ISSN:1065-9471    

DOI:10.1002/hbm.460030302

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractThis paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time‐series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley‐Liss,

ISSN:1065-9471    

DOI:10.1002/hbm.460030303

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractExplicit segmentation is required for many forms of quantitative neuroanatomic analysis. However, manual methods are time‐consuming and subject to errors in both accuracy and reproducibility (precision). A 3‐D model‐based segmentation method is presented in this paper for the completely automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI).The approach depends on a general, iterative, hierarchical non‐linear registration procedure and a 3‐D digital model of human brain anatomy that contains both volumetric intensity‐based data and a geometric atlas. Here, the traditional segmentation strategy is inverted: instead of matching geometric contours from and idealized atlas directly to the MRI data, segmentation is achieved by identifying the non‐linear spatial transformation that best maps corresponding intensity‐based features between a model image and a new MRI brain volume. When completed, atlas contours defined on the model image are mapped through the same transformation to segment and label individual structures in the new data set.Using manually segmented sturcture boundaries for comparison, measures of volumetric difference and volumetric overlap were less than 2% and better than 97% for realistic brain phantom data, and less than 10% and better than 85%, respectively, for human MRI data. This compares favorably to intra‐observer variability estimates of 4.9% and 87%, respectively. The procedure performs well, is objective and its implementation robust. The procedure requires no manual intervention, and is thus applicable to studies of large numbers of subjects. The general method for non‐linear image matching is also useful for non‐linear mapping of brain data sets into stereotaxic space if the target volume is already in stereotaxic space.

ISSN:1065-9471    

DOI:10.1002/hbm.460030304

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractA modality‐independent approch for interactive spatial normalization of tomographic images of the human brain is described and its performance evaluated. Spatial normalization is accomplished using a nine‐parameter affine transformation to interactively align and adjust the shape of a subject brain to the reference brain detailed in the 1988 atlas of Talairach et al. A user‐friendly software application was developed using the X‐windows Motif environment to guide the user through this process. This software supports data types from a wide variety of tomographic imagers and produces output in spatially concise formats.The parameters used for spatial alignment and shape normalization are presented and methods to apply them discussed. Where normalization parameters cannot be obtained directly from the image, as with positron emission tomography (PET), methods for estimating them are given. Evaluation of a new four‐landmark method to fit the AC‐PC line in 16 magnetic resonance imaging (MRI) studies indicated an average difference assessed as the distance between the true and fitted AC‐PC line at four locations of 0.82 mm when using a 2‐D weighted fit. The same landmarks were evaluated using lower spatial resolution PET‐like images simulated from the 16 MRI studies. The difference between the PET and MR image volumes following alignment was minimal, with mean rotational differences of less than 0.2 deg and mean translational differences of generally less than 2 mm. Spatial normalization is illustrated for single photon emission computed tomography (SPECT), X‐ray computed tomography (CT), PET, and MR image volumes. Modality‐independent spatial normalization can be consistently and reliably performed with the methods and software presented.

ISSN:1065-9471    

DOI:10.1002/hbm.460030305

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractSubject motion present during the time course of functional activation studies is a pervasive problem in mapping the spatial and temporal characteristics of brain activity. In functional MRI (fMRI) studies, the observed signal changes are small. Therefore, it is crucial to reduce the effect of subject motion during the acquisition of image data in order to differentiate true brain activation from artifactual signal changes due to subject motion. We have adapted a technique for automatic motion detection and correction which is based on the ratio‐variance minimization algorithm to the fMRI subject motion problem. This method was used for retrospective correction of subject motion in the acquired data and resulted in improved functional maps. In this paper we have designed and applied a series of tests to evaluate the performance of this technique which span the classes of image characteristics common to fMRI. These areas include tests of the accuracy and range of motion as well as measurement of the effect of image signal to noise ratio, focal activation, image resolution, and image coverage on the motion detection system. Also, we have evaluated the amount of residual motion remaining after motion correction, and the ability of this technique to reduce the motion‐induced artifacts and restore regions of activation lost due to subject motion. In summary, this method performed well in the range of image characteristics common for fMRI experiments, reducing motion to under 0.5 mm, and removed significant motion‐induced artifacts while restoring true regions of activation. Motion correction is expected to become a routine requirement in the analysis of fMRI experiments. © 1995 Wiley‐L

ISSN:1065-9471    

DOI:10.1002/hbm.460030306

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractCortical activation during tactile exploration of macrogeometric objects was investigated in six healthy individuals with the use of magnetic resonance imaging (MRI) sensitized to changes in cerebral blood oxygenation. Dynamic measurement of task‐related signal alterations were performed at 2.0 T using a rapid gradient‐echo MRI sequence (TR/TE=63/30 ms, flip angle 10°, measuring time 6 s) at high spatial resolution (0.8 × 1.6 mm2). Four contiguous sections (thickness 4 mm) parallel to the bicommissural plane covered the hand area of the primary sensorimotor cortex (M1, S1), the supplementary motor area (SMA), premotor areas (PMA), and superior parts of the parietal cortex (PC). Task‐related activation was determined by correlating signal intensity time courses with the stimulus protocol on a pixel‐by‐pixel basis. In contrast to predominantly contralateral M1 activation, effects in the hand area of S1 were not restricted to the contralateral side but were equally present in the posterior section of ispilateral S1. Furthermore, bilateral responses were encountered in SMA and PC, while observations within PMA remained inconsistent. These findings in single subjects readily demonstrate a highly resolved and interindividually reproducible pattern of cortical activation in relation to exploratory finger movements and associated intergration of somatosensory information. © 1995 Wil

ISSN:1065-9471    

DOI:10.1002/hbm.460030307

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

摘要:

AbstractElectric stimulation of the median nerve at the wrist evokes a series of electric potentials that can be recorded from the scalp or directly from the cortex. These somatosensory evoked potentials (SEP) include a parietal negativity with a maximum 20 ms after the stimulus, which originates in the somatosensory cortex, probably area 3b (Allison et al. [1991a], Brain 114:2465–2503 and Desmedt et al. [1987], Electroenceph Clin Neurophysiol 68:1–19). Thirty milliseconds after the stimulus, a negative potential (N30) occurs at frontal recording sites. Recently it was observed that the amplitude of this potential is altered in patients with dystonia, Parkinson's disease, and Huntington's chorea. It has been argued that the N30 potential stems from cortical areas other than the somatosensory cortex, for example, the supplementary motor area. We used multichannel recordings to investigate the scalp distribution of the N20 and the N30 potentials in healthy subjects. We found that the N20 as well as the N30 potentials were accompanied by a corresponding positivity at frontal and parietal recording sites, respectively. The N20/P20 and the N30/P30 potential fields had a mirrorlike appearance, and both showed a polarity reversal near the central sulcus. This and the results of correlation analyses led us to the conclusion that the N30 generator is located near the central sulcus. © 1995 Wiley‐Lis

ISSN:1065-9471    

DOI:10.1002/hbm.460030308

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

ISSN:1065-9471    

DOI:10.1002/hbm.460030309

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY

ISSN:1065-9471    

DOI:10.1002/hbm.460030301

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1995

数据来源: WILEY