1
|
Pantovic A, Essert C. Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning. Int J Comput Assist Radiol Surg 2024; 19:995-1002. [PMID: 38411781 DOI: 10.1007/s11548-024-03078-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 02/12/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be replaced by machine learning approaches. This paper explores the feasibility of using deep reinforcement learning (DRL) in this context, starting with the single-electrode use-case of deep brain stimulation (DBS). METHODS We propose a DRL approach based on deep Q-learning where the states represent the electrode trajectory and associated information, and actions are the possible motions. Deep neural networks allow to navigate the complex state space derived from MRI data. The chosen reward function emphasizes safety and accuracy in reaching the target structure. The results were compared with a reference (segmented electrode) and a conventional technique. RESULTS The DRL approach excelled in navigating the complex anatomy, consistently providing safer and more precise electrode placements than the reference. Compared to conventional techniques, it showed an improvement in accuracy of 2.3% in average proximity to obstacles and 19.4% in average orientation angle. Expectedly, computation times rose significantly, from 2 to 18 min. CONCLUSION Our investigation into DRL for DBS electrode trajectory planning has showcased its promising potential. Despite only delivering modest accuracy gains compared to traditional methods in the single-electrode case, its relevance for problems with high-dimensional state and action spaces and its resilience against local optima highlight its promising role for complex scenarios. This preliminary study constitutes a first step toward the more challenging problem of multiple-electrodes planning.
Collapse
|
2
|
Wankhede A, Madiraju L, Siampli E, Fischer E, Cleary K, Oluigbo C, Monfaredi R. Validation of a novel path planner for stereotactic neurosurgical interventions-A retrospective clinical study. Int J Med Robot 2022; 18:e2458. [PMID: 36109343 PMCID: PMC9633400 DOI: 10.1002/rcs.2458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/24/2022] [Accepted: 03/03/2022] [Indexed: 11/07/2023]
Abstract
BACKGROUND The gold standard workflow for targeting structures in the brain involves manual path planning. This preoperative manual path planning is very time-intensive and laborious, especially when some outcome measures such as maximum ablation and penetration depth has to be optimised. METHODS Our novel path planner generates an optimal path which maximises the hippocampus penetration and distance from critical structures using a precomputed cost map and a reward map. RESULTS The average penetration ratio for 12 cases is 88.13 ± 23.23% for a resolution of 1° and a safety margin of 1 mm. Average run time for the path planner based on 1° resolution was 1.99 ± 0.68 min. CONCLUSIONS Results show that the algorithm can generate safe and clinically relevant paths with a quantitative representation of the penetration depth and is faster than the average reported time for manual path planning.
Collapse
Affiliation(s)
- Ajeet Wankhede
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
| | - Likhita Madiraju
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
| | - Eleni Siampli
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
| | - Elizabeth Fischer
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
| | - Kevin Cleary
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
| | - Chima Oluigbo
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
- Diagnostic Imaging and Radiology Department, Children’s National Health System, Washington, District of Columbia, USA
| | - Reza Monfaredi
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System, Washington, District of Columbia, USA
| |
Collapse
|
3
|
Optimized Deep Brain Stimulation Surgery to Avoid Vascular Damage: A Single-Center Retrospective Analysis of Path Planning for Various Deep Targets by MRI Image Fusion. Brain Sci 2022; 12:brainsci12080967. [PMID: 35892408 PMCID: PMC9332267 DOI: 10.3390/brainsci12080967] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/08/2022] [Accepted: 07/12/2022] [Indexed: 11/23/2022] Open
Abstract
Co-registration of stereotactic and preoperative magnetic resonance imaging (MRI) images can serve as an alternative for trajectory planning. However, the role of this strategy has not yet been proven by any control studies, and the trajectories of commonly used targets have not been systematically studied. The purpose of this study was to analyze the trajectories for various targets, and to assess the role of trajectories realized on fused images in preventing intracranial hemorrhage (ICH). Data from 1019 patients who underwent electrode placement for deep brain stimulation were acquired. Electrode trajectories were not planned for 396 patients, whereas trajectories were planned for 623 patients. Preoperative various MRI sequences and frame-placed MRI images were fused for trajectory planning. The patients’ clinical characteristics, the stereotactic systems, intracranial hemorrhage cases, and trajectory angles were recorded and analyzed. No statistically significant differences in the proportions of male patients, patients receiving local anesthesia, and diseases or target distributions (p > 0.05) were found between the trajectory planning group and the non-trajectory planning group, but statistically significant differences were observed in the numbers of both patients and leads associated with symptomatic ICH (p < 0.05). Regarding the ring and arc angle values, statistically significant differences were found among various target groups (p < 0.05). The anatomic structures through which leads passed were found to be diverse. Trajectory planning based on MRI fusion is a safe technique for lead placement. The electrode for each given target has its own relatively constant trajectory.
Collapse
|
4
|
Iredale E, Voigt B, Rankin A, Kim KW, Chen JZ, Schmid S, Hebb MO, Peters TM, Wong E. Planning System for the Optimization of Electric Field Delivery using Implanted Electrodes for Brain Tumor Control. Med Phys 2022; 49:6055-6067. [PMID: 35754362 DOI: 10.1002/mp.15825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 06/06/2022] [Accepted: 06/17/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND The use of non-ionizing electric fields from low intensity voltage sources (<10 V) to control malignant tumor growth is showing increasing potential as a cancer treatment modality. A method of applying these low intensity electric fields using multiple implanted electrodes within or adjacent to tumor volumes has been termed as intratumoral modulation therapy (IMT). PURPOSE This study explores advancements in the previously established IMT optimization algorithm, and the development of a custom treatment planning system for patient specific IMT. The practicality of the treatment planning system is demonstrated by implementing the full optimization pipeline on a brain phantom with robotic electrode implantation, post-operative imaging, and treatment stimulation. METHODS The integrated planning pipeline in 3D Slicer begins with importing and segmenting patient magnetic resonance images (MRI) or computed tomography (CT) images. The segmentation process is manual, followed by a semi-automatic smoothing step that allows the segmented brain and tumor mesh volumes to be smoothed and simplified by applying selected filters. Electrode trajectories are planned manually on the patient MRI or CT by selecting insertion and tip coordinates for a chosen number of electrodes. The electrode tip positions, and stimulation parameters (phase shift and voltage) can then be optimized with the custom semi-automatic IMT optimization algorithm where users can select the prescription electric field, voltage amplitude limit, tissue electrical properties, nearby organs at risk, optimization parameters (electrode tip location, individual contact phase shift and voltage), desired field coverage percent, and field conformity optimization. Tables of optimization results are displayed, and the resulting electric field is visualized as a field-map superimposed on the MR or CT image, with 3D renderings of the brain, tumor, and electrodes. Optimized electrode coordinates are transferred to robotic electrode implantation software to enable planning and subsequent implantation of the electrodes at the desired trajectories. RESULTS An IMT treatment planning system was developed that incorporates patient specific MRI or CT, segmentation, volume smoothing, electrode trajectory planning, electrode tip location and stimulation parameter optimization, and results visualization. All previous manual pipeline steps operating on diverse software platforms were coalesced into a single semi-automated 3D Slicer based user interface. Brain phantom validation of the full system implementation was successful in pre-operative planning, robotic electrode implantation, and post-operative treatment planning to adjust stimulation parameters based on actual implant locations. Voltage measurements were obtained in the brain phantom to determine the electrical parameters of the phantom and validate the simulated electric field distribution. CONCLUSIONS A custom treatment planning and implantation system for IMT has been developed in this study, and validated on a phantom brain model, providing an essential step in advancing IMT technology towards future clinical safety and efficacy investigations. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Erin Iredale
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Brynn Voigt
- Department of Physics and Astronomy, Western University, London, ON, Canada
| | - Adam Rankin
- Robarts Research Institute, Western University, London, ON, Canada
| | - Kyungho W Kim
- Department of Physics and Astronomy, Western University, London, ON, Canada
| | - Jeff Z Chen
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Susanne Schmid
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Matthew O Hebb
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.,Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Terry M Peters
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.,Robarts Research Institute, Western University, London, ON, Canada
| | - Eugene Wong
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.,Department of Physics and Astronomy, Western University, London, ON, Canada
| |
Collapse
|
5
|
Scorza D, El Hadji S, Cortés C, Bertelsen Á, Cardinale F, Baselli G, Essert C, Momi ED. Surgical planning assistance in keyhole and percutaneous surgery: A systematic review. Med Image Anal 2020; 67:101820. [PMID: 33075642 DOI: 10.1016/j.media.2020.101820] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 08/07/2020] [Accepted: 09/07/2020] [Indexed: 11/29/2022]
Abstract
Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases. First, a general formulation of the problem is presented, independently from the surgical field involved, and the key steps involved in the development of a CAP solution are detailed. Secondly, we categorized the articles based on the main surgical applications, which have been object of study and we categorize them based on the type of assistance provided to the end-user.
Collapse
Affiliation(s)
- Davide Scorza
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy; Biodonostia Health Research Institute, Donostia-San Sebastián, Spain.
| | - Sara El Hadji
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy.
| | - Camilo Cortés
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
| | - Álvaro Bertelsen
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Spain
| | - Francesco Cardinale
- Claudio Munari Centre for Epilepsy and Parkinson surgery, Azienda Socio-Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (ASST GOM Niguarda), Milan, Italy
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Caroline Essert
- ICube Laboratory, CNRS, UMR 7357, Université de Strasbourg, Strasbourg, France
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| |
Collapse
|
6
|
Javaid M, Haleem A. Impact of industry 4.0 to create advancements in orthopaedics. J Clin Orthop Trauma 2020; 11:S491-S499. [PMID: 32774017 PMCID: PMC7394797 DOI: 10.1016/j.jcot.2020.03.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 03/15/2020] [Accepted: 03/16/2020] [Indexed: 12/19/2022] Open
Abstract
Scientists and health professional are focusing on improving the medical sciences for the betterment of patients. The fourth industrial revolution, which is commonly known as Industry 4.0, is a significant advancement in the field of engineering. Industry 4.0 is opening a new opportunity for digital manufacturing with greater flexibility and operational performance. This development is also going to have a positive impact in the field of orthopaedics. The purpose of this paper is to present various advancements in orthopaedics by the implementation of Industry 4.0. To undertake this study, we have studied the available literature extensively on Industry 4.0, technologies of Industry 4.0 and their role in orthopaedics. Paper briefly explains about Industry 4.0, identifies and discusses the major technologies of Industry 4.0, which will support development in orthopaedics. Finally, from the available literature, the paper identifies twelve significant advancements of Industry 4.0 in orthopaedics. Industry 4.0 uses various types of digital manufacturing and information technologies to create orthopaedics implants, patient-specific tools, devices and innovative way of treatment. This revolution is to be useful to perform better spinal surgery, knee and hip replacement, and invasive surgeries.
Collapse
Affiliation(s)
- Mohd Javaid
- Corresponding author., https://scholar.google.co.in/citations?user=rfyiwvsAAAAJ&hl=en
| | | |
Collapse
|
7
|
Knowledge-based automated planning system for StereoElectroEncephaloGraphy: A center-based scenario. J Biomed Inform 2020; 108:103460. [PMID: 32512210 DOI: 10.1016/j.jbi.2020.103460] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 05/24/2020] [Accepted: 05/28/2020] [Indexed: 11/22/2022]
Abstract
Surgical planning for StereoElectroEncephaloGraphy (SEEG) is a complex and patient specific task, where the experience and medical workflow of each institution may influence the final planning choices. To account for this variability, we developed a data-based Computer Assisted Planning (CAP) solution able to exploit the knowledge extracted by past cases. By the analysis of retrospective patients' data sets, our system proposes a pool of trajectories commonly used by the institution, which can be selected to initialize a new patient plan. An optimization framework adapts those to the patient's anatomy by optimizing clinical requirements (e.g. distance from vessel, gray matter recording and insertion angle), and adapting its strategy based on the trajectory type selected.The system has been customized based on the data of a single institution. Two neurosurgeons, working in a high-volume hospital, have validated it by using 15 retrospective patient data sets, with more than 200 trajectories reviewed. Both surgeons considered ~81% of the optimized trajectories as clinically feasible (75% inter-rater reliability). Quantitative comparison of distance from vessels, insertion angle and gray matter recording index showed that the optimized trajectories reached superior or comparable values with respect to the original manual plans. The results suggest that a tailored center-based solution could increase the acceptance rate of the automated trajectories proposed.
Collapse
|
8
|
|
9
|
Zanello M, Carron R, Peeters S, Gori P, Roux A, Bloch I, Oppenheim C, Pallud J. Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives. Neurosurg Rev 2020; 44:867-888. [PMID: 32430559 DOI: 10.1007/s10143-020-01315-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 04/17/2020] [Accepted: 05/04/2020] [Indexed: 11/24/2022]
Abstract
The creation of intracranial stereotactic trajectories, from entry point to target point, is still mostly done manually by the neurosurgeon. The development of automated stereotactic planning tools has been described in the literature. This systematic review aims to assess the effectiveness of stereotactic planning procedure automation and develop tools for patients undergoing neurosurgical stereotactic procedures. PubMed/MEDLINE, EMBASE, Google Scholar, CINAHL, PsycINFO, and Cochrane Register of Controlled Trials databases were searched from inception to September 1, 2019, at the exception of Google Scholar (from 1 January 2010 to September 1, 2019) in French and English. Eligible studies included all studies proposing automated stereotactic planning. A total of 1543 studies were screened. Forty-two studies were included in the systematic review, including 18 (42.9%) conference papers. The surgical procedures planned automatically were mainly deep brain stimulation (n = 14, 33.3%), stereoelectroencephalography (n = 12, 28.6%), and not specified (n = 10, 23.8%). The most frequently used surgical constraints to plan the trajectory were blood vessels (n = 32, 76.2%), cerebral sulci (n = 27, 64.3%), and cerebral ventricles (n = 23, 54.8%). The distance from blood vessels ranged from 1.96 to 4.78 mm for manual trajectories and from 2.47 to 7.0 mm for automated trajectories. At least one neurosurgeon was involved in 36 studies (85.7%). The automated stereotactic trajectory was preferred in 75.4% of the studied cases (range 30-92.9). Only 3 (7.1%) studies were multicentric. No study reported prospective use of the planning software. Stereotactic planning automation is a promising tool to provide valuable stereotactic trajectories for clinical applications.
Collapse
Affiliation(s)
- Marc Zanello
- Department of Neurosurgery, GHU Paris-Sainte-Anne Hospital, 1, rue Cabanis, 75674, Paris Cedex 14, France. .,Paris Descartes University, Sorbonne Paris Cité, Paris, France. .,IMABRAIN, Institute of Psychiatry and Neuroscience of Paris, INSERM UMR 1266, Paris, France.
| | - Romain Carron
- Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France.,Department of Functional and Stereotactic Neurosurgery, Timone University Hospital, Marseille, France
| | - Sophie Peeters
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA
| | - Pietro Gori
- IMABRAIN, Institute of Psychiatry and Neuroscience of Paris, INSERM UMR 1266, Paris, France.,IMAG2 Laboratory, Imagine Institute, Paris, France.,LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France
| | - Alexandre Roux
- Department of Neurosurgery, GHU Paris-Sainte-Anne Hospital, 1, rue Cabanis, 75674, Paris Cedex 14, France.,Paris Descartes University, Sorbonne Paris Cité, Paris, France.,IMABRAIN, Institute of Psychiatry and Neuroscience of Paris, INSERM UMR 1266, Paris, France
| | - Isabelle Bloch
- IMABRAIN, Institute of Psychiatry and Neuroscience of Paris, INSERM UMR 1266, Paris, France.,IMAG2 Laboratory, Imagine Institute, Paris, France.,LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France
| | - Catherine Oppenheim
- Paris Descartes University, Sorbonne Paris Cité, Paris, France.,IMABRAIN, Institute of Psychiatry and Neuroscience of Paris, INSERM UMR 1266, Paris, France.,Department of Neuroradiology, GHU Paris-Sainte-Anne Hospital, Paris, France
| | - Johan Pallud
- Department of Neurosurgery, GHU Paris-Sainte-Anne Hospital, 1, rue Cabanis, 75674, Paris Cedex 14, France.,Paris Descartes University, Sorbonne Paris Cité, Paris, France.,IMABRAIN, Institute of Psychiatry and Neuroscience of Paris, INSERM UMR 1266, Paris, France
| |
Collapse
|
10
|
Segato A, Pieri V, Favaro A, Riva M, Falini A, De Momi E, Castellano A. Automated Steerable Path Planning for Deep Brain Stimulation Safeguarding Fiber Tracts and Deep Gray Matter Nuclei. Front Robot AI 2019; 6:70. [PMID: 33501085 PMCID: PMC7806057 DOI: 10.3389/frobt.2019.00070] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 07/18/2019] [Indexed: 12/20/2022] Open
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.
Collapse
Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
| | - Alberto Favaro
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy.,Unit of Oncological Neurosurgery, Humanitas Research Hospital, Rozzano, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
| |
Collapse
|
11
|
Hong A, Boehler Q, Moser R, Zemmar A, Stieglitz L, Nelson BJ. 3D path planning for flexible needle steering in neurosurgery. Int J Med Robot 2019; 15:e1998. [DOI: 10.1002/rcs.1998] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/08/2019] [Accepted: 03/26/2019] [Indexed: 11/12/2022]
Affiliation(s)
- Ayoung Hong
- Multi‐Scale Robotics LaboratoryETH Zürich Zürich Switzerland
| | - Quentin Boehler
- Multi‐Scale Robotics LaboratoryETH Zürich Zürich Switzerland
| | - Roman Moser
- Multi‐Scale Robotics LaboratoryETH Zürich Zürich Switzerland
| | - Ajmal Zemmar
- Juha Hernesniemi International Neurosurgery Center, Henan Provincial People's HospitalZhengzhou University Zhengzhou China
| | - Lennart Stieglitz
- Department of NeurosurgeryUniversity Hospital Zurich Zürich Switzerland
| | | |
Collapse
|
12
|
Holden MS, Zhao Y, Haegelen C, Essert C, Fernandez-Vidal S, Bardinet E, Ungi T, Fichtinger G, Jannin P. Self-guided training for deep brain stimulation planning using objective assessment. Int J Comput Assist Radiol Surg 2018; 13:1129-1139. [DOI: 10.1007/s11548-018-1753-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/26/2018] [Indexed: 10/17/2022]
|
13
|
Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated. Int J Comput Assist Radiol Surg 2018; 13:1117-1128. [PMID: 29557997 DOI: 10.1007/s11548-018-1724-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 02/28/2018] [Indexed: 10/17/2022]
Abstract
PURPOSE Deep 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. METHODS Building 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. RESULTS Leave-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. CONCLUSIONS Introducing 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.
Collapse
|
14
|
D’Haese PF, Konrad PE, Dawant BM. Big Data and Deep Brain Stimulation. Neuromodulation 2018. [DOI: 10.1016/b978-0-12-805353-9.00013-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
15
|
Scorza D, De Momi E, Plaino L, Amoroso G, Arnulfo G, Narizzano M, Kabongo L, Cardinale F. Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant. Int J Comput Assist Radiol Surg 2017; 12:1727-1738. [DOI: 10.1007/s11548-017-1641-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Accepted: 07/05/2017] [Indexed: 10/19/2022]
|
16
|
Sparks R, Zombori G, Rodionov R, Nowell M, Vos SB, Zuluaga MA, Diehl B, Wehner T, Miserocchi A, McEvoy AW, Duncan JS, Ourselin S. Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment. Int J Comput Assist Radiol Surg 2016; 12:123-136. [PMID: 27368184 PMCID: PMC5216164 DOI: 10.1007/s11548-016-1452-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 06/17/2016] [Indexed: 02/06/2023]
Abstract
Purpose About one-third of individuals with focal epilepsy continue to have seizures despite optimal medical management. These patients are potentially curable with neurosurgery if the epileptogenic zone (EZ) can be identified and resected. Stereo-electroencephalography (SEEG) to record epileptic activity with intracranial depth electrodes may be required to identify the EZ. Each SEEG electrode trajectory, the path between the entry on the skull and the cerebral target, must be planned carefully to avoid trauma to blood vessels and conflicts between electrodes. In current clinical practice trajectories are determined manually, typically taking 2–3 h per patient (15 min per electrode). Manual planning (MP) aims to achieve an implantation plan with good coverage of the putative EZ, an optimal spatial resolution, and 3D distribution of electrodes. Computer-assisted planning tools can reduce planning time by quantifying trajectory suitability. Methods We present an automated multiple trajectory planning (MTP) algorithm to compute implantation plans. MTP uses dynamic programming to determine a set of plans. From this set a depth-first search algorithm finds a suitable plan. We compared our MTP algorithm to (a) MP and (b) an automated single trajectory planning (STP) algorithm on 18 patient plans containing 165 electrodes. Results MTP changed all 165 trajectories compared to MP. Changes resulted in lower risk (122), increased grey matter sampling (99), shorter length (92), and surgically preferred entry angles (113). MTP changed 42 % (69/165) trajectories compared to STP. Every plan had between 1 to 8 (median 3.5) trajectories changed to resolve electrode conflicts, resulting in surgically preferred plans. Conclusion MTP is computationally efficient, determining implantation plans containing 7–12 electrodes within 1 min, compared to 2–3 h for MP.
Collapse
Affiliation(s)
- Rachel Sparks
- Centre for Medical Image Computing, University College London, London, UK.
| | - Gergely Zombori
- Centre for Medical Image Computing, University College London, London, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - Mark Nowell
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, UK
| | - Maria A Zuluaga
- Centre for Medical Image Computing, University College London, London, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - Tim Wehner
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.,National Hospital for Neurology and Neurosurgery (NHNN), London, UK
| | - Sebastien Ourselin
- Centre for Medical Image Computing, University College London, London, UK.,Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| |
Collapse
|
17
|
Pareto Front vs. Weighted Sum for Automatic Trajectory Planning of Deep Brain Stimulation. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016 2016. [DOI: 10.1007/978-3-319-46720-7_62] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
18
|
Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories. Int J Comput Assist Radiol Surg 2015. [DOI: 10.1007/s11548-015-1263-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
19
|
Zelmann R, Beriault S, Marinho MM, Mok K, Hall JA, Guizard N, Haegelen C, Olivier A, Pike GB, Collins DL. Improving recorded volume in mesial temporal lobe by optimizing stereotactic intracranial electrode implantation planning. Int J Comput Assist Radiol Surg 2015; 10:1599-615. [PMID: 25808256 DOI: 10.1007/s11548-015-1165-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 02/13/2015] [Indexed: 11/30/2022]
Abstract
PURPOSE Intracranial electrodes are sometimes implanted in patients with refractory epilepsy to identify epileptic foci and propagation. Maximal recording of EEG activity from regions suspected of seizure generation is paramount. However, the location of individual contacts cannot be considered with current manual planning approaches. We propose and validate a procedure for optimizing intracranial electrode implantation planning that maximizes the recording volume, while constraining trajectories to safe paths. METHODS Retrospective data from 20 patients with epilepsy that had electrodes implanted in the mesial temporal lobes were studied. Clinical imaging data (CT/A and T1w MRI) were automatically segmented to obtain targets and structures to avoid. These data were used as input to the optimization procedure. Each electrode was modeled to assess risk, while individual contacts were modeled to estimate their recording capability. Ordered lists of trajectories per target were obtained. Global optimization generated the best set of electrodes. The procedure was integrated into a neuronavigation system. RESULTS Trajectories planned automatically covered statistically significant larger target volumes than manual plans [Formula: see text]. Median volume coverage was [Formula: see text] for automatic plans versus [Formula: see text] for manual plans. Furthermore, automatic plans remained at statistically significant safer distance to vessels [Formula: see text] and sulci [Formula: see text]. Surgeon's scores of the optimized electrode sets indicated that 95% of the automatic trajectories would be likely considered for use in a clinical setting. CONCLUSIONS This study suggests that automatic electrode planning for epilepsy provides safe trajectories and increases the amount of information obtained from the intracranial investigation.
Collapse
Affiliation(s)
- R Zelmann
- McConnell Brain Imaging Center, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada.
| | - S Beriault
- McConnell Brain Imaging Center, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - M M Marinho
- Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - K Mok
- Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - J A Hall
- Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - N Guizard
- McConnell Brain Imaging Center, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - C Haegelen
- LTSI - U1099 INSERM, CS34317, Université Rennes 1, 35043, Rennes, France
| | - A Olivier
- Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - G B Pike
- McConnell Brain Imaging Center, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| | - D L Collins
- McConnell Brain Imaging Center, Montreal Neurological Hospital and Institute, McGill University, 3801 University Street, Montreal, QC, H3A 2B4, Canada
| |
Collapse
|
20
|
The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery. Int J Comput Assist Radiol Surg 2014; 10:1127-40. [PMID: 25408305 DOI: 10.1007/s11548-014-1126-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 10/24/2014] [Indexed: 12/17/2022]
Abstract
PURPOSE Minimal invasion computer-assisted neurosurgical procedures with various tool insertions into the brain may carry hemorrhagic risks and neurological deficits. The goal of this study is to investigate the role of computer-based surgical trajectory planning tools in improving the potential safety of image-based stereotactic neurosurgery. METHODS Multi-sequence MRI studies of eight patients who underwent image-guided neurosurgery were retrospectively processed to extract anatomical structures-head surface, ventricles, blood vessels, white matter fibers tractography, and fMRI data of motor, sensory, speech, and visual areas. An experienced neurosurgeon selected one target for each patient. Five neurosurgeons planned a surgical trajectory for each patient using three planning methods: (1) conventional; (2) visualization, in which scans are augmented with overlays of anatomical structures and functional areas; and (3) automatic, in which three surgical trajectories with the lowest expected risk score are automatically computed. For each surgeon, target, and method, we recorded the entry point and its surgical trajectory and computed its expected risk score and its minimum distance from the key structures. RESULTS A total of 120 surgical trajectories were collected (5 surgeons, 8 targets, 3 methods). The surgical trajectories expected risk scores improved by 76% ([Formula: see text], two-sample student's t test); the average distance of a trajectory from nearby blood vessels increased by 1.6 mm ([Formula: see text]) from 0.6 to 2.2 mm (243%). The initial surgical trajectories were changed in 85% of the cases based on the expected risk score and the trajectory distance from blood vessels. CONCLUSIONS Computer-based patient-specific preoperative planning of surgical trajectories that minimize the expected risk of vascular and neurological damage due to incorrect tool placement is a promising technique that yields consistent improvements.
Collapse
|