Brain deformations associated with burr hole and dura opening during deep brain stimulation (DBS) surgeries can significantly affect electrodes' placement, directly impacting optimal treatment response. Enhanced interpretation of clinical outcomes and, in addition, study the effects of shifting electrode leads on neural pathways can be accomplished by coupling patient-specific finite element biomechanical/bioelectric tissue and conventional neurophysiological models. A dataset of six patients who had undergone intraoperative magnetic resonance (iMR)-guided DBS procedure is considered in this study. To realistically predict soft tissue deformation during DBS surgery, biomechanical models were constructed based on patient-specific imaging data and driven with iMR data. In addition, bioelectric finite element models for both undeformed (no shift) and deformed states were used to estimate the effect of electric fields using two conventional neuromodulation prediction approaches. In the first approach, successful neuron pathway recruitment was established using a neurophysiological simulation estimating the likelihood that a given field would influence action potential dynamics. In the second approach, recruitment was based on the direct use of an electric-field norm threshold to establish an activation volume. Results showed about 49% difference in recruited neuronal pathways when comparing neurophysiological models and electric-field norm threshold neural activation model.
Vagus nerve stimulation (VNS) is an effective technique for treating epilepsy, and it is a promising method to treat many other health conditions, such as depression, cardiovascular disease, chronic pain, diabetes, and others. Due to this wide range of applications, many researchers have developed VNS devices and stimulation techniques over the past decades. However, a common practice is to implant an electrode that has a rather broad stimulation field across the vagus nervus (VN) and as a result has limited anatomical specificity and may lead to adverse side effects. The efficacy and breadth of VNS therapy can be improved by selectively modulating only regions associated with a given function. Additionally, enhanced precision should also facilitate uncovering functional vagotopy. In this work, stimulation levels, amount of current injected and electrode configuration are investigated to determine the extent to which activation of the vagus nerve can be precisely controlled. A simple quantitative method to optimize activation is also proposed.
Breast conserving surgery is a common treatment option for early-stage breast cancer, and it relies on complete tumor excision such that no residual cancer is left in the resection cavity. However, the use of preoperative imaging to inform excision is compromised by intraoperative deformations that change the location, volume, and shape of the tumor compared to the imaging configuration. For intra-procedural guidance specifically, incision and retraction alter the tumor presentation and geometry. Being able to compensate for retraction deformations intraoperatively may increase the utility of image guidance technologies. In this work, a breast retraction phantom and deformation modeling approach are developed to explore the potential of modeling retraction for image guidance during BCS. Surface and subsurface beads were embedded in a realistic silicone breast phantom, and CT images were acquired in undeformed and retracted states. A reconstructive, sparse-data registration method was used to model retraction. Modeling accuracy was evaluated by comparing model-predicted and ground-truth bead displacements. The average surface bead registration error after retraction modeling in a region of interest was 0.5 ± 0.1 mm (maximum 0.5 mm). The average subsurface bead registration error in a region of interest was 1.2 ± 0.6 mm (maximum 2.6 mm). A biomechanical modeling method that includes retraction may improve the accuracy of image guidance for breast conserving surgery, but more work is needed to evaluate its utility.
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