence tolerance). The APAE is the absorbed power around the electrodes, is expressed in Watts, and is integrated over small cylindrical volumes surrounding the left and right electrodes …”

“…The CT volumes were segmented into 3 classes (internal air, soft tissue, and bone) using the opensource software 3D Slicer 43 followed by manual cleanup. 42 Segmented tissue voxel volumes were then meshed into 2-manifold, watertight surface meshes using a previously F I G U R E 1 (A) Five DBS patient models simulated in this work. All patients are implanted bilaterally; that is, they have a DBS lead implanted on the left and right hemispheres.…”