18th Annual Conference of the International Society for Computer Aided Surgery

Purpose

   In planning for a potentially curative resection of the epileptogenic zone in patients with pediatric epilepsy, invasive monitoring with intracranial EEG is often used to localize the seizure onset zone and eloquent cortex. A precise understanding of the location of subdural strip and grid electrodes on the brain surface, and of depth electrodes in the brain in relationship to eloquent areas is expected to facilitate pre-surgical planning.

Methods

   We developed a novel algorithm for the alignment of intracranial electrodes, extracted from post-operative CT, with pre-operative MRI. Our goal was to develop a method of achieving highly accurate localization of subdural and depth electrodes, in order to facilitate surgical planning. Specifically, we created a patient-specific 3D geometric model of the cortical surface from automatic segmentation of a pre-operative MRI, automatically segmented electrodes from post-operative CT, and projected each set of electrodes onto the brain surface after alignment of the CT to the MRI. Also, we produced critical visualization of anatomical landmarks, e.g., vasculature, gyri, sulci, lesions, or eloquent cortical areas, which enables the epilepsy surgery team to accurately estimate the distance between the electrodes and the anatomical landmarks, which might help for better assessment of risks and benefits of surgical resection.

Results

   Electrode localization accuracy was measured using knowledge of the position of placement from 2D intra-operative photographs in ten consecutive subjects who underwent intracranial EEG for pediatric epilepsy. Average spatial accuracy of localization was $1.31\pm 0.69 \text{ mm }$ for all 385 visible electrodes in the photos.

Conclusions

   In comparison with previously reported approaches, our algorithm is able to achieve more accurate alignment of strip and grid electrodes with minimal user input. Unlike manual alignment procedures, our algorithm achieves excellent alignment without time-consuming and difficult judgements from an operator.     

Level set based cerebral vasculature segmentation and diameter quantification in CT angiography

A level set based method is presented for cerebral vascular tree segmentation from computed tomography angiography (CTA) data. The method starts with bone masking by registering a contrast enhanced scan with a low-dose mask scan in which the bone has been segmented. Then an estimate of the background and vessel intensity distributions is made based on the intensity histogram which is used to steer the level set to capture the vessel boundaries. The relevant parameters of the level set evolution are optimized using a training set. The method is validated by a diameter quantification study which is carried out on phantom data, representing ground truth, and 10 patient data sets. The results are compared to manually obtained measurements by two expert observers. In the phantom study, the method achieves similar accuracy as the observers, but is unbiased whereas the observers are biased, i.e., the results are 0.00+/-0.23 vs. -0.32+/-0.23 mm. Also, the method's reproducibility is slightly better than the inter-and intra-observer variability. In the patient study, the method is in agreement with the observers and also, the method's reproducibility -0.04+/-0.17 mm is similar to the inter-observer variability 0.06+/-0.17 mm. Since the method achieves comparable accuracy and reproducibility as the observers, and since the method achieves better performance than the observers with respect to ground truth, we conclude that the level set based vessel segmentation is a promising method for automated and accurate CTA diameter quantification.     

Functional and structural synergy for resolution recovery and partial volume correction in brain PET

Purpose: Positron Emission Tomography (PET) has the unique capability of measuring brain function but its clinical potential is affected by low resolution and lack of morphological detail. Here we propose and evaluate a wavelet synergistic approach that combines functional and structural information from a number of sources (CT, MRI and anatomical probabilistic atlases) for the accurate quantitative recovery of radioactivity concentration in PET images. When the method is combined with anatomical probabilistic atlases, the outcome is a functional volume corrected for partial volume effects.Methods: The proposed method is based on the multiresolution property of the wavelet transform. First, the target PET image and the corresponding anatomical image (CT/MRI/atlas-based segmented MRI) are decomposed into several resolution elements. Secondly, high-resolution components of the PET image are replaced, in part, with those of the anatomical image after appropriate scaling. The amount of structural input is weighted by the relative high frequency signal content of the two modalities. The method was validated on a digital Zubal phantom and clinical data to evaluate its quantitative potential.Results: Simulation studies showed the expected relationship between functional recovery and the amount of correct structural detail provided, with perfect recovery achieved when true images were used as anatomical reference. The use of T1-MRI images brought significant improvements in PET image resolution. However improvements were maximized when atlas-based segmented images as anatomical references were used; these results were replicated in clinical data sets.Conclusion: The synergistic use of functional and structural data, and the incorporation of anatomical probabilistic information in particular, generates morphologically corrected PET images of exquisite quality.     

Repeat CT or MRI in Posttraumatic Headache

Repeat CT or MRI of the brain should be considered in posttraumatic headache. We describe two patients with posttraumatic headache who had negative CT scans on initial presentation. One patient later had bilateral subdural hematomas on CT, and the other had temporal lobe hemorrhage on MRI. We recommend considering repeat CT or MRI for persisting posttraumatic headache and mental status change.     

Estimating tissue deformation between functional images induced by intracranial electrode implantation using anatomical MRI

This paper examines a solution to the general problem of accurately relating points within functional data acquired before and after subdural intracranial electrode implantation. We develop an approach based on nonrigid registration of high resolution anatomical MRI acquired together with the functional data. This makes use of a free-form B-Spline deformation model and registration is recovered by maximizing the normalized mutual information between the preimplant MRI and the postimplant MRI. We apply the approach to estimate the tissue deformation induced by the presence of intracranial electrodes over 15 patient studies. Maximum tissue displacements of 4 mm or greater were observed in all cases either in the cortex or around the ventricles due to CSF loss. In studies involving larger 4 x 4 grids, local tissue displacement estimates exceeded 10 mm from the preimplant brain shape. The key issue with this approach is whether the deformation estimates are contaminated by the presence of susceptibility-induced imaging artifacts. We therefore evaluate the deformation estimates in recovering alignment of essentially identical SPECT studies of eight patients acquired before and after electrode placement. An ROI-based analysis of the variance of resulting subtraction values between pre- and postimplant SPECT was carried out in regions of tissue below electrode grids. Results indicate for all cases a substantial reduction in residual SPECT subtraction artifacts to a level comparable to that in an equivalent region of undeformed tissue.     

Magnetic resonance image tissue classification using a partial volume model

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.     

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.     

A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions

We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.     

Quantitative evaluation of white matter tract DTI parameter changes in gliomas using nonlinear registration

Diffusion tensor imaging (DTI) has been used extensively to investigate white matter architecture in the brain. In the context of neurological disease, quantification of DTI data sets enables objective characterisation of the associated pathological changes. The aim of this study is to propose a method of evaluating DTI parameter changes in gliomas in the internal capsule using nonlinear registration to delineate the white matter and enable quantitative assessment of DTI derived parameters. 20 patients selected pre-operatively with probable grade 2 or grade 3 glioma on structural MRI along with ten normal volunteers were included in this study. DTI fractional anisotropy (FA) maps were used to define a common segmented FA skeleton that was projected back onto the original individual FA maps. Objective segment classification as normal or abnormal was achieved by comparison to prediction intervals of FA and mean diffusivity (MD) defined in normal subjects. The internal capsules of each patient were segmented into 10 regions of interest (ROI) with 20 and 16 segments across the group having significantly increased or decreased FA and MD values respectively. Seven glioma patients had abnormal DTI parameters in the internal capsule. We show that the classification of tract segments was consistent with disruption, oedema or compression. The results suggest that this method could be used to detect changes in eloquent white matter tracts in individual patients.     

Automatic detection of detached and erroneous electrodes in electrical impedance tomography

One unfortunate occurrence in experimental measurements with electrical impedance tomography is electrodes which become detached or poorly connected, such that the measured data cannot be used. This paper presents an automatic approach to detect such erroneous electrodes. It is based on the assumption that all valid measurements are related by the image reconstruction model, while the measurements from erroneous electrodes are unrelated. The method estimates the data at an electrode based on the measurements from all other electrodes, and compares it to the measurements. If these data match adequately, the set of electrodes does not contain an erroneous electrode. In order to detect an erroneous electrode amongst N electrodes, all sets of N-1 electrodes are tested, and the set with the best match between measurements and estimate is identified as the one which excludes the erroneous electrode. The method was tested on simulated and experimental data and showed consistent identification of erroneous electrodes with those made by experts.     

CT和MRI多序列评估脑外伤患者病情

目的分析CT和MRI多序列评估脑外伤患者病况的价值。方法连续收集2018年2月~2020年4月本院收治的脑外伤患者70例的临床资料,所有入院后的患者均进行CT和磁共振T1WI、T2WI、液体衰减翻转翻转恢复序列（FLAIR）、弥散加权成像（DWI）、增强梯度回波T2*加权血管成像（ESWAN）序列扫描检查,记录病灶的位置、数目、形态等,并与手术病理学诊断结果进行对比,分析两种诊断方式对病情的评估价值;于伤后3月进行随访,记录患者有无神经症状,并进行格拉斯哥预后评分,采用Spearman相关进行比较分析。结果60例脑外伤患者中手术诊断硬膜下血肿42例,蛛网膜下腔出血21例,硬膜外血肿17例,脑挫伤23例。MRI在硬膜下血肿、脑挫伤、蛛网膜下腔出血的诊断率高于CT检查（P < 0.05）,两种检查方式在硬膜外血肿检查准确率差异无统计学意义（P>0.05）;60例患者微出血病灶MRI多序列检查中ESWAN检查数目最多,其后依次是FLAIR序列、DWI序列、T2WI序列、T1WI序列,CT与T2WI序列检出数目差异无统计学意义（P>0.05）。微出血灶主要分布在额叶、颞枕顶叶、胼胝体、基底节、丘脑、脑干等区域,ESWAN序列检出出血病灶的总体积为288 557 mm3,以颞枕顶叶白质体积最大为63 153 mm3;60例脑外伤患者ESWAN序列发现出血性病灶数目、体积与患者入院时格拉斯哥昏迷评分之间经Spearman相关检验均存在明显负相关（r=-0.753, P < 0.01;r=-0.736,P < 0.01）,伤后3月的格拉斯哥预后评分与ESWAN序列发现出血性病灶数目、体积负相关（r=-0.648, P < 0.01;r=-0.612,P < 0.01）。结论与CT检查相比,MRI多序列联合检查在脑外伤患者硬膜下血肿、脑挫伤、蛛网膜下腔出血诊断准确率更高,且ESWAN序列在出血性病灶的数目、体积检出方面更有优势,对患者病情及远期预后有重要参考价值。      

Determination of abdominal fat thickness using dual electrode separation in the focused impedance method (FIM)

Subcutaneous fat layer thickness in the abdomen is a risk indicator of several diseases and disorders like diabetes and heart problems and could be used as a measure of fitness. Skinfold measurement using mechanical calipers is simple but prone to error. Ultrasound scanning techniques are yet to be established as accurate methods for this purpose. magnetic resonance imaging (MRI) and computed tomography (CT) scans can provide the answer but are expensive and not available widely. Some initiatives were made earlier to use electrical impedance to this end, but had inadequacies. In the first part of this paper, a 4-electrode focused impedance method (FIM) with different electrode separations has been studied for its possible use in the determination of abdominal fat thickness in a localized region. For this, a saline phantom was designed to provide different electrode separations and different layers of resistive materials adjacent to the electrodes. The background saline simulated the internal organs having low impedance while the resistive layers simulated the subcutaneous fat. The plot of the measured impedance with electrode separation had different 'slopes' for different thicknesses of resistive layers, which offered a method to obtain an unknown thickness of subcutaneous fat layer. In the second part, measurements were performed on seven human subjects using two electrode separations. Fat layer thickness was measured using mechanical calipers. A plot of the above 'slope' against fat thickness could be fitted using a straight line with an R(2) of 0.93. Then this could be used as a calibration curve for the determination of unknown fat thickness. Further work using more accurate CT and MRI measurements would give a better calibration curve for practical use of this non-invasive and low-cost technique in abdominal fat thickness measurement.     

Phantom-based performance evaluation: application to brain segmentation from magnetic resonance images

This paper presents a new technique for assessing the accuracy of segmentation algorithms, applied to the performance evaluation of brain editing and brain tissue segmentation algorithms for magnetic resonance images. We propose performance evaluation criteria derived from the use of the realistic digital brain phantom Brainweb. This 'ground truth' allows us to build distance-based discrepancy features between the edited brain or the segmented brain tissues (such as cerebro-spinal fluid, grey matter and white matter) and the phantom model, taken as a reference. Furthermore, segmentation errors can be spatially determined, and ranged in terms of their distance to the reference. The brain editing method used is the combination of two segmentation techniques. The first is based on binary mathematical morphology and a region growing approach. It represents the initialization step, the results of which are then refined with the second method, using an active contour model. The brain tissue segmentation used is based on a Markov random field model. Segmentation results are shown on the phantom for each method, and on real magnetic resonance images for the editing step; performance is evaluated by the new distance-based technique and corroborates the effective refinement of the segmentation using active contours. The criteria described here can supersede biased visual inspection in order to compare, evaluate and validate any segmentation algorithm. Moreover, provided a 'ground truth' is given, we are able to determine quantitatively to what extent a segmentation algorithm is sensitive to internal parameters, noise, artefacts or distortions.     

Segmentation and measurement of brain structures in MRI including confidence bounds

The advent of new and improved imaging devices has allowed an impressive increase in the accuracy and precision of MRI acquisitions. However, the volumetric nature of the image formation process implies an inherent uncertainty, known as the partial volume effect, which can be further affected by artifacts such as magnetic inhomogeneities and noise. These degradations seriously challenge the application to MRI of any segmentation method, especially on data sets where the size of the object or effect to be studied is small relative to the voxel size, as is the case in multiple sclerosis and schizophrenia. We develop an approach to this problem by estimating a set of bounds on the spatial location of each organ to be segmented. First, we describe a method for 3D segmentation from voxel data which combines statistical classification and geometry-driven segmentation; then we discuss how the partial volume effect is estimated and object measurements are obtained. A comprehensive validation study and a set of results on clinical applications are also described.     

Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter

An application of independent component analysis (ICA) was attempted to develop a method of processing magnetic resonance (MR) images to extract physiologically independent components representing tissue relaxation times and achieve improved visualization of normal and pathologic structures. Anatomical T1-weighted, T2-weighted and proton density images were obtained from 10 normal subjects, 3 patients with brain tumors and 1 patient with multiple sclerosis. The data sets were analyzed using ICA based on the learning rule of Bell and Sejnowski after prewhitening operations. The three independent components obtained from the three original data sets corresponded to (1) short T1 components representing myelin of white matter and lipids, (2) relatively short T1 components representing gray matter and (3) long T2 components representing free water. The involvement of gray or white matter in brain tumor cases and the demyelination in the case of multiple sclerosis were enhanced and visualized in independent component images. ICA can potentially achieve separation of tissues with different relaxation characteristics and generate new contrast images of gray and white matter. With the proper choice of contrast for the original images, ICA may be useful not only for extracting subtle or hidden changes but also for preprocessing transformation before clustering and segmenting the structure of the human brain.     

Localization of dense intracranial electrode arrays using magnetic resonance imaging

Intracranial electrode arrays are routinely used in the pre-surgical evaluation of patients with medically refractory epilepsy, and recordings from these electrodes have been increasingly employed in human cognitive neurophysiology due to their high spatial and temporal resolution. For both researchers and clinicians, it is critical to localize electrode positions relative to the subject-specific neuroanatomy. In many centers, a post-implantation MRI is utilized for electrode detection because of its higher sensitivity for surgical complications and the absence of radiation. However, magnetic susceptibility artifacts surrounding each electrode prohibit unambiguous detection of individual electrodes, especially those that are embedded within dense grid arrays. Here, we present an efficient method to accurately localize intracranial electrode arrays based on pre- and post-implantation MR images that incorporates array geometry and the individual's cortical surface. Electrodes are directly visualized relative to the underlying gyral anatomy of the reconstructed cortical surface of individual patients. Validation of this approach shows high spatial accuracy of the localized electrode positions (mean of 0.96mm±0.81mm for 271 electrodes across 8 patients). Minimal user input, short processing time, and utilization of radiation-free imaging are strong incentives to incorporate quantitatively accurate localization of intracranial electrode arrays with MRI for research and clinical purposes. Co-registration to a standard brain atlas further allows inter-subject comparisons and relation of intracranial EEG findings to the larger body of neuroimaging literature.     

等密度硬膜下血肿的CT诊断

目的 探讨等密度硬膜下血肿的CT诊断，提高对其认识和诊断水平。方法 分析经手术证实的等密度硬膜下血肿患16例，男15例，女1例，年龄38-80岁。结果 16例等密度硬膜下血肿CT表现如下：(1)脑室系统向对侧移位，伴有病侧脑沟消失和病侧蛛网膜下腔闭塞。(2)脑灰白质向内移位。(3)中线结构移位、病侧脑室受压移位及对侧侧脑室的轻度扩大。结论 CT检查．尤其是CT增强是等密度硬膜下血肿术前正确诊断最有价值的诊断方法之一。