A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinson׳s disease cohort.

Parkinson׳s disease (PD) is a neurodegenerative disease that primarily affects the motor functions of the patients. Research and surgical treatment of PD (e.g., deep brain stimulation) often require human brain atlases for structural identification or as references for anatomical normalization. However, two pitfalls exist for many current atlases used for PD. First, most atlases do not represent the disease-specific anatomy as they are based on healthy young subjects. Second, subcortical structures, such as the subthalamic nucleus (STN) used in deep brain stimulation procedures, are often not well visualized. The dataset described in this Data in Brief is a population-averaged atlas that was made with 3 T MRI scans of 25 PD patients, and contains 5 image contrasts: T1w (FLASH & MPRAGE), T2*w, T1-T2* fusion, phase, and an R2* map. While the T1w, T2*w, and T1-T2* fusion templates provide excellent anatomical details for both cortical and sub-cortical structures, the phase and R2* map contain bio-chemical features. Probabilistic tissue maps of whiter matter, grey matter, and cerebrospinal fluid are provided for the atlas. We also manually segmented eight subcortical structures: caudate nucleus, putamen, globus pallidus internus and externus (GPi & GPe), thalamus, STN, substantia nigra (SN), and the red nucleus (RN). Lastly, a co-registered histology-derived digitized atlas containing 123 anatomical structures is included. The dataset is made freely available at the MNI data repository accessible through the link http://nist.mni.mcgill.ca/?p=1209.

Creation of Computerized 3D MRI-Integrated Atlases of the Human Basal Ganglia and Thalamus.

Functional brain imaging and neurosurgery in subcortical areas often requires visualization of brain nuclei beyond the resolution of current magnetic resonance imaging (MRI) methods. We present techniques used to create: (1) a lower resolution 3D atlas, based on the Schaltenbrand and Wahren print atlas, which was integrated into a stereotactic neurosurgery planning and visualization platform (VIPER); and (2) a higher resolution 3D atlas derived from a single set of manually segmented histological slices containing nuclei of the basal ganglia, thalamus, basal forebrain, and medial temporal lobe. Both atlases were integrated to a canonical MRI (Colin27) from a young male participant by manually identifying homologous landmarks. The lower resolution atlas was then warped to fit the MRI based on the identified landmarks. A pseudo-MRI representation of the high-resolution atlas was created, and a non-linear transformation was calculated in order to match the atlas to the template MRI. The atlas can then be warped to match the anatomy of Parkinson's disease surgical candidates by using 3D automated non-linear deformation methods. By way of functional validation of the atlas, the location of the sensory thalamus was correlated with stereotactic intraoperative physiological data. The position of subthalamic electrode positions in patients with Parkinson's disease was also evaluated in the atlas-integrated MRI space. Finally, probabilistic maps of subthalamic stimulation electrodes were developed, in order to allow group analysis of the location of contacts associated with the best motor outcomes. We have therefore developed, and are continuing to validate, a high-resolution computerized MRI-integrated 3D histological atlas, which is useful in functional neurosurgery, and for functional and anatomical studies of the human basal ganglia, thalamus, and basal forebrain.

Watershed cuts: thinnings, shortest path forests, and topological watersheds.

We recently introduced watershed cuts, a notion of watershed in edge-weighted graphs. In this paper, our main contribution is a thinning paradigm from which we derive three algorithmic watershed cut strategies: The first one is well suited to parallel implementations, the second one leads to a flexible linear-time sequential implementation, whereas the third one links the watershed cuts and the popular flooding algorithms. We state that watershed cuts preserve a notion of contrast, called connection value, on which several morphological region merging methods are (implicitly) based. We also establish the links and differences between watershed cuts, minimum spanning forests, shortest path forests, and topological watersheds. Finally, we present illustrations of the proposed framework to the segmentation of artwork surfaces and diffusion tensor images.

Watershed cuts: minimum spanning forests and the drop of water principle.

We study the watersheds in edge-weighted graphs. We define the watershed cuts following the intuitive idea of drops of water flowing on a topographic surface. We first establish the consistency of these watersheds: They can be equivalently defined by their "catchment basins" (through a steepest descent property) or by the "dividing lines" separating these catchment basins (through the drop of water principle). Then, we prove, through an equivalence theorem, their optimality in terms of minimum spanning forests. Afterward, we introduce a linear-time algorithm to compute them. To the best of our knowledge, similar properties are not verified in other frameworks and the proposed algorithm is the most efficient existing algorithm, both in theory and in practice. Finally, the defined concepts are illustrated in image segmentation, leading to the conclusion that the proposed approach improves, on the tested images, the quality of watershed-based segmentations.

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

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

New characterizations of simple points in 2D, 3D, and 4D discrete spaces.

A point of a discrete object is called simple if it can be deleted from this object without altering topology. In this article, we present new characterizations of simple points which hold in dimensions 2, 3 and 4, and which lead to efficient algorithms for detecting such points. In order to prove these characterizations, we establish two confluence properties of the collapse operation which hold in the neighborhood of a point in spaces of low dimension. This work is settled in the framework of cubical complexes, which provides a sound topological basis for image analysis, and allows to retrieve the main notions and results of digital topology, in particular the notion of simple point.

Towards a validation of atlas warping techniques.

Pre-operative magnetic resonance (MRI) and computed tomography (CT) image volumes are often used for planning and guidance during functional neurosurgical procedures. These operations can include the creation of lesions in the thalamus (thalamotomy) or the globus pallidus (pallidotomy), or the insertion of deep brain stimulation (DBS) electrodes in the subcortical nuclei. These subcortical targets are often difficult to localize in pre-operative imaging data due to the limited resolution and contrast available in standard MRI or CT techniques. To address this problem, digital atlases of subcortical nuclei are often used to accurately identify surgical targets since they can be warped to fit each patient's unique anatomy. Targeting accuracy thus depends on the quality of the atlas-to-patient warp. In this paper, three atlas-to-patient warping techniques are compared. Two methods rely on an MRI template as an intermediary to estimate a nonlinear atlas-to-patient transformation. The third is novel, and uses a pseudo-MRI derived from an atlas of the basal ganglia and thalamus to estimate the nonlinear atlas-to-patient transformation directly. The methods are compared using (1) manual segmentations of subcortical nuclei and (2) functional data from intra-operative thalamic stimulation. The results demonstrate that the template-based atlas-to-patient warping technique is the best of the three for customizing the atlas onto patient data.

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

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

Towards a multi-modal atlas for neurosurgical planning.

Digital brain atlases can be used in conjuction with magnetic resonance imaging (MRI) and computed tomography (CT) for planning and guidance during neurosurgery. Digital atlases are advantageous since they can be warped nonlinearly to fit each patient's unique anatomy. Functional neurosurgery with implantation of deep brain stimulating (DBS) electrodes requires accurate targeting, and has become a popular surgical technique in Parkinsonian patients. In this paper, we present a method for integrating postoperative data from subthalamic (STN) DBS implantation into an antomical atlas of the basal ganglia and thalamus. The method estimates electrode position from post-operative magnetic resonance imaging (MRI) data. These electrodes are then warped back into the atlas space and are modelled in three dimensions. The average of these models is then taken to show the region where the majority of STN DBS electrodes were implanted. The group with more favorable post-operative results was separated from the group which responded to the STN DBS implantation procedure less favourably to create a probablisitic distribution of DBS in the STN electrodes.

Anatomical and electrophysiological validation of an atlas for neurosurgical planning.

Digital brain atlases can be used in conjunction with magnetic resonance imaging (MRI) and computed tomography (CT) for planning and guidance during neurosurgery. Digital atlases are advantageous, since they can be warped nonlinearly to fit each patient's unique anatomy. Two atlas-to-patient warping techniques are compared in this paper. The first technique uses an MRI template as an intermediary to estimate a nonlinear atlas-to-patient transformation. The second, is novel, and uses a pseudo-MRI volume, derived from the voxel-label-atlas, to estimate the atlas-to-patient transformation directly. Manual segmentations and functional data are used to validate the two methods.

Optimal location of thalamotomy lesions for tremor associated with Parkinson disease: a probabilistic analysis based on postoperative magnetic resonance imaging and an integrated digital atlas.

OBJECT: Renewed interest in stereotactic neurosurgery for movement disorders has led to numerous reports of clinical outcomes associated with different treatment strategies. Nevertheless, there is a paucity of autopsy and imaging data that can be used to describe the optimal size and location of lesions or the location of implantable stimulators. In this study the authors correlated the clinical efficacy of stereotactic thalamotomy for tremor with precise anatomical localization by using postoperative magnetic resonance (MR) imaging and an integrated deformable digital atlas of subcortical structures. METHODS: Thirty-one lesions were created by stereotactic thalamotomy in 25 patients with tremor-dominant Parkinson disease. Lesion volume and configuration were evaluated by reviewing early postoperative MR images and were correlated with excellent, good, or fair tremor outcome categories. To allow valid comparisons of configurations of lesions with respect to cytoarchitectonic thalamic boundaries, the MR image obtained in each patient was nonlinearly deformed into a standardized MR imaging space, which included an integrated atlas of the basal ganglia and thalamus. The volume and precise location of lesions associated with different clinical outcomes were compared using nonparametric statistical methods. Probabilistic maps of lesions in each tremor outcome category were generated and compared. Statistically significant differences in lesion location between excellent and good. and excellent and fair outcome categories were demonstrated. On average, lesions associated with excellent outcomes involved thalamic areas located more posteriorly than sites affected by lesions in the other two outcome groups. Subtraction analysis revealed that lesions correlated with excellent outcomes necessarily involved the interface of the nucleus ventralis intermedius (Vim; also known as the ventral lateral posterior nucleus [VLp]) and the nucleus ventrocaudalis (Vc; also known as the ventral posterior [VP] nucleus). Differences in lesion volume among outcome groups did not achieve statistical significance. CONCLUSIONS: Anatomical evaluation of lesions within a standardized MR image-atlas integrated reference space is a useful method for determining optimal lesion localization. The results of an analysis of probabilistic maps indicates that optimal relief of tremor is associated with lesions involving the Vim (VLp) and the anterior Vc (VP).