Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Quantitative multi-modal analysis of pediatric focal epilepsy

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 101-103).For patients with medically refractive focal epilepsy, surgical intervention to remove the epileptic foci is often the last alternative for permanent cure. The success of such surgery is highly dependent on the doctor's ability to accurately locate the epileptogenic region during the pre-surgical planning and evaluation phase. Hence the goal of this project is to provide an end-to-end quantitative analysis pipeline that fuses an array of imaging modalities including magnetic resonance imaging (MRI), diffusion tensor MRI, positron emission tomography (PET), single-photon emission computerized tomography (SPECT) as well as EEG data to build patient-specific head models and to compute prior probability maps of epileptic hotspots for more accurate EEG source localization. By improving the ability to accurately locate these epileptogenic seizure sources, patients can benefit tremendously from accurate surgical resection and consequently have a better chance for complete seizure free recovery.by Andy Khai Siang Eow.S.M

Quantitative MRI correlates of hippocampal and neocortical pathology in intractable temporal lobe epilepsy

Intractable or drug-resistant epilepsy occurs in over 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to investigate histopathological correlates of quantitative relaxometry and DTI from hippocampal and neocortical specimens of intractable TLE patients. To achieve this goal I developed and evaluated a pipeline for histology to in-vivo MRI image registration, which finds dense spatial correspondence between both modalities. This protocol was divided in two steps whereby sparsely sectioned histology from temporal lobe specimens was first registered to the intermediate ex-vivo MRI which is then registered to the in-vivo MRI, completing a pipeline for histology to in-vivo MRI registration. When correlating relaxometry and DTI with neuronal density and morphology in the temporal lobe neocortex, I found T1 to be a predictor of neuronal density in the neocortical GM and demonstrated that employing multi-parametric MRI (combining T1 and FA together) provided a significantly better fit than each parameter alone in predicting density of neurons. This work was the first to relate in-vivo T1 and FA values to the proportion of neurons in GM. When investigating these quantitative multimodal parameters with histological features within the hippocampal subfields, I demonstrated that MD correlates with neuronal density and size, and can act as a marker for neuron integrity within the hippocampus. More importantly, this work was the first to highlight the potential of subfield relaxometry and diffusion parameters (mainly T2 and MD) as well as volumetry in predicting the extent of cell loss per subfield pre-operatively, with a precision so far unachievable. These results suggest that high-resolution quantitative MRI sequences could impact clinical practice for pre-operative evaluation and prediction of surgical outcomes of intractable epilepsy

Computer-Assisted Planning and Robotics in Epilepsy Surgery

Epilepsy is a severe and devastating condition that affects ~1% of the population. Around 30% of these patients are drug-refractory. Epilepsy surgery may provide a cure in selected individuals with drug-resistant focal epilepsy if the epileptogenic zone can be identified and safely resected or ablated. Stereoelectroencephalography (SEEG) is a diagnostic procedure that is performed to aid in the delineation of the seizure onset zone when non-invasive investigations are not sufficiently informative or discordant. Utilizing a multi-modal imaging platform, a novel computer-assisted planning (CAP) algorithm was adapted, applied and clinically validated for optimizing safe SEEG trajectory planning. In an initial retrospective validation study, 13 patients with 116 electrodes were enrolled and safety parameters between automated CAP trajectories and expert manual plans were compared. The automated CAP trajectories returned statistically significant improvements in all of the compared clinical metrics including overall risk score (CAP 0.57 +/- 0.39 (mean +/- SD) and manual 1.00 +/- 0.60, p < 0.001). Assessment of the inter-rater variability revealed there was no difference in external expert surgeon ratings. Both manual and CAP electrodes were rated as feasible in 42.8% (42/98) of cases. CAP was able to provide feasible electrodes in 19.4% (19/98), whereas manual planning was able to generate a feasible electrode in 26.5% (26/98) when the alternative generation method was not feasible. Based on the encouraging results from the retrospective analysis a prospective validation study including an additional 125 electrodes in 13 patients was then undertaken to compare CAP to expert manual plans from two neurosurgeons. The manual plans were performed separately and blindly from the CAP. Computer-generated trajectories were found to carry lower risks scores (absolute difference of 0.04 mm (95% CI = -0.42-0.01), p = 0.04) and were subsequently implanted in all cases without complication. The pipeline has been fully integrated into the clinical service and has now replaced manual SEEG planning at our institution. Further efforts were then focused on the distillation of optimal entry and target points for common SEEG trajectories and applying machine learning methods to develop an active learning algorithm to adapt to individual surgeon preferences. Thirty-two patients were prospectively enrolled in the study. The first 12 patients underwent prospective CAP planning and implantation following the pipeline outlined in the previous study. These patients were used as a training set and all of the 108 electrodes after successful implantation were normalized to atlas space to generate ‘spatial priors’, using a K-Nearest Neighbour (K-NN) classifier. A subsequent test set of 20 patients (210 electrodes) were then used to prospectively validate the spatial priors. From the test set, 78% (123/157) of the implanted trajectories passed through both the entry and target spatial priors defined from the training set. To improve the generalizability of the spatial priors to other neurosurgical centres undertaking SEEG and to take into account the potential for changing institutional practices, an active learning algorithm was implemented. The K-NN classifier was shown to dynamically learn and refine the spatial priors. The progressive refinement of CAP SEEG planning outlined in this and previous studies has culminated in an algorithm that not only optimizes the surgical heuristics and risk scores related to SEEG planning but can also learn from previous experience. Overall, safe and feasible trajectory schema were returning in 30% of the time required for manual SEEG planning. Computer-assisted planning was then applied to optimize laser interstitial thermal therapy (LITT) trajectory planning, which is a minimally invasive alternative to open mesial temporal resections, focal lesion ablation and anterior 2/3 corpus callosotomy. We describe and validate the first CAP algorithm for mesial temporal LITT ablations for epilepsy treatment. Twenty-five patients that had previously undergone LITT ablations at a single institution and with a median follow up of 2 years were included. Trajectory parameters for the CAP algorithm were derived from expert consensus to maximize distance from vasculature and ablation of the amygdalohippocampal complex, minimize collateral damage to adjacent brain structures whilst avoiding transgression of the ventricles and sulci. Trajectory parameters were also optimized to reduce the drilling angle to the skull and overall catheter length. Simulated cavities attributable to the CAP trajectories were calculated using a 5-15 mm ablation diameter. In comparison to manually planned and implemented LITT trajectories,CAP resulted in a significant increase in the percentage ablation of the amygdalohippocampal complex (manual 57.82 +/- 15.05% (mean +/- S.D.) and unablated medial hippocampal head depth (manual 4.45 +/- 1.58 mm (mean +/- S.D.), CAP 1.19 +/- 1.37 (mean +/- S.D.), p = 0.0001). As LITT ablation of the mesial temporal structures is a novel procedure there are no established standards for trajectory planning. A data-driven machine learning approach was, therefore, applied to identify hitherto unknown CAP trajectory parameter combinations. All possible combinations of planning parameters were calculated culminating in 720 unique combinations per patient. Linear regression and random forest machine learning algorithms were trained on half of the data set (3800 trajectories) and tested on the remaining unseen trajectories (3800 trajectories). The linear regression and random forest methods returned good predictive accuracies with both returning Pearson correlations of ρ = 0.7 and root mean squared errors of 0.13 and 0.12 respectively. The machine learning algorithm revealed that the optimal entry points were centred over the junction of the inferior occipital, middle temporal and middle occipital gyri. The optimal target points were anterior and medial translations of the centre of the amygdala. A large multicenter external validation study of 95 patients was then undertaken comparing the manually planned and implemented trajectories, CAP trajectories targeting the centre of the amygdala, the CAP parameters derived from expert consensus and the CAP trajectories utilizing the machine learning derived parameters. Three external blinded expert surgeons were then selected to undertake feasibility ratings and preference rankings of the trajectories. CAP generated trajectories result in a significant improvement in many of the planning metrics, notably the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.2 (mean +/- S.D.), p<0.000) and overall ablation of the amygdala (manual 45.3 +/- 22.2 % (mean +/- S.D.), CAP 64.2 +/- 20 % (mean +/- S.D.), p<0.000). Blinded external feasibility ratings revealed that manual trajectories were less preferable than CAP planned trajectories with an estimated probability of being ranked 4th (lowest) of 0.62. Traditional open corpus callosotomy requires a midline craniotomy, interhemispheric dissection and disconnection of the rostrum, genu and body of the corpus callosum. In cases where drop attacks persist a completion corpus callosotomy to disrupt the remaining fibres in the splenium is then performed. The emergence of LITT technology has raised the possibility of being able to undertake this procedure in a minimally invasive fashion and without the need for a craniotomy using two or three individual trajectories. Early case series have shown LITT anterior two-thirds corpus callosotomy to be safe and efficacious. Whole-brain probabilistic tractography connectomes were generated utilizing 3-Tesla multi-shell imaging data and constrained spherical deconvolution (CSD). Two independent blinded expert neurosurgeons with experience of performing the procedure using LITT then planned the trajectories in each patient following their current clinical practice. Automated trajectories returned a significant reduction in the risk score (manual 1.3 +/- 0.1 (mean +/- S.D.), CAP 1.1 +/- 0.1 (mean +/- S.D.), p<0.000). Finally, we investigate the different methods of surgical implantation for SEEG electrodes. As an initial study, a systematic review and meta-analysis of the literature to date were performed. This revealed a wide variety of implantation methods including traditional frame-based, frameless, robotic and custom-3D printed jigs were being used in clinical practice. Of concern, all comparative reports from institutions that had changed from one implantation method to another, such as following the introduction of robotic systems, did not undertake parallel-group comparisons. This suggests that patients may have been exposed to risks associated with learning curves and potential harms related to the new device until the efficacy was known. A pragmatic randomized control trial of a novel non-CE marked robotic trajectory guidance system (iSYS1) was then devised. Before clinical implantations began a series of pre-clinical investigations utilizing 3D printed phantom heads from previously implanted patients was performed to provide pilot data and also assess the surgical learning curve. The surgeons had comparatively little clinical experience with the new robotic device which replicates the introduction of such novel technologies to clinical practice. The study confirmed that the learning curve with the iSYS1 devices was minimal and the accuracies and workflow were similar to the conventional manual method. The randomized control trial represents the first of its kind for stereotactic neurosurgical procedures. Thirty-two patients were enrolled with 16 patients randomized to the iSYS1 intervention arm and 16 patients to the manual implantation arm. The intervention allocation was concealed from the patients. The surgical and research team could be not blinded. Trial management, independent data monitoring and trial steering committees were convened at four points doing the trial (after every 8 patients implanted). Based on the high level of accuracy required for both methods, the main distinguishing factor would be the time to achieve the alignment to the prespecified trajectory. The primary outcome for comparison, therefore, was the time for individual SEEG electrode implantation. Secondary outcomes included the implantation accuracy derived from the post-operative CT scan, infection, intracranial haemorrhage and neurological deficit rates. Overall, 32 patients (328 electrodes) completed the trial (16 in each intervention arm) and the baseline demographics were broadly similar between the two groups. The time for individual electrode implantation was significantly less with the iSYS1 device (median of 3.36 (95% CI 5.72 to 7.07) than for the PAD group (median of 9.06 minutes (95% CI 8.16 to 10.06), p=0.0001). Target point accuracy was significantly greater with the PAD (median of 1.58 mm (95% CI 1.38 to 1.82) compared to the iSYS1 (median of 1.16 mm (95% CI 1.01 to 1.33), p=0.004). The difference between the target point accuracies are not clinically significant for SEEG but may have implications for procedures such as deep brain stimulation that require higher placement accuracy. All of the electrodes achieved their respective intended anatomical targets. In 12 of 16 patients following robotic implantations, and 10 of 16 following manual PAD implantations a seizure onset zone was identified and resection recommended. The aforementioned systematic review and meta-analysis were updated to include additional studies published during the trial duration. In this context, the iSYS1 device entry and target point accuracies were similar to those reported in other published studies of robotic devices including the ROSA, Neuromate and iSYS1. The PAD accuracies, however, outperformed the previously published results for other frameless stereotaxy methods. In conclusion, the presented studies report the integration and validation of a complex clinical decision support software into the clinical neurosurgical workflow for SEEG planning. The stereotactic planning platform was further refined by integrating machine learning techniques and also extended towards optimisation of LITT trajectories for ablation of mesial temporal structures and corpus callosotomy. The platform was then used to seamlessly integrate with a novel trajectory planning software to effectively and safely guide the implantation of the SEEG electrodes. Through a single-blinded randomised control trial, the ISYS1 device was shown to reduce the time taken for individual electrode insertion. Taken together, this work presents and validates the first fully integrated stereotactic trajectory planning platform that can be used for both SEEG and LITT trajectory planning followed by surgical implantation through the use of a novel trajectory guidance system

Advanced Magnetic Resonance Imaging and Quantitative Analysis Approaches in Patients with Refractory Focal Epilepsy

Background Epilepsy has a high prevalence of 1%, which makes it the most common serious neurological disorder. The most difficult to treat type of epilepsy is temporal lobe epilepsy (TLE) with its most commonly associated lesion being hippocampal sclerosis (HS). About 30-50% of all patients undergoing resective surgery of epileptogenic tissue continue to have seizures postoperatively. Indication for this type of surgery is only given when lesions are clearly visible on magnetic resonance images (MRI). About 30% of all patients with focal epilepsy do not show an underlying structural lesion upon qualitative neuroradiological MRI assessment (MRI-negative). Objectives The work presented in this thesis uses MRI data to quantitatively investigate structural differences between brains of patients with focal epilepsy and healthy controls using automated imaging preprocessing and analysis methods. Methods All patients studied in this thesis had electrophysiological evidence of focal epilepsy, and underwent routine clinical MRI prior to participation in this study. There were two datasets and both included a cohort of age-matched controls: (i) Patients with TLE and associated HS who later underwent selective amygdalahippocampectomy (cohort 1) and (ii) MRI-negative patients with medically refractory focal epilepsy (cohort 2). The participants received high- resolution routine clinical MRI as well as additional sequences for gray and white matter (GM/WM) structural imaging. A neuroradiologist reviewed all images prior to analysis. Hippocampal subfield volume and automated tractography analysis was performed in patients with TLE and HS and related to post-surgical outcomes, while images of MRI- negative patients were analyzed using voxel-based morphometry (VBM) and manual/automated tractography. All studies were designed to detect quantitative differences between patients and controls, except for the hippocampal subfield analysis as control data was not available and comparisons were limited to patients with persistent postoperative seizures and those without. Results 1. Automated hippocampal subfield analysis (cohort 1): The high-resolution hippocampal subfield segmentation technique cannot establish a link between hippocampal subfield volume loss and post-surgical outcome. Ipsilateral and contralateral hippocampal subfield volumes did not correlate with clinical variables such as duration of epilepsy and age of onset of epilepsy. 2. Automated WM diffusivity analysis (cohort 1): Along-the-tract analysis showed that ipsilateral tracts of patients with right/left TLE and HS were more extensively affected than contralateral tracts and the affected regions within tracts could be specified. The extent of hippocampal atrophy (HA) was not related to (i) the diffusion alterations of temporal lobe tracts or (ii) clinical characteristics of patients, whereas diffusion alterations of ipsilateral temporal lobe tracts were significantly related to age at onset of epilepsy, duration of epilepsy and epilepsy burden.Patients without any postoperative seizure symptoms (excellent outcomes) had more ipsilaterally distributed WM tract diffusion alterations than patients with persistent postoperative seizures (poorer outcomes), who were affected bilaterally. 3. Automated epileptogenic lesion detection (cohort 2): Comparison of individual patients against the controls revealed that focal cortical dysplasia (FCD) can be detected automatically using statistical thresholds. All sites of dysplasia reported at the start of the study were detected using this technique. Two additional sites in two different patients, which had previously escaped neuroradiological assessment, could be identified. When taking these statistical results into account during re-assessment of the dedicated epilepsy research MRI, the expert neuroradiologist was able to confirm these as lesions. 4. Manual and automated WM diffusion tensor imaging (DTI) analysis (cohort 2): The analysis of consistency across approaches revealed a moderate to good agreement between extracted tract shape, morphology and space and a strong correlation between diffusion values extracted with both methods. While whole-tract DTI-metrics determined using Automated Fiber Quantification (AFQ) revealed correlations with clinical variables such as age of onset and duration of epilepsy, these correlations were not found using the manual technique. The manual approach revealed more differences than AFQ in group comparisons of whole-tract DTI-metrics. Along-the-tract analysis provided within AFQ gave a more detailed description of localized diffusivity changes along tracts, which correlated with clinical variables such as age of onset and epilepsy duration. Conclusions While hippocampal subfield volume loss in patients with TLE and HS was not related with any clinical variables or to post-surgical outcomes, WM tract diffusion alterations were more bilaterally distributed in patients with persistent postoperative seizures, compared to patients with excellent outcomes. This may indicate that HS as an initial precipitating injury is not affected by clinical features of the disorder and automated hippocampal subfield mapping based on MRI is not sufficient to stratify patients according to outcome. Presence of persisting seizures may depend on other pathological processes such as seizure propagation through WM tracts and WM integrity. Automated and time-efficient three-dimensional voxel-based analysis may complement conventional visual assessments in patients with MRI-negative focal epilepsy and help to identify FCDs escaping routine neuroradiological assessment. Furthermore, automated along-the-tract analysis may identify widespread abnormal diffusivity and correlations between WM integrity loss and clinical variables in patients with MRI-negative epilepsy. However, automated WM tract analysis may differ from results obtained with manual methods and therefore caution should be exercised when using automated techniques

Cortical Morphology and MRI Signal Intensity Analysis in Paediatric Epilepsy

Epilepsy encompasses a great variety of aetiologies, and as such is not a single disease but a group of diseases characterised by unprovoked seizures.The primary aim of the work presented in this thesis was to use multimodal structural imaging to improve understanding of epilepsy related brain pathology, both the epileptogenic lesions themselves and extralesional pathology, in order to improve pre-surgical planning in medicationresistant epilepsy and improve understanding of the underlying pathogenic mechanisms. The work focuses on 2 epilepsy aetiologies: focal cortical dysplasia (FCD) (chapters 2 and 3) and mesial temporal lobe epilepsy (chapters 4 & 5). Chapter 2 of this thesis develops surface-based, structural MRI post-processing techniques that can be applied to clinical T1 and FLAIR images to complement current MRI-based diagnosis of focal cortical dysplasias. Chapter 3 uses the features developed in Chapter 2 within a machine learning framework to automatically detect FCDs, obtaining 73% sensitivity using a neural network. Chapter 4 develops an in vivo method to explore neocortical gliosis in adults with TLE, while Chapter 5 applies this method to a paediatric cohort. Finally, the concluding chapter discusses contributions, main limitations and outlines options for future research

Interictal Network Dynamics in Paediatric Epilepsy Surgery

Epilepsy is an archetypal brain network disorder. Despite two decades of research elucidating network mechanisms of disease and correlating these with outcomes, the clinical management of children with epilepsy does not readily integrate network concepts. For example, network measures are not used in presurgical evaluation to guide decision making or surgical management plans. The aim of this thesis was to investigate novel network frameworks from the perspective of a clinician, with the explicit aim of finding measures that may be clinically useful and translatable to directly benefit patient care. We examined networks at three different scales, namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and micro (single unit networks) scales, consistently finding network abnormalities in children being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical translation, using frameworks such as IDEAL to robustly assess the impact of these new technologies on management and outcomes. The thesis sets up a platform from which promising computational technology, that utilises brain network analyses, can be readily translated to benefit patient care

New MR imaging techniques in epilepsy

This thesis is concerned with the application of three magnetic resonance (MR) techniques in epilepsy: i.) Fluid attenuated inversion recovery prepared (FLAIR) imaging, ii.) diffusion imaging including diffusion tensor imaging (DTI) and iii.) serial and high resolution imaging of the hippocampus. I assessed the clinical value of fast FLAIR in epilepsy in a study involving 128 patients and of 3D FLAIR in a study involving 10 patients. The conspicuity of neocortical lesions and hippocampal sclerosis was increased. New lesions were detected in 5% of patients. The extent of low grade tumours was best assessed on 3D fast FLAIR images. Fast FLAIR was inferior to standard MR techniques for identifying and heterotopia. I applied newly developed, experimental diffusion imaging techniques. In eight studies using different diffusion imaging techniques involving a total of 50 patients and 54 control subjects I investigated the mobility of water molecules in the human epileptic brain in vivo. I used spin echo diffusion imaging in two studies, echo planar imaging (EPI) based DTI in four studies and EPI diffusion imaging in a patient during focal status epilepticus. Finally, in a preliminary study I attempted to use EPI diffusion imaging as a contrast to visualise transient changes associated with frequent lateralizing spikes. Our findings were: i.) diffusion is increased in hippocampal sclerosis suggesting a loss of structural organization and expansion of the extracellular space, ii.) displaying the directionality (anisotropy) of diffusion is superior to standard imaging to visualise tracts, iii.) anisotropy is reduced in the pyramidal tract in patients with hemiparesis and iv.) in the optic radiation in patients with hemianopia after temporal lobectomy suggesting wallerian degeneration, v.) both developmental and acquired structural abnormalities have a lower anisotropy than normal white matter, vi.) diffusion abnormalities in blunt head trauma are widespread and may include regions which are normal on standard imaging, indicating micro structural damage suggestive of diffuse axonal injury, vii.) focal status epilepticus can be associated with a reduced difflision in the affected cortex, viii.) diffusion imaging may be useful as a contrast for event-related (spike triggered) functional MR imaging. With serial MRI I demonstrated hippocampal volume loss in a patient after generalized status epilepticus and with high resolution imaging of an anatomical specimen and a control subject I showed hippocampal layers on MR images. The results presented in this thesis emphasised the flexibility of MR imaging and its ability to demonstrate abnormalities in vivo. FLAIR imaging is now part of the clinical work up of patients with epilepsy. Diffusion imaging has been shown to be superior to standard imaging to visualise tracts which has far-reaching implications for neurological applications. Diffusion imaging also provides an exciting window to study cerebral micro structure in vivo. Serial imaging allows for the first time the visualisation of temporal changes and high resolution imaging has the prospect of demonstrating hippocampal layers in vivo. MR imaging is a constantly progressing technique. It is hoped that this thesis will help to formulate hypotheses for new MR experiments to study the relationship of dysfunction and structural abnormalities