Introduction

Deep brain stimulation (DBS) is a therapeutic strategy approved for the treatment of movement disorders [1]. Despite the success of DBS, the mechanisms of action of DBS are not well understood, which is slowing its translation to psychiatric disorders [2]. High-field (3T) fMRI is the ideal modality to characterize brain functional network modulations due to DBS, but is currently contra-indicated due to the risk of RF-induced heating. We propose a “virtual population” of five realistic DBS patient models for simulation of this effect. Our models are high-quality, watertight, topologically correct, non-intersecting surface meshes that can be used in conjunction with Finite Element Method (FEM) tools such as Ansys HFSS and CST.

Methods

Patients & IRB: Under an approved protocol, we searched the Research Patient Data Registry system of our institution for DBS patients who received head, neck or abdomen CT examinations for reasons either related or unrelated to their DBS condition. 107 patients were found, including 7 hospital employees who were excluded. 10 patients were selected who had received both a head and neck CT scan after the DBS implantation. Five patients were further selected because of the superior quality of their CT images (4 males, 1 female. Age 52 ± 27.7 y.o, with minimum of 19 and maximum of 79 y.o). Virtual CT: Only one of the 5 patients received a CT examination covering the entire length of the DBS implant (i.e., from IPG to top of the head). For all other patients, extraction of the entire DBS implant model required stitching the head and neck CTs. This was performed by manual registration using Freeview. The stitched CT image was down-sampled from 0.625 mm isotropic to 1 mm isotropic. Creation of the DBS implant model: Creation of the DBS model from the virtual CT volume was performed as described in [3]. The steps involved are summarized in Figs. 1&2. As shown in Fig. 3, the central process is a previously-published optimization procedure that automatically deforms the CT-derived DBS path in order to guarantee that 1) the curvature is smaller than 1/R at every point along the path and 2) that the distance between any two segments of the path is greater than R, thus removing cable intersections (R is the DBS cable radius). We modeled a generic DBS lead model, with four 1 mm-long, 1.27 mm-diameter electrodes, based on lead model 3389 (Medtronic Inc., Minneapolis, MN) . For simplification, we did not model the helicoidal structure of the internal conductor wires but four straight cables running parallel to the main path. Creation of the body surface mesh: Robust generation of body mesh models was performed following the steps outlined in Fig. 4. Manual cleanup of the segmented volume (Fig. 4, STEP #3) was by far the most time consuming step, requiring an entire day of work per patient. All steps were performed in Matlab using custom code, except for STEP #5 which was performed using MeshMixer [4]. The final step was performed using the methodology detailed in abstract #3964 [5]. Dissemination: Our goal is to disseminate these models as widely as possible. Our IRB currently prevents us from posting the models online however -- to obtain them, please contact Bastien Guerin guerin@nmr.mgh.harvard.edu.

Results

Fig. 5 shows four of the five body models (the last one is still in preparation). Left/right reconstructed DBS paths length (in mm) were 610/602 (patient A), 595/590 (patient B), 719/625 (patient C) and 862/879 (patient D). In theory, depending on the exact extension cable used by the neurosurgeon, total cable lengths should be either 600 mm or 800 mm. Deviations from these reference values are due to path simplifications, for example at the extension cable-IPG connection point. All models were successfully loaded and analyzed within Ansys HFSS, showing no topology problems or simulation errors.

Conclusion

We have initiated the assembly of a “virtual population” of DBS patient models to assist with MRI safety evaluation in this patient cohort. Although we strived to produce highly realistic models, these are necessary simplifications of actual patients. Nevertheless, the models represent the “next step” in DBS modeling as they allow the assessment of inter-subject variability of RF-induced heating, which is often missing in the literature [6-12].

Acknowledgements

NIH grants K99/R00 EB019482. The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.

References

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