A deformable digital brain atlas system according to Talairach and Tournoux

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.

Generation of human thalamus atlases from 7 T data and application to intrathalamic nuclei segmentation in clinical 3 T T1-weighted images

The thalamus serves as the central relay station for the brain. It processes and relays sensory and motor signals between different subcortical regions and the cerebral cortex and it can be divided into several neuronal clusters referred to as nuclei. Each of these can possibly be subdivided into sub-nuclei. Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is however a challenging task because the small size of the nuclei and the lack of contrast over the thalamus region in clinically acquired images does not permit the visualization of their boundaries. A number of methods have been developed for thalamus parcellation but the vast majority of these relies on diffusion imaging or functional imaging. The low resolution of these images only permit localizing the largest nuclei. In this work we propose a method to segment smaller nuclei. We first present a protocol to build histological-like atlases from a series of high-field (7 Tesla) MR images acquired with different pulse sequences that each permits to visualize the boundaries of a subset of the nuclei. We use this protocol to scan 9 subjects and we manually delineate 23 thalamic nuclei following the Morel atlas naming convention for each of these subjects. Manual contours for the nuclei are subsequently utilized to create statistical shape models. With these data, we compare four methods for the segmentation of thalamic nuclei in 3 T images we have also acquired for the 9 subjects included in the study: (1) single atlas, (2) multi atlas, (3) statistical shape, and (4) hierarchical statistical shape in which thalamic nuclei are hierarchically fitted to the images, starting from the largest ones. Results of a leave-one-out validation study conducted on the nine image sets we have acquired show that the multi atlas approach improves upon the single atlas approach for most nuclei. Segmentations obtained with the hierarchical statistical shape model yield the highest accuracy, with dice coefficients ranging from 0.53 to 0.90, mean surface errors from 0.27 mm to 0.64 mm, and maximum surface errors from 1.31 mm to 2.52 mm for all nuclei averaged across test cases. This suggests the feasibility of using such approach for localizing thalamic substructures in clinically acquired MR volumes. It may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei.

Rhesus Macaque Brain Atlas Regions Aligned to an MRI Template

To aid in the analysis of rhesus macaque brain images, we aligned digitized anatomical regions from the widely used atlas of Paxinos et al. to a published magnetic resonance imaging (MRI) template based on a large number of subjects. Digitally labelled atlas images were aligned to the template in 2D and then in 3D. The resulting grey matter regions appear qualitatively to be well registered to the template. To quantitatively validate the procedure, MR brain images of 20 rhesus macaques were aligned to the template along with regions drawn by hand in striatal and cortical areas in each subject's MRI. There was good geometric overlap between the hand drawn regions and the template regions. Positron emission tomography (PET) images of the same subjects showing uptake of a dopamine D₂ receptor ligand were aligned to the template space, and good agreement was found between tracer binding measures calculated using the hand drawn and template regions. In conclusion, an anatomically defined set of rhesus macaque brain regions has been aligned to an MRI template and has been validated for analysis of PET imaging in a subset of striatal and cortical areas. The entire set of over 200 regions is publicly available at https://www.nitrc.org/ . Graphical Abstract ᅟ.

Survey of Non-Rigid Registration Tools in Medicine

We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources (n = 22). Exclusions are due to unavailability or inappropriateness. The remaining (n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support; (ii) 7 are built on software platforms, 5 were developed for brain image registration; (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016; (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools; and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively. Researchers in medical image analysis already have a large choice of registration tools freely available. However, the most recently published algorithms may not be included in the tools, yet.

High thickness histological sections as alternative to study the three-dimensional microscopic human sub-cortical neuroanatomy

Stereotaxy is based on the precise image-guided spatial localization of targets within the human brain. Even with the recent advances in MRI technology, histological examination renders different (and complementary) information of the nervous tissue. Although several maps have been selected as a basis for correlating imaging results with the anatomical locations of sub-cortical structures, technical limitations interfere in a point-to-point correlation between imaging and anatomy due to the lack of precise correction for post-mortem tissue deformations caused by tissue fixation and processing. We present an alternative method to parcellate human brain cytoarchitectural regions, minimizing deformations caused by post-mortem and tissue-processing artifacts and enhancing segmentation by means of modified high thickness histological techniques and registration with MRI of the same specimen and into MNI space (ICBM152). A three-dimensional (3D) histological atlas of the human thalamus, basal ganglia, and basal forebrain cholinergic system is displayed. Structure's segmentations were performed in high-resolution dark-field and light-field microscopy. Bidimensional non-linear registration of the histological slices was followed by 3D registration with in situ MRI of the same subject. Manual and automated registration procedures were adopted and compared. To evaluate the quality of the registration procedures, Dice similarity coefficient and normalized weighted spectral distance were calculated and the results indicate good overlap between registered volumes and a small shape difference between them in both manual and automated registration methods. High thickness high-resolution histological slices in combination with registration to in situ MRI of the same subject provide an effective alternative method to study nuclear boundaries in the human brain, enhancing segmentation and demanding less resources and time for tissue processing than traditional methods.

MRI-Based Medial Axis Extraction and Boundary Segmentation of Cranial Nerves Through Discrete Deformable 3D Contour and Surface Models

This paper presents a segmentation technique to identify the medial axis and the boundary of cranial nerves. We utilize a 3-D deformable one-simplex discrete contour model to extract the medial axis of each cranial nerve. This contour model represents a collection of two-connected vertices linked by edges, where vertex position is determined by a Newtonian expression for vertex kinematics featuring internal and external forces, the latter of which include attractive forces toward the nerve medial axis. We exploit multiscale vesselness filtering and minimal path techniques in the medial axis extraction method, which also computes a radius estimate along the path. Once we have the medial axis and the radius function of a nerve, we identify the nerve surface using a two-simplex deformable model, which expands radially and can accommodate any nerve shape. As a result, the method proposed here combines the benefits of explicit contour and surface models, while also achieving a cornerstone for future work that will emphasize shape statistics, static collision with other critical structures, and tree-shape analysis.

A Novel Method for Constructing Histological Section Datasets of the Basal Ganglia in Digitized Human Brain

To investigate the construction of the histological section datasets in the basal ganglia of digitized human brain to provide a reference for the meso-level histological data acquisition. A fresh adult brain from a cadaver with no neurological disease was selected, and tissue blocks of the basal ganglia in the right hemisphere was extracted using the visualization method, followed by pretreatments including gradient dehydrating, gelatin-embedding and setting of calibration points. And then the tissue blocks was cryosectioned into 60-μm-thick coronal sections and the sectional images were captured simultaneously by a digital camera at a fixed position. Two series of sections (one section out of ten) were Nissl-stained with Toluidine blue and immunostained with the calbindin D-28K, respectively. Stained sections were digitized by a high resolution scanner. After alignment and registration, contours of nuclei and different nucleic function divisions in the digital images of stained sections were identified, and then were segmented and labeled using software exploited by ourselves. Datasets of one set of registrated serial sectional images and two sets of registrated histochemically stained images in basal ganglia area were obtained, which provide a histological reference for the neurosurgery and diagnostic imaging. a systematic method of cutting, slicing, staining, data acquisition and image registration of large tissue blocks was established, providing a reference for histological data acquisition on the digital human. Anat Rec, 300:1011-1021, 2017. © 2016 Wiley Periodicals, Inc.

Multimodal 7T Imaging of Thalamic Nuclei for Preclinical Deep Brain Stimulation Applications

Precise neurosurgical targeting of electrode arrays within the brain is essential to the successful treatment of a range of brain disorders with deep brain stimulation (DBS) therapy. Here, we describe a set of computational tools to generate in vivo, subject-specific atlases of individual thalamic nuclei thus improving the ability to visualize thalamic targets for preclinical DBS applications on a subject-specific basis. A sequential nonlinear atlas warping technique and a Bayesian estimation technique for probabilistic crossing fiber tractography were applied to high field (7T) susceptibility-weighted and diffusion-weighted imaging, respectively, in seven rhesus macaques. Image contrast, including contrast within thalamus from the susceptibility-weighted images, informed the atlas warping process and guided the seed point placement for fiber tractography. The susceptibility-weighted imaging resulted in relative hyperintensity of the intralaminar nuclei and relative hypointensity in the medial dorsal nucleus, pulvinar, and the medial/ventral border of the ventral posterior nuclei, providing context to demarcate borders of the ventral nuclei of thalamus, which are often targeted for DBS applications. Additionally, ascending fiber tractography of the medial lemniscus, superior cerebellar peduncle, and pallidofugal pathways into thalamus provided structural demarcation of the ventral nuclei of thalamus. The thalamic substructure boundaries were validated through in vivo electrophysiological recordings and post-mortem blockface tissue sectioning. Together, these imaging tools for visualizing and segmenting thalamus have the potential to improve the neurosurgical targeting of DBS implants and enhance the selection of stimulation settings through more accurate computational models of DBS.

Multi-modal Learning-based Pre-operative Targeting in Deep Brain Stimulation Procedures

Deep brain stimulation, as a primary surgical treatment for various neurological disorders, involves implanting electrodes to stimulate target nuclei within millimeter accuracy. Accurate pre-operative target selection is challenging due to the poor contrast in its surrounding region in MR images. In this paper, we present a learning-based method to automatically and rapidly localize the target using multi-modal images. A learning-based technique is applied first to spatially normalize the images in a common coordinate space. Given a point in this space, we extract a heterogeneous set of features that capture spatial and intensity contextual patterns at different scales in each image modality. Regression forests are used to learn a displacement vector of this point to the target. The target is predicted as a weighted aggregation of votes from various test samples, leading to a robust and accurate solution. We conduct five-fold cross validation using 100 subjects and compare our method to three indirect targeting methods, a state-of-the-art statistical atlas-based approach, and two variations of our method that use only a single modality image. With an overall error of 2.63±1.37mm, our method improves upon the single modality-based variations and statistically significantly outperforms the indirect targeting ones. Our technique matches state-of-the-art registration methods but operates on completely different principles. Both techniques can be used in tandem in processing pipelines operating on large databases or in the clinical flow for automated error detection.

Integrating multimodal information for intraoperative assistance in neurosurgery

Computer-assisted planning of complex neurosurgical interventions benefits from a variety of specific functions and tools. However, commercial planning- and neuronavigation systems are rather restrictive concerning the availability of innovative methods such as novel imaging modalities, fiber tracking algorithms or electrical dipole mapping. In this respect there is a demand for modular neurosurgical planning systems offering flexible interfaces for easy enhancement. Furthermore all relevant planning information should be available within neuron-avigation. In this work we present a planning system providing these capabilities and its suitability and application in a clinical setting. Our Multimodal Planning System (MOPS 3D) offers a variety of tools such as definition of trajectories for minimally invasive surgery, segmentation of ROIs, integration of functional information from atlas maps or magnetoencephalography. It also supplies plugin interfaces for future extensions. For intraoperative application MOPS is coupled with the neuronavigation system Brainlab Vector Vision Cranial/ENT (VVC). We evaluated MOPS in the Department of Neurosurgery at the University Hospital Heidelberg. Surgical planning and navigation was performed in 5 frequently occurring clinical cases. The time necessary for planning was between 5 and 15 minutes including data import, segmentation and planning tasks. The additional information intraoperatively provided by MOPS 3D was highly appreciated by the neurosurgeons and the performance was satisfactory.

In Vivo 7T MRI of the Non-Human Primate Brainstem

Structural brain imaging provides a critical framework for performing stereotactic and intraoperative MRI-guided surgical procedures, with procedural efficacy often dependent upon visualization of the target with which to operate. Here, we describe tools for in vivo, subject-specific visualization and demarcation of regions within the brainstem. High-field 7T susceptibility-weighted imaging and diffusion-weighted imaging of the brain were collected using a customized head coil from eight rhesus macaques. Fiber tracts including the superior cerebellar peduncle, medial lemniscus, and lateral lemniscus were identified using high-resolution probabilistic diffusion tractography, which resulted in three-dimensional fiber tract reconstructions that were comparable to those extracted from sequential application of a two-dimensional nonlinear brain atlas warping algorithm. In the susceptibility-weighted imaging, white matter tracts within the brainstem were also identified as hypointense regions, and the degree of hypointensity was age-dependent. This combination of imaging modalities also enabled identifying the location and extent of several brainstem nuclei, including the periaqueductal gray, pedunculopontine nucleus, and inferior colliculus. These clinically-relevant high-field imaging approaches have potential to enable more accurate and comprehensive subject-specific visualization of the brainstem and to ultimately improve patient-specific neurosurgical targeting procedures, including deep brain stimulation lead implantation.

High-resolution imaging of the central nervous system: how novel imaging methods combined with navigation strategies will advance patient care

This narrative review captures a subset of recent advances in imaging of the central nervous system. First, we focus on improvements in the spatial and temporal profile afforded by optical coherence tomography, fluorescence-guided surgery, and Coherent Anti-Stokes Raman Scattering Microscopy. Next, we highlight advances in the generation and uses of imaging-based atlases and discuss how this will be applied to specific clinical situations. To conclude, we discuss how these and other imaging tools will be combined with neuronavigation techniques to guide surgeons in the operating room. Collectively, this work aims to highlight emerging biomedical imaging strategies that hold potential to be a valuable tool for both clinicians and researchers in the years to come.

Estimation of brain internal structures by deforming brain atlas using finite element method

This paper presents a method for estimating the internal structures of a patient brain by deforming a standard brain atlas. Conventional deformation methods need several landmarks from the brain surface contour to fit the atlas to the patient brain shape. However, since the number and shapes of small sulci on the brain surface are different from each other, the determination of the accurate correspondence between small sulcus is difficult for experienced neurosurgeons. Moreover, the relationship between the surface shape and internal structure of the brain is unclear. Therefore, even if the deformed atlas is fitted to the patient brain shape exactly, the use of the deformed atlas does not always guarantee the reliable estimation of the internal structure of the patient brain. To solve these problems, we propose a new method for estimate the internal structure of a patient brain by the finite element method (FEM). In the deformation, our method select the landmarks from the contours of both the brain surface and the detectable internal structures from MR images.

Atlas learning in fetal brain development

Magnetic resonance imaging has become an important noninvasive technique to gain insight into fetal brain development. Its capabilities go beyond ultrasound when diagnosing high-risk pregnancies. To summarize observations across a population in magnetic resonance imaging studies, reference systems such as atlases that establish correspondences across a cohort are key. In this article, we review the evolution of atlas-building methods in light of their relevance, limitations, and benefits for the modeling of human brain development. Starting with single anatomical templates to which brain scans where mapped to such as Talairach and Montreal Neurological Institute space, we explore the uses of atlases as a means to establish correspondences across a cohort and as a model that captures the population characteristics of the cases the atlas is built from. We discuss methods that capture features of increasingly heterogeneous populations and approaches that are able to generalize with only minimal annotation. The main focus of this review are methods that explicitly model the variability in the population with regard to time, such as in the modeling of disease progression and brain development. We highlight the applicability and limitations of state-of-the art approaches, how insights from the study of disease progression are helpful in developmental studies, and point to the directions of future research that is still necessary.

Deformable medical image registration: a survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

Nonrigid point registration for 2D curves and 3D surfaces and its various applications

A nonrigid B-spline-based point-matching (BPM) method is proposed to match dense surface points. The method solves both the point correspondence and nonrigid transformation without features extraction. The registration method integrates a motion model, which combines a global transformation and a B-spline-based local deformation, into a robust point-matching framework. The point correspondence and deformable transformation are estimated simultaneously by fuzzy correspondence and by a deterministic annealing technique. Prior information about global translation, rotation and scaling is incorporated into the optimization. A local B-spline motion model decreases the degrees of freedom for optimization and thus enables the registration of a larger number of feature points. The performance of the BPM method has been demonstrated and validated using synthesized 2D and 3D data, mouse MRI and micro-CT images. The proposed BPM method can be used to register feature point sets, 2D curves, 3D surfaces and various image data.

Structural brain atlases: design, rationale, and applications in normal and pathological cohorts

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer's disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities.

Multimodal imaging and image analysis techniques for neuromodulation

Functional neurosurgical procedures used to treat the debilitating motor symptoms of Parkinson's disease and that target small subcortical structures have typically relied on semi-qualitative manual approaches that rely upon the establishing qualitative between volumetric imaging data and print atlases. This chapter reviews many new high -precision and -accuracy techniques that can be used for the full automated localization of these targets. These techniques rely on the a priori development of neuroanatomical atlases derived from magnetic resonance imaging data, high-resolution identification of subcortical structures from histology, and spatially localized data bases of intra-operative recordings and successful surgical outcomes. Other novel structural and functional MRI techniques that allow for the direct visualization of thalamic sub nuclei are also reviewed.

A Three-dimensional Deformable Brain Atlas for DBS Targeting. I. Methodology for Atlas Creation and Artifact Reduction

Background:

Targeting in deep brain stimulation (DBS) relies heavily on the ability to accurately localize particular anatomic brain structures. Direct targeting of subcortical structures has been limited by the ability to visualize relevant DBS targets.

Methods and Results:

In this work, we describe the development and implementation, of a methodology utilized to create a three dimensional deformable atlas for DBS surgery. This atlas was designed to correspond to the print version of the Schaltenbrand-Bailey atlas structural contours. We employed a smoothing technique to reduce artifacts inherent in the print version.

Conclusions:

We present the methodology used to create a three dimensional patient specific DBS atlas which may in the future be tested for clinical utility.

Medical image registration: a review

This paper presents a review of automated image registration methodologies that have been used in the medical field. The aim of this paper is to be an introduction to the field, provide knowledge on the work that has been developed and to be a suitable reference for those who are looking for registration methods for a specific application. The registration methodologies under review are classified into intensity or feature based. The main steps of these methodologies, the common geometric transformations, the similarity measures and accuracy assessment techniques are introduced and described.

Brain MRI segmentation with multiphase minimal partitioning: a comparative study

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

Deformable registration of glioma images using EM algorithm and diffusion reaction modeling

This paper investigates the problem of atlas registration of brain images with gliomas. Multiparametric imaging modalities (T1, T1-CE, T2, and FLAIR) are first utilized for segmentations of different tissues, and to compute the posterior probability map (PBM) of membership to each tissue class, using supervised learning. Similar maps are generated in the initially normal atlas, by modeling the tumor growth, using reaction-diffusion equation. Deformable registration using a demons-like algorithm is used to register the patient images with the tumor bearing atlas. Joint estimation of the simulated tumor parameters (e.g., location, mass effect and degree of infiltration), and the spatial transformation is achieved by maximization of the log-likelihood of observation. An expectation-maximization algorithm is used in registration process to estimate the spatial transformation and other parameters related to tumor simulation are optimized through asynchronous parallel pattern search (APPSPACK). The proposed method has been evaluated on five simulated data sets created by statistically simulated deformations (SSD), and fifteen real multichannel glioma data sets. The performance has been evaluated both quantitatively and qualitatively, and the results have been compared to ORBIT, an alternative method solving a similar problem. The results show that our method outperforms ORBIT, and the warped templates have better similarity to patient images.

Visualization and navigation system development and application for stereotactic deep-brain neurosurgeries

We present the development of a visualization and navigation system and its application in pre-operative planning and intra-operative guidance of stereotactic deep-brain neurosurgical procedures for the treatment of Parkinson's disease, chronic pain, and essential tremor. This system incorporates a variety of standardized functional and anatomical information, and is capable of non-rigid registration, interactive manipulation, and processing of clinical image data. The integration of a digitized and segmented brain atlas, an electrophysiological database, and collections of final surgical targets from previous patients facilitates the delineation of surgical targets and surrounding structures, as well as functional borders. We conducted studies to compare the surgical target locations identified by an experienced stereotactic neurosurgeon using multiple electrophysiological exploratory trajectories with those located by a non-expert using this system on 70 thalamotomy, pallidotomy, thalamic deep-brain stimulation (DBS), and subthalamic nucleus (STN) DBS procedures. The average displacement between the surgical target locations in both groups was 1.95 +/- 0.86 mm, 1.83 +/- 1.07 mm, 1.88 +/- 0.89 mm and 1.61 +/- 0.67 mm for each category of surgeries, respectively, indicating the potential value of our system in stereotactic deep-brain neurosurgical procedures, and demonstrating its capability for accurate surgical target initiation.

Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications

Digital atlases are commonly used in pre-operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas-to-patient warping procedures. Once fitted to pre-operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece-wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas-to-patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas-based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece-wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas-to-patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece-wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results.

Quantification of spatial consistency in the Talairach and Tournoux stereotactic atlas

BACKGROUND

The Talairach-Tournoux (TT) atlas is one of the most prevalent brain atlases. Although its spatial inconsistencies were reported earlier, there has been no systematic quantification of them across the entire atlas, which is addressed here.

METHOD

The consistency of the TT atlas, defined as uniformity of labeling across all three orthogonal atlas orientations, is calculated and presented as maps. It is analyzed in function of discrepancy measuring spatial offset in labeling.

FINDINGS

The TT atlas has 27.4% consistency and 37.7% inconsistency. The most consistent structure is the thalamus (85.7% consistency, 5.4% inconsistency). The consistency of the basal ganglia is good. For 3-mm discrepancy, the inconsistency of major subcortical gray matter structures is very low: 0% (globus pallidus medial and putamen), 0.7% (thalamus), 2.2% (globus pallidus lateral), 4.8% (hippocampus) and 4.9% (caudate nucleus). The inconsistency of all subcortical structures is relatively high (16.8%), caused by a very high inconsistency of white matter tracts. The consistency of stereotactic targets is 69.2% (GPi), 50.0% (STN) and 42.9% (VPL). The overall TT consistency increases by 20% for 1-mm discrepancy, constantly grows by 10% for 2-4-mm discrepancy and slows down to 3% for 5-6-mm discrepancy.

CONCLUSION

This work enhances our understanding of the TT atlas and its variable spatial consistency. It is helpful in using multiple atlas orientations simultaneously. It also may be useful in atlas interpolation and construction of a fully consistent 3D atlas. As the consistency of the main stereotactic targets is medium, the use of the TT atlas in stereotactic procedures requires a great deal of care and understanding of its limitations.

Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth

Although a variety of diffeomorphic deformable registration methods exist in the literature, application of these methods in the presence of space-occupying lesions is not straightforward. The motivation of this work is spatial normalization of MR images from patients with brain tumors in a common stereotaxic space, aiming to pool data from different patients into a common space in order to perform group analyses. Additionally, transfer of structural and functional information from neuroanatomical brain atlases into the individual patient's space can be achieved via the inverse mapping, for the purpose of segmenting brains and facilitating surgical or radiotherapy treatment planning. A method that estimates the brain tissue loss and replacement by tumor is applied for achieving equivalent image content between an atlas and a patient's scan, based on a biomechanical model of tumor growth. Automated estimation of the parameters modeling brain tissue loss and displacement is performed via optimization of an objective function reflecting feature-based similarity and elastic stretching energy, which is optimized in parallel via APPSPACK (Asynchronous Parallel Pattern Search). The results of the method, applied to 21 brain tumor patients, indicate that the registration accuracy is relatively high in areas around the tumor, as well as in the healthy portion of the brain. Also, the calculated deformation in the vicinity of the tumor is shown to correlate highly with expert-defined visual scores indicating the tumor mass effect, thereby potentially leading to an objective approach to quantification of mass effect, which is commonly used in diagnosis.

Automated 3-dimensional brain atlas fitting to microelectrode recordings from deep brain stimulation surgeries

OBJECTIVE

Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient.

METHODS

We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact.

RESULTS

The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance.

CONCLUSIONS AND SIGNIFICANCE

DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease

OBJECT

The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients.

METHODS

Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces.

RESULTS

Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus.

CONCLUSIONS

This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Accelerated detection of intracranial space-occupying lesions with CUDA based on statistical texture atlas in brain HRCT

In this paper, we present a method that detects intracranial space-occupying lesions in two-dimensional (2D) brain high-resolution CT images. Use of statistical texture atlas technique localizes anatomy variation in the gray level distribution of brain images, and in turn, identifies the regions with lesions. The statistical texture atlas involves 147 HRCT slices of normal individuals and its construction is extremely time-consuming. To improve the performance of atlas construction, we have implemented the pixel-wise texture extraction procedure on Nvidia 8800GTX GPU with Compute Unified Device Architecture (CUDA) platform. Experimental results indicate that the extracted texture feature is distinctive and robust enough, and is suitable for detecting uniform and mixed density space-occupying lesions. In addition, a significant speedup against straight forward CPU version was achieved with CUDA.

ORBIT: a multiresolution framework for deformable registration of brain tumor images

A deformable registration method is proposed for registering a normal brain atlas with images of brain tumor patients. The registration is facilitated by first simulating the tumor mass effect in the normal atlas in order to create an atlas image that is as similar as possible to the patient's image. An optimization framework is used to optimize the location of tumor seed as well as other parameters of the tumor growth model, based on the pattern of deformation around the tumor region. In particular, the optimization is implemented in a multiresolution and hierarchical scheme, and it is accelerated by using a principal component analysis (PCA)-based model of tumor growth and mass effect, trained on a computationally more expensive biomechanical model. Validation on simulated and real images shows that the proposed registration framework, referred to as ORBIT (optimization of tumor parameters and registration of brain images with tumors), outperforms other available registration methods particularly for the regions close to the tumor, and it has the potential to assist in constructing statistical atlases from tumor-diseased brain images.

Automatic target and trajectory identification for deep brain stimulation (DBS) procedures

This paper presents an automatic surgical target and trajectory identification technique for planning deep brain stimulation (DBS) procedures. The probabilistic functional maps, constructed from population-based actual stimulating field information and intra-operative electrophysiological activities, were integrated into a neurosurgical visualization and navigation system to facilitate the surgical planning and guidance. In our preliminary studies, we compared the actual surgical target locations and trajectories established by an experienced stereotactic neurosurgeon with those automatically planned using our probabilistic functional maps on 10 subthalamic nucleus (STN) DBS procedures. The average displacement between the surgical target locations in both groups was 1.82mm with a standard deviation of 0.77mm. The difference between the surgical trajectories was 3.1 degrees and 2.3 degrees in the lateral-to-medial and anterior-to-posterior orientations respectively.

Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context

BACKGROUND AND PURPOSE

Conformal radiation therapy techniques require the delineation of volumes of interest, a time-consuming and operator-dependent task. In this work, we aimed to evaluate the potential interest of an atlas-based automatic segmentation software (ABAS) of brain organs at risk (OAR), when used under our clinical conditions.

MATERIALS AND METHODS

Automatic and manual segmentations of the eyes, optic nerves, optic chiasm, pituitary gland, brain stem and cerebellum of 11 patients on T1-weighted magnetic resonance, 3-mm thick slice images were compared using the Dice similarity coefficient (DSC). The sensitivity and specificity of the ABAS were also computed and analysed from a radiotherapy point of view by splitting the ROC (Receiver Operating Characteristic) space into four sub-regions.

RESULTS

Automatic segmentation of OAR was achieved in 7-8 min. Excellent agreement was obtained between automatic and manual delineations for organs exceeding 7 cm3: the DSC was greater than 0.8. For smaller structures, the DSC was lower than 0.41.

CONCLUSIONS

These tests demonstrated that this ABAS is a robust and reliable tool for automatic delineation of large structures under clinical conditions in our daily practice, even though the small structures must continue to be delineated manually by an expert.

Comparison study of clinical 3D MRI brain segmentation evaluation

Although numerous methods to segment brain MRI for extraction of white matter, gray matter and cerebrospinal fluid (CSF) have been proposed for the past two decades, little work has been done to evaluate and compare the performance of different segmentation methods on real clinical data sets, especially for CSF. This study focuses on the comparison of the four following methods for segmentation of cerebral brain MRI: gray levels thresholding, three-dimensional level set, fuzzy connectedness and FSL. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation on a database of 10 adult subjects.

Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method

Quantitative assessment of metastatic disease in bone is often considered immeasurable and, as such, patients with skeletal metastases are often excluded from clinical trials. In order to effectively quantify the impact of metastatic tumor involvement in the spine, accurate segmentation of the vertebra is required. Manual segmentation can be accurate but involves extensive and time-consuming user interaction. Potential solutions to automating segmentation of metastatically involved vertebrae are demons deformable image registration and level set methods. The purpose of this study was to develop a semiautomated method to accurately segment tumor-bearing vertebrae using the aforementioned techniques. By maintaining morphology of an atlas, the demons-level set composite algorithm was able to accurately differentiate between trans-cortical tumors and surrounding soft tissue of identical intensity. The algorithm successfully segmented both the vertebral body and trabecular centrum of tumor-involved and healthy vertebrae. This work validates our approach as equivalent in accuracy to an experienced user.

A digital pediatric brain structure atlas from T1-weighted MR images

Human brain atlases are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1-weighted MR data set of a 9-year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. We constructed a 3D triangular mesh model for each structure by triangulation of the structure's reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/brainatlas) can be used to plan treatment, to conduct knowledge and model-driven segmentation, and to analyze the shapes of brain structures in pediatric patients.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

Fast Talairach Transformation for magnetic resonance neuroimages

We introduce and validate the Fast Talairach Transformation (FTT). FTT is a rapid version of the Talairach transformation (TT) with the modified Talairach landmarks. Landmark identification is fully automatic and done in 3 steps: calculation of midsagittal plane, computing of anterior commissure (AC) and posterior commissure (PC) landmarks, and calculation of cortical landmarks. To perform these steps, we use fast and anatomy-based algorithms employing simple operations. FTT was validated for 215 diversified T1-weighted and spoiled gradient recalled (SPGR) MRI data sets. It calculates the landmarks and warps the Talairach-Tournoux atlas fully automatically in about 5 sec on a standard computer. The average distance errors in landmark localization are (in mm): 1.16 (AC), 1.49 (PC), 0.08 (left), 0.13 (right), 0.48 (anterior), 0.16 (posterior), 0.35 (superior), and 0.52 (inferior). Extensions to FTT by introducing additional landmarks and applying nonlinear warping against the ventricular system are addressed. Application of FTT to other brain atlases of anatomy, function, tracts, cerebrovasculature, and blood supply territories is discussed. FTT may be useful in a clinical setting and research environment: (1) when the TT is used traditionally, (2) when a global brain structure positioning with quick searching and labeling is required, (3) in urgent cases for quick image interpretation (eg, acute stroke), (4) when the difference between nonlinear and piecewise linear warping is negligible, (5) when automatic processing of a large number of cases is required, (6) as an initial atlas-scan alignment before performing nonlinear warping, and (7) as an initial atlas-guided segmentation of brain structures before further local processing.

Development and application of functional databases for planning deep-brain neurosurgical procedures

This work presents the development and application of a visualization and navigation system for planning deep-brain neurosurgeries. This system, which incorporates a digitized and segmented brain atlas, an electrophysiological database, and collections of final surgical targets of previous patients, provides assistance for non-rigid registration, navigation, and reconstruction of clinical image data. The fusion of standardized anatomical and functional data, once registered to individual patient images, facilitates the delineation of surgical targets. Our preliminary studies compared the target locations identified by a non-expert using this system with those located by an experienced neurosurgeon using regular technique on 8 patients who had undergone subthalamic nucleus (STN) deep-brain stimulations (DBS). The average displacement between the surgical target locations in both groups was 0.58 mm +/- 0.49 mm, 0.70 mm +/- 0.37 mm, and 0.69 mm +/- 0.34 mm in x, y, and z directions respectively, indicating the capability of accurate surgical target initiation of our system, which has also shown promise in planning and guidance for other stereotactic deep-brain neurosurgical procedures.

The creation of a brain atlas for image guided neurosurgery using serial histological data

Digital and print brain atlases have been used with success to help in the planning of neurosurgical interventions. In this paper, a technique presented for the creation of a brain atlas of the basal ganglia and the thalamus derived from serial histological data. Photographs of coronal histological sections were digitized and anatomical structures were manually segmented. A slice-to-slice nonlinear registration technique was used to correct for spatial distortions introduced into the histological data set at the time of acquisition. Since the histological data were acquired without any anatomical reference (e.g., block-face imaging, post-mortem MRI), this registration technique was optimized to use an error metric which calculates a nonlinear transformation minimizing the mean distance between the segmented contours between adjacent pairs of slices in the data set. A voxel-by-voxel intensity correction field was also estimated for each slice to correct for lighting and staining inhomogeneity. The reconstructed three-dimensional (3D) histological volume can be viewed in transverse and sagittal directions in addition to the original coronal. Nonlinear transformations used to correct for spatial distortions of the histological data were applied to the segmented structure contours. These contours were then tessellated to create three-dimensional geometric objects representing the different anatomic regions in register with the histological volumes. This yields two alternate representations (one histological and one geometric) of the atlas. To register the atlas to a standard reference MR volume created from the average of 27 T1-weighted MR volumes, a pseudo-MRI was created by setting the intensity of each anatomical region defined in the geometric atlas to match the intensity of the corresponding region of the reference MR volume. This allowed the estimation of a 3D nonlinear transformation using a correlation based registration scheme to fit the atlas to the reference MRI. The result of this procedure is a contiguous 3D histological volume, a set of 3D objects defining the basal ganglia and thalamus, both of which are registered to a standard MRI data set, for use for neurosurgical planning.

Comparison of different targeting methods for subthalamic nucleus deep brain stimulation

The subthalamic nucleus (STN) has been adopted as a commonly used surgical target in deep brain stimulation (DBS) procedures for the treatment of Parkinson's disease. Many techniques have been developed to facilitate STN DBS targeting, and consequently to improve the surgical outcome. In this work, we conducted a retrospective study on 10 patients who were treated with bilateral STN DBS to assess the target localization accuracy and precision of six methods in STN DBS surgery. A visualization and navigation system integrated with normalized functional and anatomical information was employed to perform the targeting procedures. Actual surgical target location determined by an experienced neurosurgeon with pre-operative image-guided surgical target/trajectory planning and intra-operative electrophysiological exploration and confirmation was considered as the "gold standard" in this evaluation and was compared with those localized using each of the six targeting methods. The mean distance between the actual surgical targets and those planned was 3.0 +/- 1.3 mm, 3.2 +/- 1.1 mm, 2.9 +/- 1.1 mm, 2.7 +/- 1.2 mm, 2.5 +/- 1.0 mm, and 1.7 +/- 0.8 mm for targeting approaches based on T2-weighted magnetic resonance image (MRI), brain atlas, T1 and T2 maps, electrophysiological database, collection of final surgical targets of previous patients, and the combination of these functional and anatomical data respectively. The results demonstrated that the use of functional data along with anatomical data provides reliable and accurate target position for STN DBS.