Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant

Scorza, Davide; Momi, Elena De; Plaino, Lisa; Amoroso, Gaetano; Arnulfo, Gabriele; Narizzano, Massimo; Kabongo, Luis; Cardinale, Francesco

doi:10.1007/s11548-017-1641-2

Cited by 21 publications

(23 citation statements)

References 31 publications

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“…Using this platform, the surgeon was able to select the desired entry and target points in~60 s and overall planning time was reduced to~5 min. As computer processing power has increased multimodal image registration [33], automated segmentation [34] and 3D rendering [35] have allowed neurosurgical planning to become quicker and more sophisticated.…”

Section: Laser Interstitial Thermal Therapymentioning

confidence: 99%

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach

Vakharia

Sparks

et al. 2019

Neurotherapeutics

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Laser interstitial thermal therapy (LITT) is an alternative to open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Studies suggest maximal ablation of the mesial hippocampal head and amygdalohippocampal complex (AHC) improves seizure freedom rates while better neuropsychological outcomes are associated with sparing of the parahippocampal gyrus (PHG). Optimal trajectories avoid sulci and CSF cavities and maximize distance from vasculature. Computer-assisted planning (CAP) improves these metrics, but the combination of entry and target zones has yet to be determined to maximize ablation of the AHC while sparing the PHG. We apply a machine learning approach to predict entry and target parameters and utilize these for CAP. Ten patients with hippocampal sclerosis were identified from a prospectively managed database. CAP LITT trajectories were generated using entry regions that include the inferior occipital, middle occipital, inferior temporal, and middle temporal gyri. Target points were varied by sequential AHC erosions and transformations of the centroid of the amygdala. A total of 7600 trajectories were generated, and ablation volumes of the AHC and PHG were calculated. Two machine learning approaches (random forest and linear regression) were investigated to predict composite ablation scores and determine entry and target point combinations that maximize ablation of the AHC while sparing the PHG. Random forest and linear regression predictions had a high correlation with the calculated values in the test set (ρ = 0.7) for both methods. Maximal composite ablation scores were associated with entry points around the junction of the inferior occipital, middle occipital, and middle temporal gyri. The optimal target point was the anteromesial amygdala. These parameters were then used with CAP to generate clinically feasible trajectories that optimize safety metrics. Machine learning techniques accurately predict composite ablation score. Prospective studies are required to determine if this improves seizure-free outcome while reducing neuropsychological morbidity following LITT for MTLE.

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Section: Laser Interstitial Thermal Therapymentioning

confidence: 99%

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach

Vakharia

Sparks

et al. 2019

Neurotherapeutics

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“…Abhinav et al presented the technological advances leading up to the development of DTI and more advanced techniques aimed at imaging the white matter (Thomas et al, 2014). Different automatic algorithms have been proposed for minimally invasive neurosurgery, mainly for Stereoelectroencephalography (SEEG), Deep Brain Stimulation (DBS) and needle biopsies with the main goal of assisting the surgeon during the planning phase (Scorza et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

“…• The percentage of CTs and RTs that did not exceed θ max =30 • , defined as the maximum insertion angle with respect to skull normal acceptable for electrode placement (Scorza et al, 2017), as described in Equation 15and shown in Figure 3B.…”

Section: Validation Of Rts Vs Ctsmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Essert et al [3] formalized the implicit and explicit principles used by surgeons in DBS manual planning to tune an automatic straight trajectory planner. A multi-planner for Stereoelectroencephalography (SEEG) solution proposed in [5] (subsequently improved in [13]) is able to provide the surgeon with the best configuration for multiple linear electrodes, accounting for the spacial relationships among them. This is also obtained by expanding each EP initially selected by the surgeon to an entry area, where new EPs are estimated in order to search for novel trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…Simulation parameters and hard constraints values are reported in Table I. The values of the maximum inclination angle α max and M entry radius were consistent with those used in [13], while the minimum distance d pp between adjacent COMs was defined as a reasonable value in accordance with the resolution of the MRI data. The test demonstrated a saturation in the number of COMs obtained by mesh decimation and the data showed a good fit with an exponential trend (R-square= 0.986), thus an optimal decimation value has been defined as ξ opt = 70%.…”

Section: A Decimation Thresholdmentioning

confidence: 99%

Automatic multi-trajectory planning solution for steerable catheters

Favaro

Cerri

Scorza

et al. 2018

2018 International Symposium on Medical Robotics (ISMR)

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The present work describes a novel approach to trajectory planning for minimally invasive surgery consisting of an algorithm able to provide the surgeon with multiple curvilinear paths to connect an entry area defined on the brain cortex to a specific target point in the brain. A criterion based on the minimum distance from the safety-critical brain structures (blood vessels, thalamus and ventricles) is used to rank the obtained trajectories. The solution is integrated onto the EDEN2020 * programmable bevel-tip needle, a multi-segment probe whose steering ability derives from the offset generated on its tip, and provides a level of tolerance with respect to tracking errors arising from catheter model inaccuracies. The case of study of the work consists of a typical Deep Brain Stimulation scenario where tests have been performed in order to compare the result obtained from standard rectilinear trajectory planning against this novel curvilinear solution using the clearance from obstacles as an index of performance of the estimated solutions.

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Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant

Cited by 21 publications

References 31 publications

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach

Optimizing Trajectories for Cranial Laser Interstitial Thermal Therapy Using Computer-Assisted Planning: A Machine Learning Approach

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

Automatic multi-trajectory planning solution for steerable catheters

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