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Purpose : Integrating neuroradiology with neuroanatomy is essential in medical neuroeducation and neuroimage interpretation. To bridge 2D neuroradiology and 3D neuroanatomy, spatially correlated pairs of labeled images were employed, planar radiologic, and planar-surface combined. Research design : The method employs a 3D fully parcellated and labeled brain atlas extended to the head and neck with about 3000 3D components to create planar radiologic and surface neuroanatomic images. The atlas handles reformatted radiologic images as 3D objects using texture mapping which provides consistency with polygonal 3D neuroanatomic structures. This ensures a precise spatial correspondence of dual 2D-2D/3D images for any composed 3D scene reformatted in arbitrary orientation. The sequences of labeled dual images were created spanning a structure/system of interest in multiple orientations. To facilitate image searching, the image name encodes its content, orientation, and stereotactic location. Results : Labeled dual 2D-2D/3D neuroimage sequences in multiple orientations were created for the cerebrum, brainstem, deep nuclei, cerebral ventricles, intracranial arteries, dural sinuses, extracranial arteries, extracranial veins, trigeminal nerve, head muscles, glands, bones of cranium, and visual system. They all were hierarchically organized as a planar-surface gallery with 42 folders and 502 neuroimages. This gallery was integrated with a public NOW in BRAIN repository at www.nowinbrain.org with more than 7700 neuroimages. Conclusions : Owing to its advantages, simplicity, and free availability, this resource is useful for medical students, residents, educators, and clinicians to study the brain, head, and neck as well as to prepare presentations and teaching materials. The approach might potentially enhance image interpretation by integrating brain atlases with radiologic workstations.

Despite the tremendous development of various brain-related resources, a large, systematic, comprehensive, extendable, and beautiful repository of 3D reconstructed images of a living human brain expanded to the head and neck is not yet available. I have created such a novel repository and populated it with images derived from a 3D atlas constructed from 3/7 Tesla MRI and high-resolution CT scans. This web-based repository contains 6 galleries hierarchically organized in 444 albums and sub-albums with 5,156 images. Its original features include a systematic design in terms of multiple standard views, modes of presentation, and spatially co-registered image sequences; multi-tissue class galleries constructed from 26 primary tissue classes and 199 sub-classes; and a unique image naming syntax enabling image searching based solely on the image name. Anatomic structures are displayed in 6 standard views (anterior, left, posterior, right, superior, inferior), all views having the same brain size, and optionally with additional arbitrary views. In each view, the images are shown as sequences in three standard modes of presentation, non-parcellated unlabeled, parcellated unlabeled, and parcellated labeled. There are two types of spatially co-registered image sequences (imitating image layers and enabling animation creation), the appearance image sequence (for standard views) and the context image sequence (with a growing number of tissue classes). Color-coded neuroanatomic content makes the brain beautiful and facilitates its learning and understanding. This unique repository is freely available and easily accessible online at www.nowinbrain.org for a wide spectrum of users in medicine and beyond. Its future extensions are in progress., (© 2021. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools., (© 2021. The Author(s).)

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.

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., (© The Author(s) 2016.)

Background: Although the adult human skull is a complex and multifunctional structure, its 3D, complete, realistic, and stereotactic atlas has not yet been created. This work addresses the construction of a 3D interactive atlas of the adult human skull spatially correlated with the brain, cranial nerves, and intracranial vasculature., New Method: The process of atlas construction included computed tomography (CT) high-resolution scan acquisition, skull extraction, skull parcellation, 3D disarticulated bone surface modeling, 3D model simplification, brain-skull registration, 3D surface editing, 3D surface naming and color-coding, integration of the CT-derived 3D bony models with the existing brain atlas, and validation., Results: The virtual skull model created is complete with all 29 bones, including the auditory ossicles (being among the smallest bones). It contains all typical bony features and landmarks., Comparison With Existing Method(s): The created skull model is superior to the existing skull models in terms of completeness, realism, and integration with the brain along with blood vessels and cranial nerves., Conclusions: This skull atlas is valuable for medical students and residents to easily get familiarized with the skull and surrounding anatomy with a few clicks. The atlas is also useful for educators to prepare teaching materials. It may potentially serve as a reference aid in the reading and operating rooms., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background and Purpose: Knowledge of outcome prediction is important in stroke management. We propose a lesion size and location-driven method for stroke outcome prediction using a Population-based Stroke Atlas (PSA) linking neurological parameters with neuroimaging in population. The PSA aggregates data from previously treated patients and applies them to currently treated patients. The PSA parameter distribution in the infarct region of a treated patient enables prediction. We introduce a method for PSA calculation, quantify its performance, and use it to illustrate ischemic stroke outcome prediction of modified Rankin Scale (mRS) and Barthel Index (BI)., Methods: The preliminary PSA was constructed from 128 ischemic stroke cases calculated for 8 variants (various data aggregation schemes) and 3 case selection variables (infarct volume, NIHSS at admission, and NIHSS at day 7), each in 4 ranges. Outcome prediction for 9 parameters (mRS at 7th, and mRS and BI at 30th, 90th, 180th, 360th day) was studied using a leave-one-out approach, requiring 589,824 PSA maps to be analyzed., Results: Outcomes predicted for different PSA variants are statistically equivalent, so the simplest and most efficient variant aiming at parameter averaging is employed. This variant allows the PSA to be pre-calculated before prediction. The PSA constrained by infarct volume and NIHSS reduces the average prediction error (absolute difference between the predicted and actual values) by a fraction of 0.796; the use of 3 patient-specific variables further lowers it by 0.538. The PSA-based prediction error for mild and severe outcomes (mRS  =  [2]-[5]) is (0.5-0.7). Prediction takes about 8 seconds., Conclusions: PSA-based prediction of individual and group mRS and BI scores over time is feasible, fast and simple, but its clinical usefulness requires further studies. The case selection operation improves PSA predictability. A multiplicity of PSAs can be computed independently for different datasets at various centers and easily merged, which enables building powerful PSAs over the community.

Understanding brain pathology along with the underlying neuroanatomy and the resulting neurological deficits is of vital importance in medical education and clinical practice. To facilitate and expedite this understanding, we created a three-dimensional (3D) interactive atlas of neurological disorders providing the correspondence between a brain lesion and the resulting disorder(s). The atlas contains a 3D highly parcellated atlas of normal neuroanatomy along with a brain pathology database. Normal neuroanatomy is divided into about 2,300 components, including the cerebrum, cerebellum, brainstem, spinal cord, arteries, veins, dural sinuses, tracts, cranial nerves (CN), white matter, deep gray nuclei, ventricles, visual system, muscles, glands and cervical vertebrae (C1-C5). The brain pathology database contains 144 focal and distributed synthesized lesions (70 vascular, 36 CN-related, and 38 regional anatomy-related), each lesion labeled with the resulting disorder and associated signs, symptoms, and/or syndromes compiled from materials reported in the literature. The initial view of each lesion was preset in terms of its location and size, surrounding surface and sectional (magnetic resonance) neuroanatomy, and labeling of lesion and neuroanatomy. In addition, a glossary of neurological disorders was compiled and for each disorder materials from textbooks were included to provide neurological description. This atlas of neurological disorders is potentially useful to a wide variety of users ranging from medical students, residents and nurses to general practitioners, neuroanatomists, neuroradiologists and neurologists, as it contains both normal (surface and sectional) brain anatomy and pathology correlated with neurological disorders presented in a visual and interactive way.

Objective: Human cerebrovasculature has not been quantified in volume, length, and vascular-brain relationships. We investigated this using imaging., Methods: From 0.5-mm 7T and 320-row CT acquisitions, 6 arterial and 4 venous systems were reconstructed, measured, and analyzed., Results: The ratio of the volume of arterial to venous system is approximately 1:3. The ratio of the volume of dural sinuses to vasculature is 1:2. The ratio of the posterior (PCA) to anterior (ACA) to middle cerebral artery (MCA) is 1:2:4 in volume and length. Ratios of left to right vessels are 1:1 for arteries and veins. Ratios of branching frequency for the ACA, MCA, and PCA are 1:1:1. The branching frequency ratio for superficial to deep veins is 1:2. The MCA occupies 1/2 of arterial length and 1/4 of vascular length. The ratio of the length of superficial to deep veins is 1:1 and each is equal to 1/4 of the vascular length. The ratio of cerebrovasculature to brain volume is 2.5%., Conclusions: Despite its enormous complexity, cerebrovasculature is characterized by 4 approximate proportions, 1:1, 1:2, 1:3, 1:4, and their combinations, 1:1:1 and 1:2:4.

As the human brain is the most complex living organ, constructing its detailed model with exploration capabilities in a form of an atlas is a challenge. Our overall goal is to construct an advanced, detailed, parcellated, labeled, accurate, interactive, three-dimensional (3D), and scalable whole human brain atlas of structure, vasculature, tracts and systems. The objectives of this work are three-fold; to present: (1) method of atlas design and development including design principles, accuracy requirements, atlas content, architecture, functionality, user interface, and customized tools; (2) creation of an atlas of structure and systems including its modeling method and validation; and (3) integration of this atlas with the cerebrovasculature and tracts created earlier. The atlas is created from multiple in vivo 3/7 T scans. Its design based on "pyramidal principle" enables scalability while preserving design principles and exploits interaction paradigm "from blocks to brain". The atlas contains (1) navigator with modules for system/object/object state management, interaction, user interfacing, and rendering; and (2) brain model with cerebrum, cerebellum, brainstem, spinal cord, white matter, deep structures, systems, ventricles, arteries, veins, sinuses, and tracts. The brain model is parcellated, labeled, consistent, realistic, of high resolution, polygonal/volumetric, dissectible, extendable, and deformable. It has over 1700 3D components. The atlas has sub-voxel accuracy of 0.1mm and the smallest vessels of 80 μm. Brain exploration includes dynamic scene composition, manipulation-independent 3D labeling, interaction combined with animation, meta-labeling, and quantification. This atlas is useful in education, research, and clinical applications. It can potentially be foundation for a multi-level molecular-cellular-anatomical-physiological-behavioral platform., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Knowledge of the cranial nerves and their nuclei is critical in clinical practice, medical research and education. However to our best knowledge, a comprehensive source capturing full three-dimensional (3D) relationships of the cranial nerves along with surrounding neuroanatomy is not yet available. This work addresses the construction and validation of an atlas of the cranial nerves with their nuclei, correlated with surface neuroanatomy, vasculature, and magnetic resonance imaging. The atlas is interactive, stereotactic, 3D, detailed, fully parcellated, completely labeled, consistent in 3D, electronically dissectible, and scalable. A 3D geometrical model of the 12 pairs of cranial nerves with nuclei was created from an in vivo magnetic resonance scan exploiting in-house developed tools and methods, including tubular and iso-surface modeling, interactive editing, and mesh compression. This virtual model contains 439 objects with 121 different names, labeled based on Terminologia Anatomica. The model was integrated with a 3D atlas of structure, vasculature and tracts developed earlier, and correlated with sectional magnetic resonance anatomy. The whole model or its components can be interactively rotated, zoomed, panned, and add or removed with a simple few clicks. The studied material can be adaptively selected in an in-depth manner by using controls available in the user interface. This atlas is potentially useful for anatomy browsing, user self-testing, automatic student assessment, preparing materials, and localization in clinical neurology., (Copyright © 2012 Elsevier B.V. All rights reserved.)

We present a human brain atlas of white matter (WM) tracts containing 40 major tracts, which is three-dimensional (3D), segmented, labeled, interactive, stereotactic and correlated to structure and vasculature. We consider: (1) WM accuracy by correlating WM tracts to underlying neuroanatomy and quantifying them; (2) balance between realism and completeness by processing a sequence of track volumes generated for various parameters with the increasing track number to enable a tract "shape convergence". MPRAGE and DTI in 64 directions of the same subject were acquired on 3 Tesla. The method has three steps: DTI-MPRAGE registration, 3D tract generation from DTI, to WM reconstruction from MPRAGE to parcellation into 17 components. 82 track volumes were generated for a wide spectrum of parameter values: Fractional Anisotropy threshold in [0.0125, 0.55] and trajectory angle lower than 45°, 60°, 65°, 70°, 75°, 80°, 85°, 90°. For each tract, a sequence of track volumes was processed to create/edit contours delineating this tract to achieve its shape convergence. The parcellated tracts were grouped into commissures, associations, projections and posterior fossa tracts, and labeled following Terminologia Anatomica. To facilitate that, a dedicated tract editor is developed which processes multiple track volumes, handles tracts in three representations (tracks, contours, envelopes); provides editing/visualization simultaneously on axial, coronal, sagittal planes; enables tract labeling and coloring; and provides numerous tools (track counting, smoothing and length thresholding; representation conversion and saving; structural atlas support). A stereotactic tract atlas along with parcellated WM was developed to explore in real-time any individual tract or their groups along with surrounding neuroanatomy.

Purpose: Although the brain has been extensively studied, relationships of gray (GM) to white (WM) matters in individual sections as typically acquired and read radiologically have not yet been examined. A novel GM/WM-based approach with a compact whole brain representation is introduced and applied to study the brain and perform neuroimage processing., Methods: The gray to white matter ratio GWR defined as GM/(GM+WM) was calculated for 3T T1-weighted axial, coronal, and sagittal sections of 75 normal subjects. The mean (normative) GWR curves were employed to describe the normal brain and quantify aging and to illustrate pathology detection and characterization., Results: The mean GWR curves characterize the normal brain by only six, neuroanatomy-related numbers. The regions with a significant GWR decline with age surround the ventricular system. The GWR decline rate in males is higher (-0.17%/year) than females (-0.14%/year); moreover, males show a significantly higher decline in middle to elder group. The GWR decline from young (≤25 years) to middle (26-40 years) age group (males/females -0.31%/-0.34%/year) is significantly higher than that from middle to elder (>40 years) group (males/females -0.13/-0.07%/year)., Conclusion: The GWR-based analysis is useful to characterize normal brain, determine significant regions of interest, and quantify healthy aging. It has potential applications in brain compression, comparison, morphometry, normalization, and detecting and quantifying pathologies, which open new avenues in computer-assisted neuroradiology from screening to large brain databases searching.

Although there exist some reference and stereotactic anatomical human brain atlases, there is no equivalent cerebrovascular atlas. A 3D reference atlas of the human cerebrovasculature that is interactive, stereotactic, very detailed, completely parcellated, fully labeled, extendable, dissectible, deformable, and user friendly, is needed in education, training, research, and clinical applications. We constructed a sophisticated and very detailed cerebrovascular atlas with 1325 vascular segments, the smallest of 80 μm in diameter. The atlas was created from multiple 3 and 7T scans by employing tools and methods developed in our lab. In contrast to a sort of "sampled" vascular geometry cited in the literature, we provide information about the "continuous" geometry of the vascular model measurable in 3D at every location and along any vascular segment for both hemispheres. This cerebrovascular atlas was validated taking into account domain knowledge from various fields including angiography, neurosurgery, neuroradiology, (clinical) neuroanatomy, and terminology. Validation was considered as a confirmation of the created vascular model to a typical (conventional) cerebrovascular system with nearly average dimensions. It was done in terms of vessel subdivision, origin, course, surrounding anatomy, patterns, length, diameter, and branches. This validation demonstrates that the constructed cerebrovascular model generally represents a normal, typical cerebrovasculature with its dimensions close to average. This 7T atlas along with the vascular model is a rich source of knowledge about the human cerebrovasculature. The atlas is applicable in education, research, and clinical applications; it has already been applied in deep brain stimulation., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Rationale and Objectives: Although segmentation algorithms for cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM) on unenhanced computed tomographic (CT) images exist, there is no complete research in this area. To take into account poor image contrast and intensity variability on CT scans, the aim of this study was to derive and validate a novel, automatic, adaptive, and robust algorithm., Materials and Methods: Unenhanced CT scans of normal subjects from two different centers were used. The algorithm developed uses adaptive thresholding, connectivity, and domain knowledge and is based on heuristics on the shape of CT histogram. The slope of the intensity histogram corresponding to the three-dimensional largest connected region in a variable CSF intensity range is tracked to determine the critical intensity, which serves as an initial classifier of CSF-WM. Thresholds of CSF, WM, and GM are then optimally derived to minimize classification overlap. Multiple, null, and erroneous classifications are resolved by applying domain knowledge., Results: The ground-truth regions with the minimal partial volume effect were used to evaluate segmentation results using the statistical markers. Average sensitivity, Dice index, and specificity, respectively, for the first center were 95.7%, 97.0%, and 98.6% for CSF; 96.1%, 97.3%, and 98.8% for WM; and 95.2%, 94.3%, and 92.8% for GM. The results were consistent for the second data center., Conclusions: The algorithm automatically identifies CSF, WM, and GM on unenhanced CT images with high accuracy, is robust to data from different scanners, does not require any parameter setting, and takes about 5 minutes in MATLAB to process a 512 × 512 × 30 scan. The algorithm has potential use in research and clinical applications., (Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.)

Objectives: Abnormalities of brain white matter have been noted in structural magnetic resonance imaging and diffusion tensor imaging (DTI) studies of bipolar disorder, but there are fewer investigations specifically examining white matter integrity early in the course of illness. In this study, we employed DTI to elucidate white matter changes in adult patients with remitted first-episode mania and hypothesized that first-episode mania was associated with decreased fractional anisotropy in cortical (frontal) and subcortical (thalamus, striatum) white matter as well as white matter tracts (cingulum, corpus callosum)., Methods: Diffusion tensor images were acquired from 16 patients with remitted first-episode mania and 16 healthy controls matched for age, gender, handedness, and years of education. Fractional anisotropy and radial and axial diffusivities were analyzed using Tract-Based Spatial Statistics., Results: Patients had lower fractional anisotropy and higher radial diffusivity in the left anterior frontal white matter, right posterior thalamic radiation, left cingulum, and bilateral sagittal striatum. In addition, increased radial diffusivity was found in the left corpus callosum., Conclusion: Our findings highlighted that white matter abnormalities were present by the time of remission of first-episode mania. The widespread occurrence of these white matter abnormalities both in first-episode mania and chronic bipolar disorder suggested that disruption of white matter cortical-subcortical networks as well as projection, associative, and commissural tracts is a hallmark of the illness.

Objective: The identification of the interhemispheric fissure (IF) is important in clinical applications for brain landmark identification, registration, symmetry assessment, and pathology detection. The IF is usually approximated by the midsagittal plane (MSP) separating the brain into two hemispheres. We present a fast accurate, automatic, and robust algorithm for finding the MSP for CT scans acquired in emergency room (ER) with a large slice thickness, high partial volume effect, and substantial head tilt., Materials and Methods: An earlier algorithm for MSP identification from MRI using the Kullback-Leibler's measure was extended for CT by estimating patient's head orientation using model fitting, image processing, and atlas-based techniques. The new algorithm was validated on 208 clinical scans acquired mainly in the ER with slice thickness ranging from 1.5 to 6 mm and severe head tilt., Results: The algorithm worked robustly for all 208 cases. An angular discrepancy (degrees) and maximum distance (mm) between the calculated MSP and ground truth have the mean value (SD) 0.0258 degrees (0.9541 degrees) and 0.1472 (0.7373) mm, respectively. In average, the algorithm takes 10 s to process of a typical CT case., Conclusion: The proposed algorithm is robust to head rotation, and correctly identifies the MSP for a standard clinical CT scan with a large slice thickness. It has been applied in our several CT stroke CAD systems.

Preparation of tests and student's assessment by the instructor are time consuming. We address these two tasks in neuroanatomy education by employing a digital media application with a three-dimensional (3D), interactive, fully segmented, and labeled brain atlas. The anatomical and vascular models in the atlas are linked to Terminologia Anatomica. Because the cerebral models are fully segmented and labeled, our approach enables automatic and random atlas-derived generation of questions to test location and naming of cerebral structures. This is done in four steps: test individualization by the instructor, test taking by the students at their convenience, automatic student assessment by the application, and communication of the individual assessment to the instructor. A computer-based application with an interactive 3D atlas and a preliminary mobile-based application were developed to realize this approach. The application works in two test modes: instructor and student. In the instructor mode, the instructor customizes the test by setting the scope of testing and student performance criteria, which takes a few seconds. In the student mode, the student is tested and automatically assessed. Self-testing is also feasible at any time and pace. Our approach is automatic both with respect to test generation and student assessment. It is also objective, rapid, and customizable. We believe that this approach is novel from computer-based, mobile-based, and atlas-assisted standpoints.

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.

Introduction: Previous studies examining brain effects of duration of illness in schizophrenia have focused on either cortical or subcortical structures. Hence this study sought to elucidate the regional grey matter changes (both cortical and subcortical) and neurocognitive correlates with increased duration of illness in a large sample of patients with schizophrenia using voxel-based morphometry., Materials and Methods: Ninety patients (72 males and 18 females) with DSM-IV diagnosis of schizophrenia were recruited and assessed using magnetic resonance imaging and a battery of neuropsychological tests., Results: A longer duration of illness was associated with smaller grey matter volumes in the left superior frontal gyrus, bilateral putamen, right superior temporal gyrus, right superior occipital gyrus as well as the right thalamus. No region showed increased grey matter volume above threshold with longer duration of illness. Longer duration of illness was correlated with poorer attention., Conclusions: The grey matter reductions in different brain regions highlighted that a distributed network of cortical and subcortical regions was associated with duration of illness. This is consistent with neural models that implicate involvement of thalamo-cortical circuitry as the disruption in these neural pathways can result in specific deficits such as poorer attention. The results have implications for the understanding of brain changes in schizophrenia, and with further studies, may guide better tailored and targeted clinical management in terms of reducing the impact of duration of illness on neural substrates in schizophrenia in the future.

The human cerebrovasculature is extremely complicated and its three dimensional (3D) highly parcellated models, though necessary, are unavailable. We constructed a digital cerebrovascular model from a high resolution, 3T 3D time-of-flight magnetic resonance angiography scan. This model contains the arterial and venous systems and is 3D, geometric, highly parcellated, fully segmented, and completely labeled with name, diameter, and variants. Our approach replaces the tedious and time consuming process of checking and correcting automatic segmentation results done at 2D image level with an aggregate and faster process at 3D model level. The creation of the vascular model required vessel pre-segmentation, centerline extraction, vascular segments connection, centerline smoothing, vessel surface construction, vessel grouping, tracking, editing, labeling, setting diameter, and checking correctness and completeness. For comparison, the same scan was segmented automatically with 59.8% sensitivity and only 16.5% of vessels smaller than 1 pixel size were extracted. To check and correct this automatic segmentation requires 8 weeks. Conversely, the speedup of our approach (the number of 2D segmented areas/the number of 3D vascular segments) is 34. This cerebrovascular model can serve as a reference framework in clinical, research, and educational applications. The wealth of information aggregated with its quantification capabilities can augment or replace numerous textbook chapters. Five applications of the vascular model were described. The model is easily extendable in content, parcellation, and labeling, and the proposed approach is applicable for building a whole body vascular system.

The increasing complexity of human body models enabled by advances in diagnostic imaging, computing, and growing knowledge calls for the development of a new generation of systems for intelligent exploration of these models. Here, we introduce a novel paradigm for the exploration of digital body models illustrating cerebral vasculature. It enables dynamic scene compositing, real-time interaction combined with animation, correlation of 3D models with sectional images, quantification as well as 3D manipulation-independent labeling and knowledge-related meta labeling (with name, diameter, description, variants, and references). This novel exploration is incorporated into a 3D atlas of cerebral vasculature with arteries and veins along with the surrounding surface and sectional neuroanatomy derived from 3.0 Tesla scans. This exploration paradigm is useful in medical education, training, research, and clinical applications. It enables development of new generation systems for rapid and intelligent exploration of complicated digital body models in real time with dynamic scene compositing from highly parcellated 3D models, continuous navigation, and manipulation-independent labeling with multiple features., ((c) 2009 American Association of Anatomists)

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.

Background: This work quantifies and visualises 3D inconsistencies of the ventrointermediate nucleus (VIM) of the thalamus, including the VIM externum (VIMe) and VIM internum (VIMi), in the Schaltenbrand-Wahren (SW) brain atlas., Method: For each VIM, VIMe, VIMi the 3D models, 3D-A, 3D-C and 3D-S were reconstructed from the SW axial, coronal and sagittal microseries, respectively, by applying a shape-based method. All 3D models, placed in the SW coordinate system, were compared quantitatively in terms of location (centroids), size (volumes), shape (normalised eigen values), orientation (eigen vectors), and mutual spatial relationships (overlaps and inclusions)., Findings: The reconstructed 3D models differ significantly in location, size, shape, and inclusion rate. The centroid of 3D-A/VIM differs considerably from those of 3D-C/VIM and 3D-S/VIM. The difference between the centroids of 3D-C/VIM and 3D-S/VIM is in laterality only: that of 3D-C/VIM is located more medially (11.85 mm) than that of 3D-S/VIM (14.62 mm). 3D-A/VIM has the smallest volume (69.00 mm(3)); 3D-C/VIM is 3.71 and 3D-S/VIM 3.89 times larger. The overlap is also highly variable: 104.88 mm(3) for 3D-C/VIM with 3D-S/VIM, and very low (3.22 and 7.45 mm(3)) when 3D-A/VIM is involved. The highest inclusion rate is for 3D-C/VIM with 3D-S/VIM (39.10 and 40.97%) and the lowest for 3D-A/VIM with 3D-C/VIM (1.26 and 4.66%). The centroid of 3D-A/VIMe differs noticeably from those of 3D-C/VIMe and 3D-S/VIMe. The difference between the centroids of 3D-C/VIMe and 3D-S/VIMe is mainly in laterality: that of 3D-C/VIMe is located more medially (12.91 mm) than that of 3D-S/VIMe (16.65 mm). 3D-A/VIMe has the smallest volume (49.87 mm(3)); 3D-S/VIMe is 3.24 and 3D-C/VIMe 3.36 times larger. The overlap sizes are low: 32.72 mm(3) for 3D-C/VIMe with 3D-S/VIMe, and very low (1.32 and 2.01 mm(3)) when 3D-A/VIMe is involved. The inclusion rates are also low: the highest is for 3D-C/VIMe with 3D-S/VIMe (19.53 and 20.29%) and the lowest for 3D-A/VIMe with 3D-C/VIMe (1.19 and 4.01%). Lateral scaling of the coronal microseries by 1.2897 to match the 3D-C/VIMe and 3D-S/VIMe centroids increases the inclusion rates for the sagittal microseries by more than twice. The volume of scaled 3D-C enlarges to 216.24 mm(3) which is 1.34 bigger than that of 3D-S. There are substantial differences among the centroids of 3D-A/VIMi, 3D-C/VIMi and 3D-S/VIMi. The centroid of 3D-A/VIMi is located more anteriorly (-1.92 mm) than that of 3D-C/VIMi (-5.02 mm). The centroid of 3D-A/VIMi is located more ventrally (2.88 mm) than those of 3D-C/VIMi and 3D-S/VIMi (each at 5.34 mm). 3D-A/VIMi has the smallest volume (19.75 mm(3)); 3D-S/VIMi is 3.23 and 3D-C/VIMi 4.30 times larger. 3D-A/VIMi practically does not overlap with 3D-C/VIMi and 3D-S/VIMi. The inclusion rates for 3D-C/VIMi with 3D-S/VIMi are medium (32.63 and 43.43%)., Conclusion: Each VIM, VIMe, VIMi as reconstructed from the SW atlas has a significant 3D inaccuracy within each orientation and across them. Therefore, absolute and direct reliance on the original SW atlas is unreliable and unsafe, and this atlas has to be used with great care and understanding of its strengths and limitations.

This paper provides an overview of blood flow in the arterial system and aims to estimate the blood velocity from cerebral angiography scans without having acquired data on velocity by using Murray's Law. The estimation technique post-processes the scan and provides crucial 3D visual data for the development of a visualization program of the blood flow in the human brain.

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.

A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leibler's (KL) measure, which characterizes the difference between two distributions. Slices along the sagittal direction are analyzed with respect to a reference slice to determine the coarse MSP. To calculate the final MSP, a local search algorithm is applied. The proposed method does not need any preprocessing, like reformatting, skull stripping, etc. The algorithm was validated quantitatively on 75 MRI datasets of different pulse sequences (T1WI, T2WI, FLAIR and SPGR) and MRA. The angular and distance errors between the calculated MSP and the ground truth lines marked by the expert were calculated. The average distance and angular deviation were 1.25 pixels and 0.63 degrees , respectively. In addition, the algorithm was tested qualitatively on PD, FLAIR, MRA, and CT datasets. To analyze the robustness of the method against rotation, inhomogeneity and noise, the phantom data were used.

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.

Rationale and Objectives: The human brain demonstrates approximate bilateral symmetry of anatomy, function, neurochemical activity, and electrophysiology. This symmetry reflected in radiological images may be affected by pathology. Hence quantitative analysis of brain symmetry may enable the normal and pathological brain discrimination. We propose a method based on the Jeffreys divergence measure (J-divergence), which attempts to quantify "approximate symmetry" and also aids to classify the brain as bilaterally symmetrical/asymmetrical (normal/abnormal)., Materials and Methods: The dataset included studies of 101 patients (59 without detectable pathologies and 42 with different abnormalities). First, the midsagittal plane is computed for the volume data that divides the head into two hemispheres. The J-divergence is calculated from the density functions of intensities of both the hemispheres. Statistical analysis was conducted to find the best distribution for normal/abnormal datasets., Results: Statistical tests showed that the lognormal distribution best characterizes the values of the J-divergence for both normal and abnormal cases, and the threshold value for the Jeffreys divergence measure to classify the brains with and without detectable pathologies is T = 0.007. The threshold value had a sensitivity of 88.1% and specificity of 90.9%., Conclusion: The proposed method is fast and simple to compute. The high sensitivity and specificity indicate the results are encouraging. This method can be used for the initial analysis of data, detection of pathology, classification of dataset as presumably normal/abnormal, and localization of abnormality.

Rationale and Objectives: A near real-time and fully automatic method for calculation of the midsagittal plane (MSP) for magnetic resonance (MR) diffusion and perfusion images is introduced., Materials and Methods: The method is based on the Kullback-Leibler's (KL) measure quantifying the difference between two intensity distributions. The MSP is a sagittal plane with the highest KL measure. The method was validated quantitatively for 61 diffusion-weighted imaging (DWI), cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), peak height (PKHT), and time to peak (TPP) data sets of 11 stroke patients based on the ground truth provided by two raters., Results: Average angular errors are less than 1 degrees for DWI and less than 2 degrees for CBF and CBV. Average distance errors measured in the worst case (on the brain's bounding box) are less than 2.5 mm for DWI and less than 5 mm for CBF and CBV. This algorithmic accuracy is at the level of interrater variability. Results obtained for the other perfusions maps (MTT, PKHT, TTP) were inferior; therefore, processing of CBF or CBV is preferred for accurate and robust calculation of the MSP from perfusion maps. Calculation of the MSP takes about half a second on a standard computer., Conclusions: The proposed method is near real-time and fully automatic, and neither user interaction nor parameter setting is needed. It does not require preprocessing of data. The method potentially is useful in rapid and automated processing of MR stroke diffusion and perfusion images.

This paper presents a method to improve the semi-automatic method for intensity inhomogeneity correction by Dawant et al. through introducing a fully automatic approach to reference points generation, which is based on order statistics and integrates information from the fine to coarse scale representations of the input image. The method has been validated and compared with two popular methods, N3 and BFC. Advantages of the proposed method are demonstrated.

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.

The major shortcomings of the Schaltenbrand-Wahren (SW) brain atlas include 3-dimensional (3D) inconsistency and spatial sparseness. This work quantifies and visualizes 3D inconsistency of the globus pallidus internus (GPi), a stereotactic target for the treatment of Parkinson's disease, dystonia and Huntington disease. The GPi 3D models 3D-A, 3D-C and 3D-S are reconstructed from the SW axial, coronal and sagittal microseries, respectively, by applying a shape-based (Nonuniform Rational B Splines) method. All three 3D models, placed in the SW coordinate system, are compared quantitatively in terms of location (centroids), size (volumes), shape (normalized eigen values), orientation (eigen vectors) and mutual spatial relationships (overlaps and inclusions). The analysis is done in 3D within each orientation and across them. The reconstructed 3D GPi models substantially differ in location, size and inclusion rate. The centroid of 3D-C is located more medially (15.6 mm) than those of 3D-A (17.5 mm) and 3D-S (18.2 mm), and that of 3D-A more ventrally (-2.3 mm) than those of 3D-C (-0.1 mm) and 3D-S (-0.4 mm). 3D-S has the smallest volume (347.3 mm3); 3D-A is 1.18 and 3D-C 1.85 times larger. The highest inclusion rate is for 3D-S (54.3 and 56.3%) and the lowest for 3D-C (28.8 and 30.6%). A smaller variability is observed in shape, orientation and overlap size (196.8, 196.1 and 185.5 mm3). To get a better correspondence between 3D-C and 3D-S, the coronal microseries were scaled laterally by 1.1667. This results in a substantial improvement of the inclusion rate of 3D-S (87.9%), though raising the volume mismatch to 2.16. The GPi in the SW atlas has a substantial 3D inaccuracy within each orientation and across them. Therefore, absolute and direct reliance on the original atlas is unsafe, and this atlas has to be used with great care and understanding of its limitations. As matching various SW microseries by global scaling is not feasible, we propose the target-dependent scaling based on structure centroid matching., (Copyright 2006 S. Karger AG, Basel.)

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.

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., ((c) RSNA, 2005.)

The Talairach-Tournoux (TT) atlas is probably the most often used brain atlas. We overview briefly the activities in developments of electronic versions of the TT atlas and focus on our more than 10-yr efforts in its continuous enhancement resulting in three main versions: TT-1997, TT-2000, and TT-2004. The recent TT-2004 version is substantially improved over the digitized print original with a higher structure parcellation, better quality and resolution of individual structures, and improved three-dimensional (3D) spatial consistency. It is also much more suitable for developing atlas-based applications owing to pure color-coding (for automatic structure labeling), contour representation (to avoid scan blocking by the overlaid atlas), and color cross-atlas consistency (for the simultaneous use of multiple atlases). We also provide a procedure for 3D spatial consistency improvement and illustrate its use. Finally, we present some of our latest atlas-assisted applications for fast and automatic interpretation of morphological, stroke, and molecular images, and discuss the future steps in TT atlas enhancement.

A knowledge-driven algorithm for a rapid, robust, accurate, and automatic extraction of the human cerebral ventricular system from MR neuroimages is proposed. Its novelty is in combination of neuroanatomy, radiological properties, and variability of the ventricular system with image processing techniques. The ventricular system is divided into six 3D regions: bodies and inferior horns of the lateral ventricles, third ventricle, and fourth ventricle. Within each ventricular region, a 2D region of interest (ROI) is defined based on anatomy and variability. Each ventricular region is further subdivided into subregions, and conditions detecting and preventing leakage into the extra-ventricular space are specified for each subregion. The algorithm extracts the ventricular system by (1) processing each ROI (to calculate its local statistics, determine local intensity ranges of cerebrospinal fluid and gray and white matters, set a seed point within the ROI, grow region directionally in 3D, check anti-leakage conditions, and correct growing if leakage occurred) and (2) connecting all unconnected regions grown by relaxing growing conditions. The algorithm was validated qualitatively on 68 and quantitatively on 38 MRI normal and pathological cases (30 clinical, 20 MGH Brain Repository, and 18 MNI BrainWeb data sets). It runs successfully for normal and pathological cases provided that the slice thickness is less than 3.0 mm in axial and less than 2.0 mm in coronal directions, and the data do not have a high inter-slice intensity variability. The algorithm also works satisfactorily in the presence of up to 9% noise and up to 40% RF inhomogeneity for the BrainWeb data. The running time is less than 5 s on a Pentium 4, 2.0 GHz PC. The best overlap metric between the results of a radiology expert and the algorithm is 0.9879 and the worst 0.9527; the mean and standard deviation of the overlap metric are 0.9723 and 0.01087, respectively.

Objective: The Schaltenbrand-Wahren atlas (SWA) is anatomical, sparse and inconsistent in three dimensions. A high-resolution, electrophysiology-based atlas derived from numerous specimens overcomes these limitations. A combined anatomy-physiology atlas leverages the strengths and complementarity of both, which is studied here., Method: An electronic version of the SWA was constructed. A probabilistic functional atlas (PFA) was developed from electrophysiological and neuroimaging data with 0.25 mm3 resolution. A combined atlas is constructed by co-registering the PFA with the SWA by applying linear scaling along the intercommissural distance and the height of the thalamus., Results: An anatomy-physiology atlas is superior to its component atlases. When in register, the anatomical and functional atlases are displayed together and used simultaneously. A structure of interest from the PFA can be displayed with high resolution, compensating for its sparseness in the SWA, while its surrounding structures are obtained from the SWA. The best anatomical and functional targets can be compared: for the subthalamic nucleus, the horizontal and lateral coordinates of the SWA-based and PFA- based targets are approximately the same, while the latter is located 1.3-1.5 mm more anteriorly., Conclusion: An anatomy-physiology atlas may enhance the accuracy of targeting and increase the neurosurgeon's confidence. It also opens new research avenues to serve as a reference for: (1) constructing an extendable atlas by adding new electrophysiological data, (2) comparing anatomical and functional targets, (3) studying and comparing various symptoms, and (4) comparing image-based targets obtained from various modalities., (2004 S. Karger AG, Basel.)

A rapid algorithm for robust, accurate, and automatic extraction of the midsagittal plane (MSP) of the human cerebrum from normal and pathological neuroimages is proposed. The MSP is defined as a plane formed from the interhemispheric fissure line segments having the dominant orientation. The algorithm extracts the MSP in four steps: (1) determine suitable axial slices for processing, (2) localize the fissure line segments on them, (3) select inliers from the extracted fissure line segments through histogram-based outlier removal, and (4) calculate the equation of the MSP from the selected inliers. The fissure line segments are localized by minimizing the local symmetry index characterizing anatomical properties of images in the vicinity of the interhemispheric fissure. A two-stage angular and distance outlier removal is introduced to handle abnormalities. The algorithm has been validated quantitatively with 125 structural MRI and CT cases from 10 centers on three continents by studying its accuracy; tolerance to rotation, noise, asymmetry, and bias field; sensitivity to parameters; and performance. A statistical relationship between algorithm accuracy and the data's adherence to planarity is also determined. The algorithm extracts the MSP below 6 s on Pentium 4 (2.4 GHz) with the average angular and distance errors of (0.40 degrees; 0.63 mm) for normal and (0.59 degrees; 0.73 mm) for pathological cases. The robustness to noise, asymmetry, rotation, and bias field is achieved by extracting the MSP based on the dominant orientation and local symmetry index. A low computational cost results from applying simple operations capturing intrinsic anatomic features, constraining the searching space to the local vicinity of the interhemispheric fissure, and formulating a noniterative algorithm with a coarse and fine fixed-step searching. In comparison to the existing methods, our algorithm is much faster, performs accurately and robustly for a wide range of diversified data, and is fully automatic and thoroughly validated, which make it suitable for clinical applications.

The paper introduces an optimal algorithm for rapid calculation of a probabilistic functional atlas (PFA) of subcortical structures from data collected during functional neurosurgery procedures. The PFA is calculated based on combined intraoperative electrophysiology, pre- and intraoperative neuroimaging, and postoperative neurological verification. The algorithm converts the coordinates of the neurologically most effective contacts into probabilistic functional maps taking into account the geometry of a stimulating electrode. The PFA calculation comprises the reconstruction of the contact coordinates from two orthogonal projections, normalizing (warping) the contacts modeled as cylinders, voxelizing the contact models, calculating the atlas, and computing probability. In addition, an analytical representation of the PFA is formulated based on Gaussian modeling. The initial PFA has been calculated from the data collected during the treatment of 274 Parkinson's disease patients, most of them operated bilaterally (487 operated hemispheres). It contains the most popular stereotactic targets, the subthalamic nucleus, globus pallidus internus, and ventral intermedius nucleus. The key application of the algorithm is targeting in stereotactic and functional neurosurgery, and it also can be employed in human and animal brain research.

Objective: This paper describes an Internet portal for stereotactic and functional neurosurgery with a probabilistic functional atlas (PFA) calculated from electrophysiological and neuroimaging data. This portal enables (1) data sharing among neurosurgeons; (2) the calculation of probabilistic functional maps of stereotactic target structures from a neurosurgeon's own data, optionally combined with data from other neurosurgeons, and (3) the construction and development of a PFA of the human brain by the neurosurgical community via the Internet., Method: The overall approach is done in three steps: (1) development and validation of the algorithm for PFA generation; (2) design and development of the portal for stereotactic and functional neurosurgery with tools for the rapid calculation, presentation, and use of the PFA, and (3) portal testing with the initial data acquired for 274 Parkinson's disease patients, 487 hemispheres, and 500 best contacts. This paper covers step 2 and some results from step 3., Results: The Internet portal has been developed in Java and tested with the available data. Functions have been developed for: (1) data input, transfer, and editing; (2) data selection for PFA generation; (3) PFA generation; (4) PFA display and interactive manipulation, and (5) target planning. The portal has been put on the Internet for public use and so far more than 100 users downloaded it., Conclusion: The Internet portal for stereotactic and functional neurosurgery with a PFA potentially increases both the accuracy of targeting and the neurosurgeon's confidence. The portal may be useful for both experienced functional neurosurgeons who have studied numerous cases and novices in the field. The use of the PFA through this portal, and sharing and expanding its content by neurosurgeons represents a paradigm shift from manufacturer centric to community centric., (Copyright 2002 S. Karger AG, Basel)

In this paper, a novel non-rigid registration method is proposed for registration of the Talairach-Tournoux brain atlas with MRI images and the Schaltenbrand-Wahren brain atlas. A metalforming principle-based finite element method with the large deformation problem is used to find the local deformation, in which finite element equations are governed by constraints in the form of displacements derived from the correspondence relationship between extracted feature points. Some detectable substructures, such as the cortical surface, ventricles and corpus callosum, are first extracted from MRI, forming feature points which are classified into different groups. The softassign method is used to establish the correspondence relationship between feature points within each group and to obtain the global transformation concurrently. The displacement constraints are then derived from the correspondence relationship. A metalforming principle-based finite element method with the large deformation problem is used in which finite element equations are reorganized and simplified by integrating the displacement constraints into the system equations. Our method not only matches the model to the data efficiently, but also decreases the degrees of freedom of the system and consequently reduces the computational cost. The method is illustrated by matching the Talairach-Tournoux brain atlas to MRI normal and pathological data and to the Schaltenbrand-Wahren brain atlas. We compare the results quantitatively between the force assignment-based method and the proposed method. The results show that the proposed method yields more accurate results in a fraction of the time taken by the previous method.

Objective: This paper introduces a method for localization analysis of functional images assisted by a brain atlas. The usefulness of the system developed, based on this method, is analyzed for human brain mapping and neuroradiology., Materials and Methods: We use an enhanced and extended electronic Talairach-Tournoux brain atlas, containing segmented and labeled subcortical structures, Brodmann's areas, and gyri. The brain atlas serves as a tool for anatomy referencing, segmentation, labeling, registration, and providing 3D anatomical relationships. The process of localization analysis is decomposed into five steps: data loading, feature extraction, data normalization, identification and editing of loci, and getting labels and values. This analysis is supported by near real-time data-to-atlas warping based on the Talairach transformation. Metanalysis is enabled by merging the current and external lists of activation loci., Results: We have designed, developed, tested, and deployed a commercial system for atlas-assisted localization analysis of functional images. This is the first system where an electronic version of the Talairach-Tournoux brain atlas is used interactively for analysis of functional images. This system runs on personal computers and provides functions for a rapid normalization of anatomical and functional volumetric data, data segmentation and labeling, readout of Talairach coordinates, and data display. It also is empowered with several unique features including: interactive warping facilitating fine tuning of the data-to-atlas fit, a backtracking mechanism to compensate for missing landmarks and enhancing the outcome of the overall process of data analysis, navigation on the triplanar formed by the data and the atlas, multiple-images-in-one display with atlas-anatomy-function blending, a fast locus-controlled generation of results, editing of loci, multiple label display, and saving and reading of loci. The system normalizes a single image in near real-time (0.7 s), so analysis of anatomical and functional datasets can be done on-the-fly regardless of the number of slices. The same task performed by the state-of-the-art non-linear registration methods may require up to several days., Conclusions: The system is a useful tool for atlas-assisted localization analysis and a helpful adjunct to function/location metanalysis in human brain mapping research. It is also a step forward in bringing the atlas and the clinical data together within a practical and powerful solution that is fast and flexible, yet low-cost and affordable.

This paper presents a novel approach for the use of the Atlas for Stereotaxy of the Human Brain by Schaltenbrand and Wahren [Stuttgart, Thieme, 1977] for anatomical targeting in functional neurosurgery. We propose to use simultaneously all three electronic axial, coronal and sagittal mutually coregistered Schaltenbrand-Wahren brain atlas microseries. The printed atlas microseries are digitized, extended to cover both hemispheres, contoured, labeled, organized into atlas volumes, and mutually coregistered. The electronic atlas is interactively registered with the data by using the three-dimensional Talairach proportional grid system transformation, followed up by local warping in the region of interest based on any clearly visible landmarks. The detailed targeting steps for pallidotomy, thalamotomy and subthalamotomy are formulated. The potential of this approach is to increase the accuracy of target definition, to decrease the time of the procedure by reducing the number of microelectrode tracts, and to give an extra degree of confidence to the neurosurgeon. The advantages of the approach and the limitations of the Schaltenbrand-Wahren atlas are discussed.

The Electronic Clinical Brain Atlas is a CD-ROM containing several classic brain atlases and a real-time yet simple registration function that deforms the atlases to match them with specific patient studies. This article presents the use of this registration function for functional neurosurgery planning. We first propose the CD-ROM assisted planning procedure, then illustrate it with two cases: a pallidal stimulation and a thalamic stimulation. The Schaltenbrand-Wahren atlas is registered and scaled to conform with an actual patient's data by means of two-dimensional (2-D) local deformations performed in multiple orientations. First a rectangular region of interest (ROI), which is set between any clearly visible landmarks chosen by the neurosurgeon, is measured on the film or scanner console. The corresponding atlas plate with the target is then deformed in real time for the same landmarks, such that the dimensions of this ROI are the same on the film and on the deformed atlas plate. Next the target is set on the deformed (individualized) atlas plate and its coordinates are read. The individualized atlas plate can also be printed on transparent foil and superimposed on the film or, alternatively, this superimposition can be done electronically. The planning steps can be repeated for all available orientations. The proposed atlas-assisted planning procedure extends the traditional use of printed stereotactic atlases by individualizing them to specific patients. The preliminary results show that this procedure may improve the definition of the target and may have several advantages over other approaches, such as indirect measurements based on the AC-PC line or 1-D (intercommissural distance based) scaling. It provides the neurosurgeon with a convenient and immediate means of accessing ancillary data that is usually only available in the printed form.

We present a suite of neurosurgery supporting tools developed around (i) the Virtual Workbench, a productive environment for the control of 3-D data, in which delicate work can be performed for hours on end without strain, and (ii) the Electronic Brain Atlas, integrating the major print brain atlases in day-to-day clinical use. We describe in detail the Brain Bench, a surgical planning system for stereotactic frame neurosurgery. Its objective is to prepare faster plans; have a better and more accurate choice of target points; improve the avoidance of sensitive structures; have fewer sub-optimal frame attachments and speedier, more effective planning and training. If validated by a clinical study now under way, this will improve medical efficacy and reduce costs.

The authors present their experience in the use of an atlas-based computer system for preoperative functional neurosurgery planning and postoperative analysis. It has also some potential for intraoperative support. The system is based on a deformable electronic version of "Atlas of Stereotaxy of the Human Brain" by Schaltenbrand and Wahren. This atlas is used for interactive segmentation and labelling of clinical data in two- and three dimensions, and for definition of stereotactic targets. The Schaltenbrand-Wahren atlas microseries are digitized, enhanced, segmented, labelled, aligned and organized into atlas volumes. They are mutually preregistered, and three-dimensional models of the structures are constructed. The atlas may be interactively registered with an actual patient's data. A computer system is developed which provides data interpolation, reformatting, registration, visualization, navigation, mensuration and path display and editing in two- and three dimensions. The system increases the accuracy of target definition, reduces the time of planning and the time of the procedure itself. It also constitutes a research platform for the construction of more advanced neurosurgery supporting tools and brain atlases.

For the purpose of developing multiple, complementary, fully labeled electronic brain atlases and an atlas-based neuroimaging system for analysis, quantification, and real-time manipulation of cerebral structures in two and three dimensions, we have digitized, enhanced, segmented, and labeled the following print brain atlases: Co-Planar Stereotaxic Atlas of the Human Brain by Talairach and Tournoux, Atlas for Stereotaxy of the Human Brain by Schaltenbrand and Wahren, Referentially Oriented Cerebral MRI Anatomy by Talairach and Tournoux, and Atlas of the Cerebral Sulci by Ono, Kubik, and Abernathey. Three-dimensional extensions of these atlases have been developed as well. All two- and three-dimensional atlases are mutually preregistered and may be interactively registered with an actual patient's data. An atlas-based neuroimaging system has been developed that provides support for reformatting, registration, visualization, navigation, image processing, and quantification of clinical data. The anatomical index contains about 1,000 structures and over 400 sulcal patterns. Several new applications of the brain atlas database also have been developed, supported by various technologies such as virtual reality, the Internet, and electronic publishing. Fusion of information from multiple atlases assists the user in comprehensively understanding brain structures and identifying and quantifying anatomical regions in clinical data. The multiple brain atlas database and atlas-based neuroimaging system have substantial potential impact in stereotactic neurosurgery and radiotherapy by assisting in visualization and real-time manipulation in three dimensions of anatomical structures, in quantitative neuroradiology by allowing interactive analysis of clinical data, in three-dimensional neuroeducation, and in brain function studies.