Magnetic Resonance Images Of Human Brain Research Articles

Talairach‐Based Parcellation of Neonatal Brain Magnetic Resonance Imaging Data: Validation of a New Approach

ABSTRACTBackground and Purpose. Talairach‐based parcellation (TP) of human brain magnetic resonance imaging (MRI) data has been used increasingly in clinical research to make regional measurements of brain structures in vivo. Recently, TP has been applied to pediatric research to elucidate the changes in regional brain volumes related to several neurological disorders. However, all freely available tools have been designed to parcellate adult brain MRI data. Parcellation of neonatal MRI data is very challenging owing to the lack of strong signal contrast, variability in signal intensity within tissues, and the small size and thus difficulty in identifying small structures used as landmarks for TP. Hence the authors designed and validated a new interactive tool to parcellate brain MRI data from newborns and young infants. Methods. The authors' tool was developed as part of a postprocessing pipeline, which includes registration of multichannel MR images, segmentation, and parcellation of the segmented data. The tool employs user‐friendly interactive software to visualize and assign the anatomic landmarks required for parcellation, after which the planes and parcels are generated automatically by the algorithm. The authors then performed 3 sets of validation experiments to test the precision and reliability of their tool. Results. Validation experiments of intra‐and interrater reliability on data obtained from newborn and 1‐year‐old children showed a very high sensitivity of >95% and specificity >99.9%. The authors also showed that rotating and reformatting the original MRI data results in a statistically significant difference in parcel volumes, demonstrating the importance of using a tool such as theirs that does not require realignment of the data prior to parcellation. Conclusions. To the authors' knowledge, the presented approach is the first TP method that has been developed and validated specifically for neonatal brain MRI data. Their approach would also be valuable for the analysis of brain MRI data from older children and adults.

A method for an accurate T1 relaxation-time measurement compensating B1 field inhomogeneity in magnetic-resonance imaging

Two-point gradient echo methods are practical means for estimating T1 value in magnetic-resonance (MR) imaging. Meanwhile, the inhomogeneity of the transmitted B1 field and the imperfect slice profile severely distorts the estimated T1 value. We propose a method for an accurate T1 measurement compensating the inhomogeneity of the B1 field and the effects of the imperfect slice profile. Three MR images of a whole human brain were obtained by multisliced spoiled gradient echo sequences on a 1.5-T scanner with different repetition times and flip angles to calculate the quantitative T1 maps. The histograms of the T1 maps showed two distinguishable peaks of the white matter (672.9±15.5ms) and the gray matter (921.1±24.7ms) of the human brain. The results indicate that the T1 value was properly estimated compensating the B1 field inhomogeneity and the slice profile imperfection. The proposed method is applicable to images obtained on any scanners with different parameters, because the method needs neither the information of the flip angle calibrations nor the phantom data to compensate the B1 field inhomogeneity.

Fast and robust registration of PET and MR images of human brain

In recent years, mutual information has proved to be an excellent criterion for registration of intra-individual images from different modalities. Multi-resolution coarse-to-fine optimization was proposed for speeding-up of the registration process. The aim of our work was to further improve registration speed without compromising robustness or accuracy. We present and evaluate two procedures for co-registration of positron emission tomography (PET) and magnetic resonance (MR) images of human brain that combine a multi-resolution approach with an automatic segmentation of input image volumes into areas of interest and background. We show that an acceleration factor of 10 can be achieved for clinical data and that a suitable preprocessing can improve robustness of registration. Emphasis was laid on creation of an automatic registration system that could be used routinely in a clinical environment. For this purpose, an easy-to-use graphical user interface has been developed. It allows physicians with no special knowledge of the registration algorithm to perform a fast and reliable alignment of images. Registration progress is presented on the fly on a fusion of images and enables visual checking during a registration.

Histogram analysis of the human brain MR images based on the S-function membership and Shannon's entropy function

The analysis of medical images for the purpose of computer-aided diagnosis and therapy planning includes segmentation as a preliminary stage for the visualization or quantification. In this paper, we present the first step in our fuzzy segmentation system that is capable of segmenting magnetic resonance (MR) images of a human brain. The histogram analysis based on the S-function membership and the Shannon's entropy function provides finding exact segmentation points. In the final stage, pixel classification is performed using the rule-based fuzzy logic inference. When the segmentation is complete, attributes of these classes may be determined (e.g., volumes), or the classes may be visualized as spatial objects. In contrast to other segmentation methods, like thresholding and region-based algorithms, our methods proceeds automatically and allow more exact delineation of the anatomical structures.

Automated segmentation of human brain MR images aided by fuzzy information granulation and fuzzy inference

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.

Short TE hydrogen-1 spectroscopic MR imaging of normal human brain: reproducibility studies.

Studies have been performed to evaluate the reproducibility of longitudinal acquisitions with short TE spectroscopic magnetic resonance (MR) imaging of human brain. In healthy volunteers, the ratios of N-acetyl-aspartate (NAA) and choline (Cho) with respect to total creatine (Cr) (creatine and phosphocreatine) have been examined in terms of voxel-to-voxel, acquisition-to-acquisition, and subject-to-subject variation. Overall coefficients of variation for NAA/Cr ratios and Cho/Cr ratios were 18% and 16%, respectively. The interacquisition variation was not statistically significant (P = .05) for either ratio. In terms of F ratios, the dominant source of variation in the NAA/Cr ratio was intersubject variability, whereas for the Cho/Cr ratio the dominant source of variation was intervoxel variability. Consequences of spectroscopic processing with spatial apodization and postacquisition water suppression and resolution enhancement were also examined. Such reproducibility studies are crucial if longitudinal spectroscopic evaluations are to be performed in a clinical setting to follow the natural course of disease states or the effects of therapy.

Knowledge-based classification and tissue labeling of MR images of human brain

Presents a knowledge-based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of the human brain. The system consists of 2 components: an unsupervised clustering algorithm and an expert system. MR brain data is initially segmented by the unsupervised algorithm, then the expert system locates a landmark tissue or cluster and analyzes it by matching it with a model or searching in it for an expected feature. The landmark tissue location and its analysis are repeated until a tumor is found or all tissues are labeled. The knowledge base contains information on cluster distribution in feature space and tissue models. Since tissue shapes are irregular, their models and matching are specially designed: 1) qualitative tissue models are defined for brain tissues such as white matter; 2) default reasoning is used to match a model with an MR image; that is, if there is no mismatch between a model and an image, they are taken as matched. The system has been tested with 53 slices of MR images acquired at different times by 2 different scanners. It accurately identifies abnormal slices and provides a partial labeling of the tissues. It provides an accurate complete labeling of all normal tissues in the absence of large amounts of data nonuniformity, as verified by radiologists. Thus the system can be used to provide automatic screening of slices for abnormality. It also provides a first step toward the complete description of abnormal images for use in automatic tumor volume determination.

Automatic detection of white matter lesions in magnetic resonance brain images

A new approach to automating radiologic diagnosis is described and tested in a system that locates multiple sclerosis lesions in magnetic resonance human brain images. This approach uses a step-by-step procedure, where the most obvious features in the images are identified first, and used to calibrate the application of the next step, until the desired features are identified. The approach stresses testing on a large number of images of the same type, and exploiting the special characteristics of this type of images. The system was designed with the variations of the various image parameters in mind, to ensure its reliability. New low-level methods of high reliability have been developed for segmenting images. A new geometric modeling method which tolerates the variability of biological structures is used to encode anatomic knowledge. The system and its components have been tested on 1132 images from 17 patients, with very good results.