Automated Steerable Path Planning for Deep Brain Stimulation Safeguarding Fiber Tracts and Deep Gray Matter Nuclei

Abstract

Deep Brain Stimulation (DBS) is a neurosurgical procedure consisting in the stereotactic implantation of stimulation electrodes to specific brain targets, such as deep gray matter nuclei. Current solutions to place the electrodes rely on rectilinear stereotactic trajectories (RTs) manually defined by surgeons, based on pre-operative images. An automatic path planner that accurately targets subthalamic nuclei (STN) and safeguards critical surrounding structures is still lacking. Also, robotically-driven curvilinear trajectories (CTs) computed on the basis of state-of-the-art neuroimaging would decrease DBS invasiveness, circumventing patient-specific obstacles. This work presents a new algorithm able to estimate a pool of DBS curvilinear trajectories for reaching a given deep target in the brain, in the context of the EU's Horizon EDEN2020 project. The prospect of automatically computing trajectory plans relying on sophisticated newly engineered steerable devices represents a breakthrough in the field of microsurgical robotics. By tailoring the paths according to single-patient anatomical constraints, as defined by advanced preoperative neuroimaging including diffusion MR tractography, this planner ensures a higher level of safety than the standard rectilinear approach. Ten healthy controls underwent Magnetic Resonance Imaging (MRI) on 3T scanner, including 3DT1-weighted sequences, 3Dhigh-resolution time-of-flight MR angiography (TOF-MRA) and high angular resolution diffusion MR sequences. A probabilistic q-ball residual-bootstrap MR tractography algorithm was used to reconstruct motor fibers, while the other deep gray matter nuclei surrounding STN and vessels were segmented on T1 and TOF-MRA images, respectively. These structures were labeled as obstacles. The reliability of the automated planner was evaluated; CTs were compared to RTs in terms of efficacy and safety. Targeting the anterior STN, CTs performed significantly better in maximizing the minimal distance from critical structures, by finding a tuned balance between all obstacles. Moreover, CTs resulted superior in reaching the center of mass (COM) of STN, as well as in optimizing the entry angle in STN and in the skull surface.

Dynamic Oscillations Evoked by Subcallosal Cingulate Deep Brain Stimulation

“…Identification of neural signals tracking with specific white matter targets is an area of ongoing research and interest, especially as white matter targets for psychiatric illnesses grow (Waters et al, 2018 ; Segato et al, 2019 ; Horn and Fox, 2020 ). Since LFP-DOs are seem mostly in non-therapeutic OffTarget stimulation, they have limited utility as a sensitive signal for target engagement confirmation, but potential DOs in the EEG under OnTarget conditions suggests a role for DOs in assessing network-level engagement in antidepressant SCC-DBS (Waters et al, 2018 ; Howell et al, 2019 , 2020 ).…”

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

“…Interfacing with the neural system in an invasive manner for rehabilitation purposes can also be achieved with deeper brain sensing and stimulation. Robotic physical assistance has been converging with deep brain stimulation (DBS), leading to novel forms of neural engineering technology, although this field does not necessarily precisely fit the scope of neural rehabilitation [ 105 ].…”

Advances in Multidisciplinary Management of Skull Base Meningiomas

“…Virtual planning starts with appropriate image acquisition, often requiring multiple modalities [ 15 ]: Tel et al [ 16 ] described a multimodal image fusion algorithm based on automatic registration of CT and MRI images. For computer-reconstruction of intracranial vessels, MRI angiography is essential to analyze the three-dimensional spatial relationship between the tumor and the vessels [ 17 , 18 ]. Segmentation, defined as the process of automatic or semi-automatic detection of boundaries for regions of interest within DICOM images, allows anatomical and pathological structures to be identified, which are rebuilt in the three-dimensional space across all slices of the radiological image [ 19 ].…”