Atlas-assisted localization analysis of functional images

Human brain atlasing: past, present and future

We have recently witnessed an explosion of large-scale initiatives and projects addressing mapping, modeling, simulation and atlasing of the human brain, including the BRAIN Initiative, the Human Brain Project, the Human Connectome Project (HCP), the Big Brain, the Blue Brain Project, the Allen Brain Atlas, the Brainnetome, among others. Besides these large and international initiatives, there are numerous mid-size and small brain atlas-related projects. My contribution to these global efforts has been to create adult human brain atlases in health and disease, and to develop atlas-based applications. For over two decades with my R&D lab I developed 35 brain atlases, licensed to 67 companies and made available in about 100 countries. This paper has two objectives. First, it provides an overview of the state of the art in brain atlasing. Second, as it is already 20 years from the release of our first brain atlas, I summarise my past and present efforts, share my experience in atlas creation, validation and commercialisation, compare with the state of the art, and propose future directions.

Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model and Multiscale Statistical Features

OBJECTIVE

The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images.

METHODS

We propose a segmentation-guided partially-joint regression forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization method to extract high-level multiscale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts.

RESULTS

Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than 2 mm.

CONCLUSION

Our model has addressed challenges of both interpatient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization.

SIGNIFICANCE

Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency.

Automatic detection of the anterior and posterior commissures on MRI scans using regression forests

Identification of the anterior and posterior commissure is crucial in stereotactic and functional neurosurgery, human brain mapping, and medical image processing. We present a learning-based algorithm to automatically and rapidly localize these landmarks using random forests regression. Given a point in the image, we extract a set of multi-scale long-range textural features, and associate a probability for this point to be the landmark. We build random forests models to learn the relationship between the value of these features and the probability of a point to be a landmark point. Three-stage coarse-to-fine models are trained for AC and PC separately using down-sampled by 4, down-sampled by 2, and the original images. Testing is performed in a hierarchical approach to first obtain a rough estimation at the coarse level and then fine-tune the landmark position. We extensively evaluate our method in a leave-one-out fashion using a large dataset of 100 T1-weighted images. We also compare our method to the state-of-art AC/PC detection methods including an atlas-based approach with six well-established nonrigid registration algorithms and a publicly available implementation of a model-based approach. Our method results in an overall error of 0.84±0.41mm for AC, 0.83±0.36mm for PC and a maximum error of 2.04mm; it performs significantly better than the model-based AC/PC detection method we compare it to and better than three of the nonrigid registration methods. It is much faster than nonrigid registration methods.

Usefulness of brain atlases in neuroradiology: Current status and future potential

Human brain atlases, although prevalent in medical education and stereotactic and functional neurosurgery, are not yet applied practically in neuroradiology. In a step towards introducing brain atlases to neuroradiology, we discuss nine different situations of potential atlas use: (1) to support interpretation of brain scans with clearly visible structures (to increase confidence of non-neuroradiologists); (2) to delineate and label scans of low anatomical content (with indiscernible or poorly visible anatomy); (3) to assist in generating the structured report; (4) to assist in interpreting small deep lesions, since an atlas's anatomical parcellation is higher than that of the interpreted scan; (5) to approximate distorted due to pathology (and unknown to the interpreter) anatomy and label it; (6) to cope with data explosion; (7) to assist in the interpretation of functional scans (to label the activation foci with the underlying anatomy and Brodmann's areas); (8) to support ischemic stroke image handling by means of atlases of anatomy and blood supply territories; and (9) to communicate image interpretation results (diagnosis) to others. The usefulness of the atlas for automatic structure identification, localisation, delineation, labelling and quantification, as well as for reporting and communication, potentially increases the interpreter's efficiency and confidence, as well as expedites image interpretation.

Automatic localization of the anterior commissure, posterior commissure, and midsagittal plane in MRI scans using regression forests

Localizing the anterior and posterior commissures (AC/PC) and the midsagittal plane (MSP) is crucial in stereotactic and functional neurosurgery, human brain mapping, and medical image processing. We present a learning-based method for automatic and efficient localization of these landmarks and the plane using regression forests. Given a point in an image, we first extract a set of multiscale long-range contextual features. We then build random forests models to learn a nonlinear relationship between these features and the probability of the point being a landmark or in the plane. Three-stage coarse-to-fine models are trained for the AC, PC, and MSP separately using downsampled by 4, downsampled by 2, and the original images. Localization is performed hierarchically, starting with a rough estimation that is progressively refined. We evaluate our method using a leave-one-out approach with 100 clinical T1-weighted images and compare it to state-of-the-art methods including an atlas-based approach with six nonrigid registration algorithms and a model-based approach for the AC and PC, and a global symmetry-based approach for the MSP. Our method results in an overall error of 0.55 ±0.30 mm for AC, 0.56 ±0.28 mm for PC, 1.08(°) ±0.66 in the plane's normal direction, and 1.22 ±0.73 voxels in average distance for MSP; it performs significantly better than four registration algorithms and the model-based method for AC and PC, and the global symmetry-based method for MSP. We also evaluate the sensitivity of our method to image quality and parameter values. We show that it is robust to asymmetry, noise, and rotation. Computation time is 25 s.

Toward the holistic, reference, and extendable atlas of the human brain, head, and neck

Despite numerous efforts, a fairly complete (holistic) anatomical model of the whole, normal, adult human brain, which is required as the reference in brain studies and clinical applications, has not yet been constructed. Our ultimate objective is to build this kind of atlas from advanced in vivo imaging. This work presents the taxonomy of our currently developed brain atlases and addresses the design, content, functionality, and current results in the holistic atlas development as well as atlas usefulness and future directions. We have developed to date 35 commercial brain atlases (along with numerous research prototypes), licensed to 63 companies and institutions, and made available to medical societies, organizations, medical schools, and individuals. These atlases have been applied in education, research, and clinical applications. Hundreds of thousands of patients have been treated by using our atlases. Based on this experience, the first version of the holistic and reference atlas of the brain, head, and neck has been developed and made available. The atlas has been created from multispectral 3 and 7 Tesla and high-resolution CT in vivo scans. It is fully 3D, scalable, interactive, and highly detailed with about 3,000 labeled components. This atlas forms a foundation for the development of a multi-level molecular, cellular, anatomical, physiological, and behavioral brain atlas platform.

Graphical neuroimaging informatics: application to Alzheimer's disease

The Informatics Visualization for Neuroimaging (INVIZIAN) framework allows one to graphically display image and meta-data information from sizeable collections of neuroimaging data as a whole using a dynamic and compelling user interface. Users can fluidly interact with an entire collection of cortical surfaces using only their mouse. In addition, users can cluster and group brains according in multiple ways for subsequent comparison using graphical data mining tools. In this article, we illustrate the utility of INVIZIAN for simultaneous exploration and mining a large collection of extracted cortical surface data arising in clinical neuroimaging studies of patients with Alzheimer's Disease, mild cognitive impairment, as well as healthy control subjects. Alzheimer's Disease is particularly interesting due to the wide-spread effects on cortical architecture and alterations of volume in specific brain areas associated with memory. We demonstrate INVIZIAN's ability to render multiple brain surfaces from multiple diagnostic groups of subjects, showcase the interactivity of the system, and showcase how INVIZIAN can be employed to generate hypotheses about the collection of data which would be suitable for direct access to the underlying raw data and subsequent formal statistical analysis. Specifically, we use INVIZIAN show how cortical thickness and hippocampal volume differences between group are evident even in the absence of more formal hypothesis testing. In the context of neurological diseases linked to brain aging such as AD, INVIZIAN provides a unique means for considering the entirety of whole brain datasets, look for interesting relationships among them, and thereby derive new ideas for further research and study.

MRI-based multiscale model for electromagnetic analysis in the human head with implanted DBS

Deep brain stimulation (DBS) is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI) to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF) energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes. A multiscale model (MS) was created by an atlas-based segmentation using a 1 mm³ head model (mRes) refined in the basal ganglia by a 200 μm² ex-vivo dataset. Four DBS electrodes targeting the left globus pallidus internus were modeled. Electromagnetic simulations at 128 MHz showed that the peak of the electric field of the MS doubled (18.7 kV/m versus 9.33 kV/m) and shifted 6.4 mm compared to the mRes model. Additionally, the MS had a sixfold increase over the mRes model in peak-specific absorption rate (SAR of 43.9 kW/kg versus 7 kW/kg). The results suggest that submillimetric resolution and improved anatomical detail in the model may increase the accuracy of computed electric field and local SAR around the tip of the implant.

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.

A Simple and Fast Method of 3D Registration and Statistical Landmark Localization for Sparse Multi-Modal/Time-Series Neuroimages Based on Cortex Ellipse Fitting

Existing methods of neuroimage registration typically require high quality scans and are time-consuming. We propose a simple and fast method which allows intra-patient multi-modal and time-series neuroimage registration as well as landmark identification (including commissures and superior/inferior brain landmarks) for sparse data. The method is based on elliptical approximation of the brain cortical surface in the vicinity of the midsagittal plane (MSP). Scan registration is performed by a 3D affine transformation based on parameters of the cortex elliptical fit and by aligning the MSPs. The landmarks are computed using a statistical localization method based on analysis of 53 structural scans without detectable pathology. The method is illustrated for multi-modal registration, analysis of hemorrhagic stroke time series, and ischemic stroke follow ups, as well as for localization of hardly visible or not discernible landmarks in sparse neuroimages. The method also enables a statistical localization of landmarks in sparse morphological/non-morphological images, where landmark points may be invisible.

VIRTUAL REALITY IN BRAIN INTERVENTION

After introduction to medical imaging, virtual reality (VR), and VR surgical simulation, the paper overviews our long-term efforts in human body modeling and development of VR-based applications for brain intervention. It describes construction, features, and applications of anatomical, functional, and vascular brain atlases. It also presents VR-based applications developed for stereotactic and functional neurosurgery (NeuroPlanner, NeuroBase, and BrainBench), interventional neuroradiology (NeuroCath and tele-NeuroCath), and tumor stereotaxis (VIVIAN). Finally, a vision of the future surgery (the DOTELL environment) is presented.

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.

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.

Towards construction of an ideal stereotactic brain atlas

BACKGROUND

The role of the brain atlas is changing in many aspects with the advancements in stereotactic and functional neurosurgery. Therefore, there is a critical need to construct a new atlas. This paper addresses the definition and construction of an atlas, ideal (in our opinion) for stereotactic and functional neurosurgery. The essence of the new atlas is not only its population-based structural and functional content, but also its continuous "self-updatability" with the new clinical results obtained.

METHOD

The ideal atlas defined here contains four major components: brain models, knowledge database, tools, and clinical results. Towards its creation, a multi-atlas is proposed. The construction of the initial version of the multi-atlas is detailed with the probabilistic functional atlas (PFA), interpolated Talairach-Tournoux atlas, and enhanced Schaltenbrand-Wahren atlas. These atlases are put in a spatial register by matching their AC-PC distances and heights of the thalamus; the Schaltenbrand coronal and sagittal microseries are scaled laterally to match the target structure centroids with the locations of the best targets of the PFA.

FINDINGS

Construction of an initial version of the ideal stereotactic atlas is feasible at present from the available resources. To achieve that, our three atlases (PFA, Talairach and Schaltenbrand) are enhanced and combined together. A single lateral scaling factor per target structure is feasible to co-register the Schaltenbrand atlas with PFA in four situations (compensated against the third ventricle, non-compensated, bilateral, and non-bilateral). The STN has to be stretched by 18% more than the VIM on the Schaltenbrand coronal microseries, and the VIM has to be compressed by 13% less than the STN on the Schaltenbrand sagittal microseries.

CONCLUSION

The new multi-atlas can potentially be more useful than the currently employed atlases and will facilitate further development of the ideal atlas for stereotactic and functional neurosurgery.

Injury and recovery in the developing brain: evidence from functional MRI studies of prematurely born children

Functional MRI (fMRI) might provide important insights into emerging data that suggest that recovery from injury can occur in the brains of children born prematurely. Strategies employing auditory stimulation demonstrate blood-oxygen-level-dependent (BOLD) activation in preterm infants as young as 33 weeks' gestational age, and reliable BOLD signal in response to visual stimulation occurs at term-equivalent age. Strategies based on fMRI are particularly suited to the study of language and memory, and emerging data are likely to provide insights into perplexing reports that have demonstrated improving cognitive scores but persistent volumetric and microstructural changes in frontotemporal language systems in the prematurely born. Even when sex, gestational age and early medical and environmental interventions are taken into account, fMRI data from several investigators suggest the engagement of alternative neural networks for language and memory in the developing preterm brain.

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.

Virtual reality in brain intervention: models and applications

Human body models are key components of VR surgical simulators. We overview here our efforts and share experience in constructing cerebral models and using them in VR-based applications for brain intervention. We have constructed four groups of brain atlases: anatomical, functional, vascular, and physically-based. The atlas-assisted VR systems discussed here are applied to stereotactic and functional neurosurgery, interventional neuroradiology, and brain stereotaxy. We also briefly feature our concept of future surgery.

A hybrid approach to shape-based interpolation of stereotactic atlases of the human brain

Stereotactic human brain atlases, either in print or electronic form, are useful not only in functional neurosurgery, but also in neuroradiology, human brain mapping, and neuroscience education. The existing atlases represent structures on 2D plates taken at variable, often large intervals, which limit their applications. To overcome this problem, we propose a hybrid interpolation approach to build high-resolution brain atlases from the existing ones. In this approach, all section regions of each object are grouped into two types of components: simple and complex. A NURBS-based method is designed for interpolation of the simple components, and a distance map-based method for the complex components. Once all individual objects in the atlas are interpolated, the results are combined hierarchically in a bottom-up manner to produce the interpolation of the entire atlas. In the procedure, different knowledge-based and heuristic strategies are used to preserve various topological relationships. The proposed approach has been validated quantitatively and used for interpolation of two stereotactic brain atlases: the Talairach-Tournoux atlas and Schaltenbrand-Wahren atlas. The interpolations produced are of high resolution and feature high accuracy, 3D consistency, smooth surface, and preserved topology. They potentially open new applications for electronic stereotactic brain atlases, such as atlas reformatting, accurate 3D display, and 3D nonlinear warping against normal and pathological scans. The proposed approach is also potentially useful in other applications, which require interpolation and 3D modeling from sparse and/or variable intersection interval data. An example of 3D modeling of an infarct from MR diffusion images is presented.

Morphologic relationship among the corpus callosum, fornix, anterior commissure, and posterior commissure MRI-based variability study

RATIONALE AND OBJECTIVES

This study explores morphological relationships and structural variability of the corpus callosum (CC), fornix (Fo), anterior (AC), and posterior commissures (PC).

MATERIALS AND METHODS

These structures are extracted automatically on the midsagittal plane. The CC and Fo are modeled using best-fit ellipses. The parameters characterizing these structures and relationships among them are points, distances, angles, and eccentricities. The minimum, maximum and mean values, standard deviations, and coefficients of variation for all parameters are calculated for 62 diversified MRI datasets. Subsequently, the regression analysis and parameter distribution study are performed.

RESULTS

The parameters have at least 10% variations. The major axis of CC and eccentricities of CC and Fo vary much less than the other parameters The major axis of CC is approximately parallel to the AC-PC line. The mean eccentricity of each of CC and Fo is greater than 0.95. The most significant correlation (P < .05) is observed between various angles and the angle between the major axes of CC and Fo. The correlation is also significant between other angles and distances. The Weibull distribution characterizes the major axis of CC, and distance between the AC and the most superior point of CC. Distribution of angle between the major axes of CC and Fo is log (logistic), and normal for the AC-PC distance.

CONCLUSIONS

The AC-PC distance, used prevalently for brain normalization, is not correlated with any parameters except with the distance between the AC and the most superior point on the body of the CC with P < .05.

Dorsoventral Extension of the Talairach Transformation and Its Automatic Calculation for Magnetic Resonance Neuroimages

The Talairach transformation (TT), the most prevalent method for brain normalization and atlas-to-data warping, is conceptually simple, fast and can be automated. Two problems with the TT in the clinical setting that are addressed in this article are reduced accuracy at the orbitofrontal cortex and upper corpus callosum (CC) and unsuitability for functional neurosurgery because of incomplete scanning. To increase dorsoventral accuracy, we introduce 2 additional landmarks: the top of the CC (SM) and the most ventral point of the orbitofrontal cortex on the midsagittal slab (IM). A method for their automatic calculation is proposed and validated against 55 diversified magnetic resonance (MR) imaging cases. The SM and IM landmarks are identified accurately and robustly in an automatic way. The average error of SM localization is 0.69 mm, and 91% of all cases have an error not greater than 1 mm. The average error of IM localization is 0.98 mm, approximately three quarters of cases have an error not greater than 1 mm, and 95% of all cases have an error not larger than 2 mm. The SM is correlated (R(2) = 0.72) with the most superior cortical landmark, whereas the IM is only loosely correlated (R(2) = 0.22) with the most inferior cortical landmark. On average, the original TT overlays the atlas axial plate at -24 on the orbitofrontal cortex as opposed to the correct plate at -28. Therefore, 1-dimensional ventral scaling in the original TT is insufficient to cope with variability in the orbitofrontal cortex. The key advantages of our approach are the preserved conceptual simplicity of the TT, fully automatic identification of the new landmarks, improved accuracy of the atlas-to-data match without compromising performance, and enabled TT use in functional neurosurgery when a dorsal part of the brain is not available in the scan.

Fast, accurate, and automatic extraction of the modified Talairach cortical landmarks from magnetic resonance images

The Talairach transformation is the most prevalent way to normalize brains and is hindered by, among others things, a lack of automatic determination of cortical landmarks. An algorithm to locate the modified Talairach cortical landmarks in three steps is proposed: determination of the three planes containing the landmarks; segmentation of the planes based on range-constrained thresholding and morphologic operations; and local refinement of the segmentation to locate the landmarks. The algorithm has been validated against 62 T(1)-weighted and SPGR MR diversified data sets. For each data set, it takes less than 2 s on a Pentium 4 to extract all six landmarks. The average landmark location errors are below 0.9 mm. The algorithm is robust due to incorporation of anatomic knowledge. A low computational cost results from processing of three 2D images and employing only simple operations like thresholding, basic morphologic operations, and distance transform.

Informatics in Radiology (infoRAD)

Of the existing atlases of the brain anatomy and cerebrovasculature, none integrates the anatomy and vasculature by providing for direct manipulation of three-dimensional (3D) cerebral models. An atlas-based application was developed in four steps: (a) construction of 3D anatomic models, (b) construction of 3D vascular models, (c) interactive spatial coregistration of the anatomic and vascular models, and (d) development of functionality and a user interface for the application. Three-dimensional anatomic models were imported from an electronic brain atlas database derived from classic print atlases. A novel vascular modeling technique was developed and applied to create a vascular atlas from magnetic resonance angiographic data. The use of 3D polygonal models allows smooth navigation (rotation, zooming, panning) and interactive labeling of anatomic structures and vascular segments. This application enables the user to examine 3D anatomic structures and 3D cerebral vasculature and to gain a better understanding of the relationships between the two. The combined anatomic-vascular atlas is a user-friendly neuroeducational tool that is useful for medical students and neuroscience researchers as well as for educators in preparing teaching materials.

Precentral gyrus discrepancy in electronic versions of the Talairach atlas

Electronic versions of the atlas of Talairach and Tournoux, including the Talairach Daemon and the official versions published by Thieme, contain a discrepant region of the precentral gyrus on axial slice +35 mm that extends far forward into the frontal lobe. This area is anatomically incorrect and internally inconsistent within the digital atlas software applications using their multiplanar cross-referencing tools. By cross-referencing the axial, sagittal, and coronal plates from the original printed atlas, we demonstrate that the discrepant area should be labeled middle frontal gyrus. The mislabeled portion encompasses a 3 x 1.5-cm region in the axial plane and has significant implications for sensorimotor studies that rely on the digital atlases for anatomic labeling.

The Cerefy Neuroradiology Atlas: a Talairach–Tournoux atlas-based tool for analysis of neuroimages available over the internet

The article introduces an atlas-assisted method and a tool called the Cerefy Neuroradiology Atlas (CNA), available over the Internet for neuroradiology and human brain mapping. The CNA contains an enhanced, extended, and fully segmented and labeled electronic version of the Talairach-Tournoux brain atlas, including parcelated gyri and Brodmann's areas. To our best knowledge, this is the first online, publicly available application with the Talairach-Tournoux atlas. The process of atlas-assisted neuroimage analysis is done in five steps: image data loading, Talairach landmark setting, atlas normalization, image data exploration and analysis, and result saving. Neuroimage analysis is supported by a near-real-time, atlas-to-data warping based on the Talairach transformation. The CNA runs on multiple platforms; is able to process simultaneously multiple anatomical and functional data sets; and provides functions for a rapid atlas-to-data registration, interactive structure labeling and annotating, and mensuration. It is also empowered with several unique features, including interactive atlas warping facilitating fine tuning of atlas-to-data fit, navigation on the triplanar formed by the image data and the atlas, multiple-images-in-one display with interactive atlas-anatomy-function blending, multiple label display, and saving of labeled and annotated image data. The CNA is useful for fast atlas-assisted analysis of neuroimage data sets. It increases accuracy and reduces time in localization analysis of activation regions; facilitates to communicate the information on the interpreted scans from the neuroradiologist to other clinicians and medical students; increases the neuroradiologist's confidence in terms of anatomy and spatial relationships; and serves as a user-friendly, public domain tool for neuroeducation. At present, more than 700 users from five continents have subscribed to the CNA.

A locus-driven mechanism for rapid and automated atlas-assisted analysis of functional images by using the Brain Atlas for Functional Imaging

Object

Functional imaging is an established neurosurgical modality for studying the brain in health and disease. Identifying numerous activation loci on many functional images and reading their underlying cortical and subcortical anatomy, coordinates, and anatomical and functional values is a tedious, time-consuming, and error-prone task. In this study the authors propose a novel approach to this problem by using an electronic brain atlas in conjunction with a locus-driven mechanism.

Methods

The Brain Atlas for Functional Imaging containing an enhanced and extended electronic version of the Talairach–Tournoux brain atlas was used for analysis. It enables loading of anatomical and functional data, correlation of these data, identification of activation loci, and their labeling with Brodmann areas, gyri, and subcortical structures by means of the atlas. The Talairach proportional grid system transformation is used to register the anatomical and functional data with the atlas. The availability of numerous tools supports this process.

A locus-driven mechanism for analysis of activation loci is implemented. Locus placement within the activation region is supported by thresholding, and its location can be further edited in three dimensions on any orthogonal plane. Once all loci are identified and edited, their labels, coordinates, and anatomical/functional values are read automatically and saved in an external file. This mechanism enables the analysis to be performed in an automated, rapid, explicit, three-dimensionally consistent, and user-friendly way.

Conclusions

The electronic brain atlas with locus-driven mechanism is a useful tool for localization analysis of functional images.