More accurate Talairach coordinates for neuroimaging using non-linear registration

While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e.g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.     

中国人群帕金森病患者灰质萎缩:采用中国人脑图谱Chinese2020的基于体素的形态学研究

目的 采用基于体素的形态学测量（VBM）方法探讨中国人脑图谱Chinese2020检测中国人群帕金森病（PD）患者灰质体积改变的价值。方法 收集15例PD患者（PD组）和15名性别和年龄匹配的健康志愿者为对照组。分别采用基于中国人群的脑图谱Chinese2020和基于高加索人群的脑图谱MNI152进行空间标准化,比较配准到这2种标准空间所引起的脑结构形变差异,并采用VBM方法比较2种图谱检出PD患者灰质体积萎缩的脑区以及萎缩脑区灰质占比的差异。结果 采用MNI152脑图谱发现PD患者灰质萎缩脑区包括双侧颞叶并扩展至同侧脑岛/海马/海马旁回、左侧枕上回/楔叶/楔前叶及右侧壳核;采用Chinese2020图谱后,除上述脑区外,右侧额中回亦可见灰质体积萎缩。PD组和对照组受试者脑结构配准到Chinese2020空间发生形变小于配准到MNI152空间,且基于Chinese2020检测灰质萎缩脑区灰质占比高于采用MNI152图谱（t=2.502,P=0.037）。结论 基于中国人群的神经影像学研究应该采用中国人脑图谱进行空间标准化,其脑结构形变更小、所识别出的脑区灰质占比更高,从而提高检测的准确性。     

Comparison of different methodological implementations of voxel-based morphometry in neurodegenerative disease

Voxel-based morphometry (VBM) is a popular method for probing inter-group differences in brain morphology. Variation in the detailed implementation of the algorithm, however, will affect the apparent results of VBM analyses and in turn the inferences drawn about the anatomic expression of specific disease states. We qualitatively assessed group comparisons of 43 normal elderly control subjects and 51 patients with probable Alzheimer's disease, using five different VBM variations. Based on the known pathologic expression of the disease, we evaluated the biological plausibility of each. The use of a custom template and custom tissue class prior probability images (priors) produced inter-group comparison maps with greater biological plausibility than the use of the Montreal Neurological Institute (MNI) template and priors. We present a method for initializing the normalization to a custom template, and conclude that, when incorporated into the VBM processing chain, it yields the most biologically plausible inter-group differences of the five methods presented.     

Effects of spatial transformation on regional brain volume estimates

Spatial transformation of MR brain images is a standard tool used in automated anatomical parcellation and other quantitative and qualitative methods to assess brain tissue volume, composition, and distribution. Despite widespread use, the quantitative effects of spatial transformation on regional brain volume estimates have been little studied. We report on the effects of transformation on regional brain volumes of 38 (17M, 21F) manually parcellated brains. After tracing in native space, regions of interest were transformed using a classic piecewise-linear Talairach transformation (Tal) or a nonlinear registration (AIR 5th order nonlinear algorithm, 158 parameters) to one of three Talairach-based templates: 1) Tal50, constructed from 50 Talairach-transformed normal brains, 2) the MNI 305 atlas, 3) IA38, constructed from MNI305-transformed scans of the 38 subjects used in this study. Native volumes were compared to the transformed volumes. We found that: 1) significant group-level differences can be obtained in transformed data sets that are in the opposite direction of effects obtained in native space; 2) the effects of transformation are heterogeneous across brain regions, even after covarying for total brain volume and age; 3) volumetric intra-class correlations between native and transformed brains differ by registration method and template choice, region, and tissue type; and 4) transformed brains produced hippocampus and corpus callosum volume proportions that were significantly different from those obtained in native space. Our results suggest that region-based volumetric differences uncovered by spatial-transformation-based methods should be replicated in native-space brains, and that meta-analyses should take into account whether volumes are determined using spatially-transformed images and/or specific automated methods.     

RABBIT: Rapid alignment of brains by building intermediate templates

A brain image registration algorithm, referred to as RABBIT, is proposed to achieve fast and accurate image registration with the help of an intermediate template generated by a statistical deformation model. The statistical deformation model is built by principal component analysis (PCA) on a set of training samples of brain deformation fields that warp a selected template image to the individual brain samples. The statistical deformation model is capable of characterizing individual brain deformations by a small number of parameters, which is used to rapidly estimate the brain deformation between the template and a new individual brain image. The estimated deformation is then used to warp the template, thus generating an intermediate template close to the individual brain image. Finally, the shape difference between the intermediate template and the individual brain is estimated by an image registration algorithm, e.g., HAMMER. The overall registration between the template and the individual brain image can be achieved by directly combining the deformation fields that warp the template to the intermediate template, and the intermediate template to the individual brain image. The algorithm has been validated for spatial normalization of both simulated and real magnetic resonance imaging (MRI) brain images. Compared with HAMMER, the experimental results demonstrate that the proposed algorithm can achieve over five times speedup, with similar registration accuracy and statistical power in detecting brain atrophy.     

A spatially unbiased atlas template of the human cerebellum

This article presents a new high-resolution atlas template of the human, cerebellum and brainstem, based on the anatomy of 20 young healthy individuals. The atlas is spatially unbiased, i.e., the location of each structure is equal to the expected location of that structure across individuals in MNI space, a result that is cross-validated with an independent sample of 16 individuals. At the same time, the new template preserves the anatomical detail of cerebellar structures through a nonlinear atlas generation algorithm. In comparison to current whole-brain templates, it allows for an improved voxel-by-voxel normalization for functional MRI and lesion analysis. Alignment to the template requires that the cerebellum and brainstem are isolated from the surrounding tissue, a process for which an automated algorithm has been developed. Compared to normalization to the MNI whole-brain template, the new method strongly improves the alignment of individual fissures, reducing their spatial spread by 60%, and improves the overlap of the deep cerebellar nuclei. Applied to functional MRI data, the new normalization technique leads to a 5-15% increase in peak t values and in the activated volume in the cerebellar cortex for movement vs. rest contrasts. This indicates that the new template significantly improves the overlap of functionally equivalent cerebellar regions across individuals. The template and software are freely available as an SPM-toolbox, which also allows users to relate the new template to the annotated volumetric (Schmahmann, J.D., Doyon, J., Toga, A., Petrides, M., Evans, A. (2000). MRI atlas of the human cerebellum. San Diego: Academic Press) and surface-based (Van Essen, D.C. (2002a) Surface-based atlases of cerebellar cortex in the human, macaque, and mouse. Ann. N. Y. Acad. Sci. 978:468-479.) atlas of one individual, the "colin27"-brain.     

A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: preliminary results

Commonly used brain templates are based on adults' or children's brains. In this study, we create a neonatal brain template. This becomes necessary because of the pronounced differences not only in size but even more importantly in geometrical proportions of the brains of adults and children as compared to the ones of newborns. The template is created based on high resolution T1 magnetic resonance images of 7 individuals with gestational ages between 39 and 42 weeks at the dates of examination. As usual, the created template presents two characteristics in a single image: an average intensity and an average shape. The normalization process to map subjects to the same space is done using SPM2 (Statistical Parametric Mapping) and its deformation toolbox. It consists of two steps: an affine and a nonlinear registration for global and local alignments, respectively. The template was evaluated by (i) study of anatomical local deviations and (ii) amount of local deformations of brain tissues in normalized neonatal images. The extracted results were compared with the ones obtained by normalization using adult and pediatric templates. It was shown that the application of our neonatal brain template for alignment of neonatal images results in a pronounced increase in performance of the normalization procedure as indicated by reduction of deviation of anatomical equivalent structures. The neonatal atlas template is freely downloadable from http://www.u-picardie.fr/labo/GRAMFC.     

Segmentation of White Matter Flaring Areas in Ultrasound Images of Very-Low-Birth-Weight Preterm Infants

In this article, we present an interactive algorithm segmenting white brain matter, visible as hyperechoic flaring areas in ultrasound (US) images of preterm infants with periventricular leukomalacia (PVL). The algorithm combines both the textural properties of pathological brain tissue and mathematical morphology operations. An initial flaring area estimate is derived from a multifeature multiclassifier tissue texture classifier. This area is refined based on the structural properties of the choroid plexus, a brain feature known to have characteristics similar to flaring. Subsequently, a combination of a morphological closing, gradient and opening by reconstruction operation determines the final flaring area boundaries. Experimental results are compared with a gold standard constructed from manual flaring area delineations of 12 medical experts. In addition, we compared our algorithm to an existing active contour method. The results show our technique agrees to the gold standard with statistical significance and outperforms the existing method in accuracy. Finally, using the flaring area as a criterion we improve the sensitivity of PVL detection up to 98% as compared with the state of the art. (E-mail: ervsteen@telin.ugent.be)     

Volumes, spatial extents and a probabilistic atlas of the human basal ganglia and thalamus

The basal ganglia and thalamus are involved in processing all physiological behaviors and affected by many diseases. Accurate localization is a crucial issue in neuroimaging, particularly when working with groups of normalized images in a standard stereotaxic space. Here, manual delineation of the central structures (thalamus; nucleus caudatus and accumbens; putamen, pallidum, substantia nigra) was performed on 30 high resolution MRIs of healthy young adults (15 female, median age 31 years) in native space. Protocol inter-rater reliabilities were quantified as structure overlap (similarity indices, SIs). Structural volumes were calculated in native space, and after spatial normalization to stereotaxic space (MNI/ICBM152) and in relation to hemispheric volumes. Spatial extents relative to the anterior commissure (AC) were extracted. The 30 resulting atlases were then used to create probabilistic maps in stereotaxic space. Inter-rater SIs were high at 0.85-0.92 except for the nucleus accumbens. In native space, caudate, nucleus accumbens and putamen were significantly larger on the left, and the globus pallidus larger in males. After normalizing for brain volume, the nucleus accumbens, putamen and thalamus were larger on the left, with the gender difference in the globus pallidus still detectable. Some of these volume differences translated into significantly different distances from the AC. The probabilistic maps showed that overall the central structures' boundaries are relatively unchanged after spatial normalization. We present a comprehensive assessment of thalamic and basal ganglia volumetric and geometric data in both native and stereotaxic spaces. Probabilistic maps in MNI/ICBM152 space will allow accurate localization in group analyses.     

Skull stripping based on region growing for magnetic resonance brain images

In this paper, we propose a new skull stripping method for T1-weighted magnetic resonance (MR) brain images. Skull stripping has played an important role in neuroimage research because it is a basic preliminary step in many clinical applications. The process of skull stripping can be challenging due to the complexity of the human brain, variable parameters of MR scanners, individual characteristics, etc. In this paper, we aim to develop a computationally efficient and robust method. In the proposed algorithm, after eliminating the background voxels with histogram analysis, two seed regions of the brain and non-brain regions were automatically identified using a mask produced by morphological operations. Then we expanded these seed regions with a 2D region growing algorithm based on general brain anatomy information. The proposed algorithm was validated using 56 volumes of human brain data and simulated phantom data with manually segmented masks. It was compared with two popular automated skull stripping methods: the brain surface extractor (BSE) and the brain extraction tool (BET). The experimental results showed that the proposed algorithm produced accurate and stable results against data sets acquired from various MR scanners and effectively addressed difficult problems such as low contrast and large anatomical connections between the brain and surrounding tissues. The proposed method was also robust against noise, RF, and intensity inhomogeneities.     

Enantiomorphic normalization of focally lesioned brains

In order to make spatial inferences about the brain across a group of patients, it is usually necessary to employ some means of bringing each brain image into register with either a group mean image or a standard template. In the presence of focal brain lesions, automated methods for performing such so-called normalization are liable to distortion from the abnormal signal within the lesion, especially when the non-linear warping necessary for maximum registration fidelity is used. The most frequently used method for minimizing this distortion--cost function masking--simply eliminates the lesioned area when deriving the normalization parameters. As lesion size increases, however, the normalization error may be expected to rise steeply since the volume of brain from which the parameters are derived falls with it. Here we propose an alternative non-linear registration method that exploits a natural redundancy in the brain--the enantiomorphic relation between the two hemispheres--to correct the signal within the lesion using information from the undamaged homologous region within the contralesional hemisphere. As lesion size increases, the normalization error should theoretically asymptote to inter-hemispheric differences, which are both quantifiable and much lower than the inter-subject difference. Using SPM's non-linear normalization routines, we evaluate this technique with images of normal brains to which lesions selected from a large dataset have been artificially applied. Our results show the enantiomorphic method to be vastly superior to cost function masking across subjects, lesion characteristics, and brain voxels. We therefore propose that it should be the method of choice for normalizing images of focally lesioned brains.     

Spatial normalization of lesioned brains: performance evaluation and impact on fMRI analyses

A key component of group analyses of neuroimaging data is precise and valid spatial normalization (i.e., inter-subject image registration). When patients have structural brain lesions, such as a stroke, this process can be confounded by the lack of correspondence between the subject and standardized template images. Current procedures for dealing with this problem include regularizing the estimate of warping parameters used to match lesioned brains to the template, or "cost function masking"; both these solutions have significant drawbacks. We report three experiments that identify the best spatial normalization for structurally damaged brains and establish whether differences among normalizations have a significant effect on inferences about functional activations. Our novel protocols evaluate the effects of different normalization solutions and can be applied easily to any neuroimaging study. This has important implications for users of both structural and functional imaging techniques in the study of patients with structural brain damage.     

Improved parametric image generation using spatial-temporal analysis of dynamic PET studies

The value of parametric images that represent both spatial distribution and quantification of the physiological parameters of tracer kinetics has long been recognized. However, the inherent high noise level of pixel kinetics of dynamic PET makes it unsuitable to generate parametric images of the microparameters of tracer kinetic model by conventional weighted nonlinear least squares (WNLS) fitting. Based on the concept that both spatial and temporal information should be integrated to improve parametric image quality, a nonlinear ridge regression with spatial constraint (NLRRSC) parametric imaging algorithm was proposed in this study. For NLRRSC, a term that penalizes local spatial variation of parameters was added to the cost function of WNLS fitting. The initial estimates and spatial constraint were estimated by component representation model (CRM) with cluster analysis. A hierarchical cluster with average linkage method was used to extract components. The ridge parameter was determined by linear ridge regression theory at each iteration, and a modified Gauss-Newton algorithm was used for minimizing the cost function. Results from a computer simulation showed that the percent mean square error of estimates obtained by NLRRSC can be decreased by 60-80% compared to that of WNLS. The parametric images estimated by NLRRSC are significantly better than the ones generated by WNLS. A highly correlated linear relationship was found between the ROI values calculated from the microparametric images generated by NLRRSC and estimates from ROI kinetic fitting. NLRRSC provided a reliable estimate of glucose metabolite uptake rate with a comparable image quality compared to Patlak analysis. In conclusion, NLRRSC is a reliable and robust parametric imaging algorithm for dynamic PET studies.     

Optimum template selection for atlas-based segmentation

Atlas-based segmentation of MR brain images typically uses a single atlas (e.g., MNI Colin27) for region identification. Normal individual variations in human brain structures present a significant challenge for atlas selection. Previous researches mainly focused on how to create a specific template for different requirements (e.g., for a certain population). We address atlas selection with a different approach: instead of choosing a fixed brain atlas, we use a family of brain templates for atlas-based segmentation. For each subject and each region, the template selection method automatically chooses the 'best' template with the highest local registration accuracy, based on normalized mutual information. The region classification performances of the template selection method and the single template method were quantified by the overlap ratios (ORs) and intraclass correlation coefficients (ICCs) between the manual tracings and the respective automated labeled results. Two groups of brain images and multiple regions of interest (ROIs), including the right anterior cingulate cortex (ACC) and several subcortical structures, were tested for both methods. We found that the template selection method produced significantly higher ORs than did the single template method across all of the 13 analyzed ROIs (two-tailed paired t-test, right ACC at t(8)=4.353, p=0.0024; right amygdala, matched paired t test t(8)>3.175, p<0.013; for the remaining ROIs, t(8)=4.36, p<0.002). The template selection method also provided more reliable volume estimates than the single template method with increased ICCs. Moreover, the improved accuracy of atlas-based segmentation using optimum templates approaches the accuracy of manual tracing, and thus is valid for automated brain imaging analyses.     

An MRI study of spatial probability brain map differences between first-episode schizophrenia and normal controls

We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects' magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.     

Assessment of the impact of model-based scatter correction on [18F]-FDG 3D brain PET in healthy subjects using statistical parametric mapping

It is recognized that scatter correction can supply more accurate absolute quantification, and that iterative reconstruction results in better noise properties and significantly reduces streak artefacts; however, it is not entirely clear whether they produce significant changes in [18F]-FDG distribution of reconstructed 3D brain PET images relative to not scatter corrected images and analytic reconstruction procedures. The current study assesses the effect of model-based scatter correction using the single-scatter simulation algorithm and iterative reconstruction in 3D brain PET studies, using statistical parametric mapping (SPM) analysis. The study population consisted of 14 healthy volunteers (6 males, 8 females; age 63-80 years). PET images were reconstructed using an analytic 3DRP reprojection algorithm with (SC) and without explicit scatter correction (NSC), as well as using an iterative ordered subset-expectation maximization (OSEM) algorithm. Calculated attenuation correction was performed assuming uniform attenuation (mu = 0.096 cm(-1)) for brain tissues when data are precorrected for scatter. The broad-beam attenuation coefficient (mu = 0.06 cm(-1)) determined from phantom studies was applied to NSC images. The images were coregistered and normalized using the default [15O]-H2O template supplied with SPM99 and an [18F]-FDG template. A t statistic image for the contrast condition effect was then constructed. The contrast comparing SC to NSC images suggest that regional brain metabolic activity decreases significantly in the frontal gyri, in addition to the middle temporal and postcentral gyri. On the other hand, activity increases in the cerebellum, thalamus, insula, brainstem, temporal lobe, and the frontal cortex. No significant changes were detected when comparing images reconstructed using analytic and iterative algorithms. It is concluded that, for some cerebral areas, significant differences in [18F]-FDG distribution arise when images are reconstructed with and without explicit SC. This needs to be considered when interpreting [18F]-FDG 3D brain PET images after applying SC.     

Spatial normalization of brain images with focal lesions using cost function masking

In studies of patients with focal brain lesions, it is often useful to coregister an image of the patient's brain to that of another subject or a standard template. We refer to this process as spatial normalization. Spatial normalization can improve the presentation and analysis of lesion location in neuropsychological studies; it can also allow other data, for example from functional imaging, to be compared to data from other patients or normal controls. In functional imaging, the standard procedure for spatial normalization is to use an automated algorithm, which minimizes a measure of difference between image and template, based on image intensity values. These algorithms usually optimize both linear (translations, rotations, zooms, and shears) and nonlinear transforms. In the presence of a focal lesion, automated algorithms attempt to reduce image mismatch between template and image at the site of the lesion. This can lead to significant inappropriate image distortion, especially when nonlinear transforms are used. One solution is to use cost-function masking-masking the areas used in the calculation of image difference-to exclude the area of the lesion, so that the lesion does not bias the transformations. We introduce and evaluate this technique using normalizations of a selection of brains with focal lesions and normal brains with simulated lesions. Our results suggest that cost-function masking is superior to the standard approach to this problem, which is affine-only normalization; we propose that cost-function masking should be used routinely for normalizations of brains with focal lesions.     

A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity

This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present an expectation maximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background factors and noise artifacts and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of localization with beamforming algorithms.