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

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

Magnetic resonance-guided laser interstitial thermal therapy for drug-resistant epilepsy: A systematic review and individual participant data meta-analysis

Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has emerged as a popular minimally invasive alternative to open resective surgery for drug-resistant epilepsy (DRE). We sought to perform a systematic review and individual participant data meta-analysis to identify independent predictors of seizure outcome and complications following MRgLITT for DRE. Eleven databases were searched from January 1, 2010 to February 6, 2021 using the terms "MR-guided ablation therapy" and "epilepsy". Multivariable mixed-effects Cox and logistic regression identified predictors of time to seizure recurrence, seizure freedom, operative complications, and postoperative neurological deficits. From 8705 citations, 46 studies reporting on 450 MRgLITT DRE patients (mean age = 29.5 ± 18.1 years, 49.6% female) were included. Median postoperative seizure freedom and follow-up duration were 15.5 and 19.0 months, respectively. Overall, 240 (57.8%) of 415 patients (excluding palliative corpus callosotomy) were seizure-free at last follow-up. Generalized seizure semiology (hazard ratio [HR] = 1.78, p = .020) and nonlesional magnetic resonance imaging (MRI) findings (HR = 1.50, p = .032) independently predicted shorter time to seizure recurrence. Cerebral cavernous malformation (CCM; odds ratio [OR] = 7.97, p < .001) and mesial temporal sclerosis/atrophy (MTS/A; OR = 2.21, p = .011) were independently associated with greater odds of seizure freedom at last follow-up. Operative complications occurred in 28 (8.5%) of 330 patients and were independently associated with extratemporal ablations (OR = 5.40, p = .012) and nonlesional MRI studies (OR = 3.25, p = .017). Postoperative neurological deficits were observed in 53 (15.1%) of 352 patients and were independently predicted by hypothalamic hamartoma etiology (OR = 5.93, p = .006) and invasive electroencephalographic monitoring (OR = 4.83, p = .003). Overall, MRgLITT is particularly effective in treating patients with well-circumscribed lesional DRE, such as CCM and MTS/A, but less effective in nonlesional cases or lesional cases with a more diffuse epileptogenic network associated with generalized seizures. This study identifies independent predictors of seizure freedom and complications following MRgLITT that may help further guide patient selection.

Imaging in Interventional Radiology: 2043 and Beyond

Since its inception in the early 20th century, interventional radiology (IR) has evolved tremendously and is now a distinct clinical discipline with its own training pathway. The arsenal of modalities at work in IR includes x-ray radiography and fluoroscopy, CT, MRI, US, and molecular and multimodality imaging within hybrid interventional environments. This article briefly reviews the major developments in imaging technology in IR over the past century, summarizes technologies now representative of the standard of care, and reflects on emerging advances in imaging technology that could shape the field in the century ahead. The role of emergent imaging technologies in enabling high-precision interventions is also briefly reviewed, including image-guided ablative therapies.

Association of intraoperative end-tidal carbon dioxide level with ablation volume during magnetic resonance-guided laser interstitial thermal therapy for mesial temporal lobe epilepsy

OBJECTIVE

Maximal safe ablation of target structures during magnetic resonance-guided laser interstitial thermal therapy (MRgLiTT) is critical to achieving good seizure outcome in patients with mesial temporal lobe epilepsy (mTLE). The authors sought to determine whether intraoperative physiological variables are associated with ablation volume during MRgLiTT.

METHODS

Patients with mTLE who underwent MRgLiTT at our institution from 2014 to 2019 were retrospectively analyzed. Ablation volume was determined with volumetric analysis of intraoperative postablation MR images. Physiological parameters (systolic blood pressure [SBP], diastolic blood pressure [DBP], mean arterial pressure [MAP], end-tidal carbon dioxide [ETCO2]) measured 40 minutes prior to ablation were analyzed. Univariate and multivariate regression analyses were performed to determine independent predictors of ablation volume.

RESULTS

Forty-four patients met the inclusion criteria. The median (interquartile range) ablation volume was 4.27 (2.92-5.89) cm3, and median ablation energy was 7216 (6402-8784) J. The median MAP, SBP, DBP, and ETCO2 values measured during the 40-minute period leading up to ablation were 72.8 (66.2-81.5) mm Hg, 104.4 (96.4-114.4) mm Hg, 62.4 (54.1-69.8) mm Hg, and 34.1 (32.0-36.2) mm Hg, respectively. In univariate analysis, only total laser energy (r = 0.464, p = 0.003) and 40-minute average ETCO2 (r = -0.388, p = 0.012) were significantly associated with ablation volume. In multivariate analysis, only ETCO2 ≤ 33 mm Hg (p = 0.001) was significantly associated with ablation volume.

CONCLUSIONS

Total ablation energy and ETCO2, but not blood pressure, may significantly affect ablation volume in mTLE patients undergoing MRgLiTT. Mild hypocapnia was associated with increased extent of ablation. Intraoperative monitoring and modulation of ETCO2 may help improve extent of ablation, prediction of ablation volume, and potentially seizure outcome.

Effects of laser interstitial thermal therapy for mesial temporal lobe epilepsy on the structural connectome and its relationship to seizure freedom

OBJECTIVE

Laser interstitial thermal therapy (LITT) is a minimally invasive surgery for mesial temporal lobe epilepsy (mTLE), but the effects of individual patient anatomy and location of ablation volumes affect seizure outcomes. The purpose of this study is to see if features of individual patient structural connectomes predict surgical outcomes after LITT for mTLE.

METHODS

This is a retrospective analysis of seizure outcomes of LITT for mTLE in 24 patients. We use preoperative diffusion tensor imaging (DTI) to simulate changes in structural connectivity after laser ablation. A two-step machine-learning algorithm is applied to predict seizure outcomes from the change in connectomic features after surgery.

RESULTS

Although node-based network features such as clustering coefficient and betweenness centrality have some predictive value, changes in connection strength between mesial temporal regions predict seizure outcomes significantly better. Changes in connection strength between the entorhinal cortex (EC), and the insula, hippocampus, and amygdala, as well as between the temporal pole and hippocampus, predict Engel Class I outcomes with an accuracy of 88%. Analysis of the ablation location, as well as simulated, alternative ablations, reveals that a more medial, anterior, and inferior ablation volume is associated with a greater effect on these connections, and potentially on seizure outcomes.

SIGNIFICANCE

Our results indicate (1) that seizure outcomes can be retrospectively predicted with excellent accuracy using changes in structural connectivity, and (2) that favorable connectomic changes are associated with an ablation volume involving relatively mesial, anterior, and inferior locations. These results may provide a framework whereby individual pre-operative structural connectomes can be used to optimize ablation volumes and improve outcomes in LITT for mTLE.

Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives

BACKGROUND

Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties.

METHODS

A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool.

RESULTS

41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified.

CONCLUSIONS

In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.

Two-trajectory laser amygdalohippocampotomy: Anatomic modeling and initial seizure outcomes

OBJECTIVE

Laser interstitial thermal therapy (LITT) for mesial temporal lobe epilepsy (mTLE) is typically performed with one trajectory to target the medial temporal lobe (MTL). MTL structures such as piriform and entorhinal cortex are epileptogenic, but due to their relative geometry, they are difficult to target with one trajectory while simultaneously maintaining adequate ablation of the amygdala and hippocampus. We hypothesized that a two-trajectory approach could improve ablation of all relevant MTL structures. First, we created large-scale computer simulations to compare idealized one- vs two-trajectory approaches. A two-trajectory approach was then validated in an initial cohort of patients.

METHODS

We used magnetic resonance imaging (MRI) from the Human Connectome Project (HCP) to create subject-specific target structures consisting of hippocampus, amygdala, and piriform/entorhinal/perirhinal cortex. An algorithm searched for safe potential trajectories along the hippocampal axis (catheter one) and along the amygdala-piriform axis (catheter two) and compared this to a single trajectory optimized over all structures. The proportion of each structure ablated at various burn radii was evaluated. A cohort of 11 consecutive patients with mTLE received two-trajectory LITT; demographic, operative, and outcome data were collected.

RESULTS

The two-trajectory approach was superior to the one-trajectory approach at nearly all burn radii for all hippocampal subfields and amygdala nuclei (p < .05). Two-laser trajectories achieved full ablation of MTL cortical structures at physiologically realistic burn radii, whereas one-laser trajectories could not. Five patients with at least 1 year of follow-up (mean = 21.8 months) experienced Engel class I outcomes; 6 patients with less than 1 year of follow-up (mean = 6.6 months) are on track for Engel class I outcomes.

SIGNIFICANCE

Our anatomic analyses and initial clinical results suggest that LITT amygdalohippocampotomy performed via two-laser trajectories may promote excellent seizure outcomes. Future studies are required to validate the long-term clinical efficacy and safety of this approach.

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

Computer Assisted Planning for Curved Laser Interstitial Thermal Therapy

Laser interstitial thermal therapy (LiTT) is a minimally invasive alternative to conventional open surgery for drug-resistant focal mesial temporal lobe epilepsy (MTLE). Recent studies suggest that higher seizure freedom rates are correlated with maximal ablation of the mesial hippocampal head, whilst sparing of the parahippocampal gyrus (PHG) may reduce neuropsychological sequelae. Current commercially available laser catheters are inserted following manually planned straight-line trajectories, which cannot conform to curved brain structures, such as the hippocampus, without causing collateral damage or requiring multiple insertions. The clinical feasibility and potential of curved LiTT trajectories through steerable needles has yet to be investigated. This is the focus of our work. We propose a GPU-accelerated computer-assisted planning (CAP) algorithm for steerable needle insertions that generates optimized curved 3D trajectories with maximal ablation of the amygdalohippocampal complex and minimal collateral damage to nearby structures, while accounting for a variable ablation diameter ( 5-15mm). Simulated trajectories and ablations were performed on 5 patients with mesial temporal sclerosis (MTS), which were identified from a prospectively managed database. The algorithm generated obstacle-free paths with significantly greater target area ablation coverage and lower PHG ablation variance compared to straight line trajectories. The presented CAP algorithm returns increased ablation of the amygdalohippocampal complex, with lower patient risk scores compared to straight-line trajectories. This is the first clinical application of preoperative planning for steerable needle based LiTT. This study suggests that steerable needles have the potential to improve LiTT procedure efficacy whilst improving the safety and should thus be investigated further.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

Surgical planning assistance in keyhole and percutaneous surgery: A systematic review

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.

Refining Planning for Stereoelectroencephalography: A Prospective Validation of Spatial Priors for Computer-Assisted Planning With Application of Dynamic Learning

Objective: Stereoelectroencephalography (SEEG) is a procedure in which many electrodes are stereotactically implanted within different regions of the brain to estimate the epileptogenic zone in patients with drug-refractory focal epilepsy. Computer-assisted planning (CAP) improves risk scores, gray matter sampling, orthogonal drilling angles to the skull and intracerebral length in a fraction of the time required for manual planning. Due to differences in planning practices, such algorithms may not be generalizable between institutions. We provide a prospective validation of clinically feasible trajectories using “spatial priors” derived from previous implantations and implement a machine learning classifier to adapt to evolving planning practices.

Methods: Thirty-two patients underwent consecutive SEEG implantations utilizing computer-assisted planning over 2 years. Implanted electrodes from the first 12 patients (108 electrodes) were used as a training set from which entry and target point spatial priors were generated. CAP was then prospectively performed using the spatial priors in a further test set of 20 patients (210 electrodes). A K-nearest neighbor (K-NN) machine learning classifier was implemented as an adaptive learning method to modify the spatial priors dynamically.

Results: All of the 318 prospective computer-assisted planned electrodes were implanted without complication. Spatial priors developed from the training set generated clinically feasible trajectories in 79% of the test set. The remaining 21% required entry or target points outside of the spatial priors. The K-NN classifier was able to dynamically model real-time changes in the spatial priors in order to adapt to the evolving planning requirements.

Conclusions: We provide spatial priors for common SEEG trajectories that prospectively integrate clinically feasible trajectory planning practices from previous SEEG implantations. This allows institutional SEEG experience to be incorporated and used to guide future implantations. The deployment of a K-NN classifier may improve the generalisability of the algorithm by dynamically modifying the spatial priors in real-time as further implantations are performed.

Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives

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.

Domain Heterogeneity in Radiofrequency Therapies for Pain Relief: A Computational Study with Coupled Models

The objective of the current research work is to study the differences between the predicted ablation volume in homogeneous and heterogeneous models of typical radiofrequency (RF) procedures for pain relief. A three-dimensional computational domain comprising of the realistic anatomy of the target tissue was considered in the present study. A comparative analysis was conducted for three different scenarios: (a) a completely homogeneous domain comprising of only muscle tissue, (b) a heterogeneous domain comprising of nerve and muscle tissues, and (c) a heterogeneous domain comprising of bone, nerve and muscle tissues. Finite-element-based simulations were performed to compute the temperature and electrical field distribution during conventional RF procedures for treating pain, and exemplified here for the continuous case. The predicted results reveal that the consideration of heterogeneity within the computational domain results in distorted electric field distribution and leads to a significant reduction in the attained ablation volume during the continuous RF application for pain relief. The findings of this study could provide first-hand quantitative information to clinical practitioners about the impact of such heterogeneities on the efficacy of RF procedures, thereby assisting them in developing standardized optimal protocols for different cases of interest.

Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

Percutaneous thermal ablation has proven to be an effective modality for treating both benign and malignant tumours in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50°C, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumour destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of-the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non-invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow.

Multicenter validation of automated trajectories for selective laser amygdalohippocampectomy

Objective

Laser interstitial thermal therapy (LITT) is a novel minimally invasive alternative to open mesial temporal resection in drug‐resistant mesial temporal lobe epilepsy (MTLE). The safety and efficacy of the procedure are dependent on the preplanned trajectory and the extent of the planned ablation achieved. Ablation of the mesial hippocampal head has been suggested to be an independent predictor of seizure freedom, whereas sparing of collateral structures is thought to result in improved neuropsychological outcomes. We aim to validate an automated trajectory planning platform against manually planned trajectories to objectively standardize the process.

Methods

Using the EpiNav platform, we compare automated trajectory planning parameters derived from expert opinion and machine learning to undertake a multicenter validation against manually planned and implemented trajectories in 95 patients with MTLE. We estimate ablation volumes of regions of interest and quantify the size of the avascular corridor through the use of a risk score as a marker of safety. We also undertake blinded external expert feasibility and preference ratings.

Results

Automated trajectory planning employs complex algorithms to maximize ablation of the mesial hippocampal head and amygdala, while sparing the parahippocampal gyrus. Automated trajectories resulted in significantly lower calculated risk scores and greater amygdala ablation percentage, whereas overall hippocampal ablation percentage did not differ significantly. In addition, estimated damage to collateral structures was reduced. Blinded external expert raters were significantly more likely to prefer automated to manually planned trajectories.

Significance

Retrospective studies of automated trajectory planning show much promise in improving safety parameters and ablation volumes during LITT for MTLE. Multicenter validation provides evidence that the algorithm is robust, and blinded external expert ratings indicate that the trajectories are clinically feasible. Prospective validation studies are now required to determine if automated trajectories translate into improved seizure freedom rates and reduced neuropsychological deficits.

A Primer on Magnetic Resonance-Guided Laser Interstitial Thermal Therapy for Medically Refractory Epilepsy

Epilepsy surgery that eliminates the epileptogenic focus or disconnects the epileptic network has the potential to significantly improve seizure control in patients with medically intractable epilepsy. Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has been an established option for epilepsy surgery since the US Food and Drug Administration cleared the use of MRgLITT in neurosurgery in 2007. MRgLITT is an ablative stereotactic procedure utilizing heat that is converted from laser energy, and the temperature of the tissue is monitored in real-time by MR thermography. Real-time quantitative thermal monitoring enables titration of laser energy for cellular injury, and it also estimates the extent of tissue damage. MRgLITT is applicable for lesion ablation in cases that the epileptogenic foci are localized and/or deep-seated such as in the mesial temporal lobe epilepsy and hypothalamic hamartoma. Seizure-free outcomes after MRgLITT are comparable to those of open surgery in well-selected patients such as those with mesial temporal sclerosis. Particularly in patients with hypothalamic hamartoma. In addition, MRgLITT can also be applied to ablate multiple discrete lesions of focal cortical dysplasia and tuberous sclerosis complex without the need for multiple craniotomies, as well as disconnection surgery such as corpus callosotomy. Careful planning of the target, the optimal trajectory of the laser probe, and the appropriate parameters for energy delivery are paramount to improve the seizure outcome and to reduce the complication caused by the thermal damage to the surrounding critical structures.