Caroline Essert

Automatic computation of electrodes trajectory for deep brain stimulation

In this paper, we propose an approach to find the optimal position of an electrode, for assisting surgeons in planning Deep Brain Stimulation. We first show how we formalized the rules governing this surgical procedure into geometric constraints. Then we explain our method, using a formal geometric solver, and a template built from 15 MRIs, used to propose a space of possible solutions and the optimal one. We show our results for the retrospective study on 8 implantations from 4 patients, and compare them with the trajectory of the electrode that was actually implanted. The results show a slight difference with the reference trajectories, with a better evaluation for our proposition.

Preoperative trajectory planning for percutaneous procedures in deformable environments

In image-guided percutaneous interventions, a precise planning of the needle path is a key factor to a successful intervention. In this paper we propose a novel method for computing a patient-specific optimal path for such interventions, accounting for both the deformation of the needle and soft tissues due to the insertion of the needle in the body. To achieve this objective, we propose an optimization method for estimating preoperatively a curved trajectory allowing to reach a target even in the case of tissue motion and needle bending. Needle insertions are simulated and regarded as evaluations of the objective function by the iterative planning process. In order to test the planning algorithm, it is coupled with a fast needle insertion simulation involving a flexible needle model and soft tissue finite element modeling, and experimented on the use-case of thermal ablation of liver tumors. Our algorithm has been successfully tested on twelve datasets of patient-specific geometries. Fast convergence to the actual optimal solution has been shown. This method is designed to be adapted to a wide range of percutaneous interventions.

Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories.

PurposeAutomatic methods for preoperative trajectory planning of electrodes in deep brain stimulation are usually based on the search for a path that resolves a set of surgical constraints to propose an optimal trajectory. The relative importance of each surgical constraint is usually defined as weighting parameters that are empirically set beforehand. The objective of this paper is to analyze the use of these parameters thanks to a retrospective study of trajectories manually planned by neurosurgeons. For that purpose, we firstly retrieved weighting factors allowing to match neurosurgeons manually planned choice of trajectory on each retrospective case; secondly, we compared the results from two different hospitals to evaluate their similarity; and thirdly, we compared the trends to the weighting factors empirically set in most current approaches.MethodsTo retrieve the weighting factors best matching the neurosurgeons manual plannings, we proposed two approaches: one based on a stochastic sampling of the parameters and the other on an exhaustive search. In each case, we obtained a sample of combinations of weighting parameters with a measure of their quality, i.e., the similarity between the automatic trajectory they lead to and the one manually planned by the surgeon as a reference. Visual and statistical analyses were performed on the number of occurrences and on the rank means.ResultsWe performed our study on 56 retrospective cases from two different hospitals. We could observe a trend of the occurrence of each weight on the number of occurrences. We also proved that each weight had a significant influence on the ranking. Additionally, we observed no influence of the medical center parameters, suggesting that the trends were comparable in both hospitals. Finally, the obtained trends were confronted to the usual weights chosen by the community, showing some common points but also some discrepancies.ConclusionThe results tend to show a predominance of the choice of a trajectory close to a standard direction. Secondly, the avoidance of the vessels or sulci seems to be sought in the surroundings of the standard position. The avoidance of the ventricles seems to be less predominant, but this could be due to the already reasonable distance between the standard direction and the ventricles. The similarity of results between two medical centers tends to show that it is not an exceptional practice. These results suggest that manual planning software may introduce a bias in the planning by proposing a standard position.

Anticipation of Brain Shift in Deep Brain Stimulation Automatic Planning

Deep Brain Stimulation is a neurosurgery procedure consisting in implanting an electrode in a deep structure of the brain. This intervention requires a preoperative planning phase, with a millimetric accuracy, in which surgeons decide the best placement of the electrode depending on a set of surgical rules. However, brain tissues may deform during the surgery because of the brain shift phenomenon, leading the electrode to mistake the target, or moreover to damage a vital anatomical structure. In this paper, we present a patient-specific automatic planning approach for DBS procedures which accounts for brain deformation. Our approach couples an optimization algorithm with FEM based brain shift simulation. The system was tested successfully on a patient-specific 3D model, and was compared to a planning without considering brain shift. The obtained results point out the importance of performing planning in dynamic conditions.

Pareto Front vs. Weighted Sum for Automatic Trajectory Planning of Deep Brain Stimulation

Preoperative path planning for Deep Brain Stimulation (DBS) is a multi-objective optimization problem consisting in searching the best compromise between multiple placement constraints. Its automation is usually addressed by turning the problem into mono-objective thanks to an aggregative approach. However, despite its intuitiveness, this approach is known for its incapacity to find all optimal solutions. In this work, we introduce an approach based on multi-objective dominance to DBS path planning. We compare it to a classical aggregative weighted sum of the multiple constraints and to a manual planning thanks to a retrospective study performed by a neurosurgeon on 14 DBS cases. The results show that the dominance-based method is preferred over manual planning, and covers a larger choice of relevant optimal entry points than the traditional weighted sum approach which discards interesting solutions that could be preferred by surgeons.

Evolutionary approaches for surgical path planning: A quantitative study on Deep Brain Stimulation

Path planning for surgical tools in minimally invasive surgery is a multi-objective optimization problem consisting in searching the best compromise between multiple placement constraints to find an optimal insertion point for the tool. Many works have been proposed to automate the decision-making process. Most of them use an aggregative approach that transforms the problem into a mono-objective problem. However, despite its intuitiveness, this approach is known for its incapacity to find all optimal solutions. After a previous clinical study in which we pointed out the interest of introducing MOEAs to neurosurgery [12], in this work, we aim at maximizing the range of optimal solutions proposed to the surgeon. Our study compares three different optimization approaches: an aggregative method using a weighted sum of the multiple constraints, an evolutionary multi-objective method, and an exhaustive dominance-based method used as ground truth. For each approach, we extract the set of all optimal insertion points based on dominance rules, and analyze the common and differing solutions by comparing the surfaces they cover. The experiments have been performed on 30 images datasets from patients who underwent a Deep Brain Stimulation electrode implant in the brain. It can be observed that the areas covered by the optimal insertion points obtained by the three methods differ significantly. The obtained results show that the traditional weighted sum approach is not sufficient to find the totality of the optimal solutions. The Pareto-based approaches provide extra solutions, but neither of them could find the complete optimal solution space. Further works should investigate either hybrid or extended methods such as adaptive weighted sum, or hybrid visualization of the solutions in the GUI.

Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated

PurposeDeep brain stimulation (DBS) is a procedure requiring accurate targeting and electrode placement. The two key elements for successful planning are preserving patient safety by ensuring a safe trajectory and creating treatment efficacy through optimal selection of the stimulation point. In this work, we present the first approach of computer-assisted preoperative DBS planning to automatically optimize both the safety of the electrode’s trajectory and location of the stimulation point so as to provide the best clinical outcome.MethodsBuilding upon the findings of previous works focused on electrode trajectory, we added a set of constraints guiding the choice of stimulation point. These took into account retrospective data represented by anatomo-clinical atlases and intersections between the stimulation region and sensitive anatomical structures causing side effects. We implemented our method into automatic preoperative planning software to assess if the algorithm was able to simultaneously optimize electrode trajectory and the stimulation point.ResultsLeave-one-out cross-validation on a dataset of 18 cases demonstrated an improvement in the expected outcome when using the new constraints. The distance to critical structures was not reduced. The intersection between the stimulation region and structures sensitive to stimulation was minimized.ConclusionsIntroducing these new constraints guided the planning to select locations showing a trend toward symptom improvement, while minimizing the risks of side effects, and there was no cost in terms of trajectory safety.

Self-guided training for deep brain stimulation planning using objective assessment

ObjectiveDeep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning.MethodsTo assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout.ResultsAt baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures.ConclusionWe developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees’ proficiency, and the quantitative measures allow trainees’ progress to be monitored.

Introducing Pareto-based MOEA to Neurosurgery Preoperative Path Planning

This paper presents the first implementation of NSGA-II in neurosurgery preoperative path planning. Deep Brain Stimulation (DBS) is a surgical treatment of Parkinsons disease that can be regarded as a multi-objective optimization problem, searching for the best compromise between multiple electrode placement rules. Most of the current automatic decision-making processes use aggregative approaches with single objective optimization, even though they are known for their inability to find all Pareto-optimal solutions. Firstly, we show this is the case on 20 datasets of patients by comparing our implementation of NSGA-II to the weighted sum (WS) strategy. Then, we show that it requires about 9 hours to find equivalent results using a deterministic scan of the search space where NSGA-II does it in about 3mn. This paper presents an objective validation that even simple techniques such as NSGA-II should be used by surgeons over more intuitive weighted based methods.

Comparative study of the performances of several haptic modalities for a 3D menu

We introduce a new technique of haptic guidance, for navigation and control of applications in virtual environments. We haptically simulate the collisions of the pointer with the borders of a polyhedral menu, making it glide towards the items. We propose the preliminary results of an empirical evaluation of this technique.