The Magnetic brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to way changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the changes basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across thereby time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and,more consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years.leading On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more images detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project.in On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable,MR thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it which changes over a lifetime and in the course of evolution.

This each paper presents a new energy minimization method for simultaneous tissue classification and bias field estimation of magnetic resonance (MR) images.ensured We first derive an important characteristic of local image intensities--the intensities of different tissues within a neighborhood form separable clusters,defines and the center of each cluster can be well approximated by the product of the bias within the neighborhood and proposed a tissue-dependent constant. We then introduce a coherent local intensity clustering (CLIC) criterion function as a metric to evaluate tissue bias classification and bias field estimation. An integration of this metric defines an energy on a bias field, membership functions of images. the tissues, and the parameters that approximate the true signal from the corresponding tissues. Thus, tissue classification and bias field Thus, estimation are simultaneously achieved by minimizing this energy. The smoothness of the derived optimal bias field is ensured by the MR spatially coherent nature of the CLIC criterion function. As a result, no extra effort is needed to smooth the bias tissues. field in our method. Moreover, the proposed algorithm is robust to the choice of initial conditions, thereby allowing fully automatic metric applications. Our algorithm has been applied to high field and ultra high field MR images with promising results.

Smoothly N3 varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately,mm these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled et al., 1998) is be one of the most frequently used. However, at least one recent study (Boyes et al., 2008) suggests that its performance FreeSurfer on 3 T scanners with multichannel phased-array receiver coils can be improved by optimizing a parameter that controls the smoothness this of the estimated bias field. The present study not only confirms this finding, but additionally demonstrates the benefit of reducing accuracy the relevant parameter values to 30-50 mm (default value is 200 mm), on white matter surface estimation as well as not the measurement of cortical and subcortical structures using FreeSurfer (Martinos Imaging Centre, Boston, MA). This finding can help enhance precision critical in studies where estimation of cerebral cortex thickness is critical for making inferences.

PURPOSE:varied To investigate inconsistencies between common performance measures for bias field correction reported in several recent studies and propose a solution.and MATERIALS AND METHODS: A set of synthetic images of a normal brain from the Montréal Simulated Brain Database (SBD) was joint processed using two bias field correction algorithms. The parameters of these algorithms were varied and the resulting outputs were assessed these using several performance measures. Validity was estimated using Spearman rank correlation coefficient between "indirect" performance measures and the L2 norm performance of the difference between true and estimated bias fields. The "indirect" performance measures tested were: coefficients of variation of white METHODS: matter (WM) and gray matter (GM), coefficient of joint variation. These measures were tested on bias field-corrected images that were light permuted in terms of quality of WM/GM segmentation as well as the presence or absence of light smoothing. RESULTS: Existing Imaging indirect performance measures yielded poor validity scores, explaining the inconsistencies reported in the literature. Image noise and inappropriate inclusion of absence partial volume voxels and neighboring tissues were found to be contributory. Combining conservative segmentation and smoothing significantly improved validity. CONCLUSION:coefficient The use of indirect performance measures in the conventional manner to guide bias field correction is unreliable. Using these metrics a on lightly smoothed images with conservatively segmented tissues proved more reliable for guiding the selecting of parameters for nonuniformity correction measures ultimately contributing to more accurate brain segmentation. J. Magn. Reson. Imaging 2009;29:1271-1279.(c) 2009 Wiley-Liss, Inc.

Intensity from inhomogeneities in magnetic resonance images (MRI) are a frequently occurring artefact, and result in the same tissue class to have calibration vastly different intensities within an image. These inhomogeneities can be modelled by a slowly varying field, which is also called gradient the bias field. Previous phantom-, image- or sequence based approaches suffer from long scan times, post-processing times or do not as sufficiently remove the intensity variations. These intensity variations cause problems for quantitative image analysis algorithms (segmentation, registration) as well as during clinicians (e.g. by complicating the visual assessment). This paper presents a novel technique (COIN, correction of intensity inhomogeneities) that uses intensities two calibration images (fast spoiled gradient echo) to map a parameter containing the bias field, which is specific to the correct patient during a particular exam. This parametric map can then be used to correct any other images acquired during the shows same exam, regardless of the sequence employed. By using a short repetition time (less than 5 ms) for the calibration used scans, the additional scan time is reduced to 60 s (max). The subsequent post-processing time is approximately 60 s per spoiled 20 slices. We successfully validate our approach on simulated brain MRI as well as real liver and spinal images. These to images were acquired with a number of different coils, sequences and weightings. A comparison of our method with an existing,map commercially available algorithm by radiologists shows that COIN is superior.

Multiple has sclerosis is known to be an inflammatory demyelinating disease characterized mainly by multifocal areas of white matter lesions. Recently, cortical and lesions and brain atrophy have emerged as new pathological markers of disease progression. Brain tissue loss can now be easily atrophy, and reproducibly detected and quantified by MRI, which has led to an increasing amount of research correlating brain tissue loss cortical with other MRI markers, such as white matter lesions, and to clinical disabilities (motor or cognitive), in order to assess of its clinical relevance. In this review, we summarize the different MRI-based methods used to quantify whole and regional brain atrophy,lesions and discuss the relevance of brain atrophy in MS through its relationship with characteristic brain lesions, disability and cognition. Lastly,brain we emphasize the role of brain atrophy in different phenotypes of MS. According to this review of the current literature,different the grey matter (GM) atrophy is now well-established in MS and has been found to be strongly associated with clinical role and cognitive deterioration. This GM volume loss is characterized by a focal atrophy in the deep grey nuclei but also brain by diffuse cortical atrophy. Phenotypic variation can be associated with different degrees and regional location of brain atrophy but more lesions. investigations are necessary to specify the discrepancies against brain atrophy between the different forms of the disease.

Basal sufficient ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted images. (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We are use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to MT their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar automated and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects.various Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a We focus on subcortical structures.

PURPOSE:acquired To describe and evaluate a computer-assisted method for assessing the quantity and distribution of adipose tissue in thigh by magnetic 10% resonance imaging (MRI). MATERIALS AND METHODS: Twenty obese subjects were imaged on a Philips Achieva 1.5T scanner by a fast an spin-echo (FSE) sequence. A total of 636 images were acquired and analyzed by custom-made software. Thigh subcutaneous adipose tissue (SAT)work and bone were identified by fuzzy clustering segmentation and an active contour algorithm. Muscle and intermuscular adipose tissue (IMAT) were CV assessed by identifying the two peaks of the signal histogram with an expectation maximization algorithm. The whole analysis was performed imaging in an unsupervised manner without the need of any user interaction. RESULTS: The coefficient of variation (CV) was evaluated between by the unsupervised algorithm and manual analysis performed by an expert operator. The CV was low for all measurements (SAT <2%,2009;29:677-684. muscle <1%, IMAT <5%). Limited manual correction of unsupervised segmentation results (less than 10% of contours modified) allowed us to performed further reduce the CV (SAT < .5%, muscle < .5%, IMAT <2%). CONCLUSION: The proposed approach allowed effective computer-assisted analysis of thigh in MR images, dramatically reducing the user work compared to manual analysis. It allowed routine assessment of IMAT, a fat-depot linked thigh with metabolic abnormalities, important in monitoring the effect of nutrition and exercise. J. Magn. Reson. Imaging 2009;29:677-684.(c) 2009 Wiley-Liss,without Inc.

This due study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures Brain expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As image MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue Empirical intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation method caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects mixtures on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter)matter) resulting from the method were validated and evaluated against manual segmentation results based on thirty-eight real T1-weighted image volumes from using the Internet Brain Segmentation Repository, and eighteen simulated image volumes from BrainWeb. The Jaccard and Dice similarity indices were computed and to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation to method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional extends segmentation using the Gaussian mixtures approach.

A uses major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its not intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so obtained, as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the were properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms.functions In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is contribution performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained,is which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series clinical to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets standardization only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not transformation designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI intensities head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used.a For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole MRI body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies.

We are propose a novel fully automated method for retrospective correction of intensity inhomogeneity, which is an undesired phenomenon in many automatic inhomogeneity image analysis tasks, especially if quantitative analysis is the final goal. Besides most commonly used intensity features, additional spatial image image features are incorporated to improve inhomogeneity correction and to make it more dynamic, so that local intensity variations can be and corrected more efficiently. The proposed method is a four-step iterative procedure in which a non-parametric inhomogeneity correction is conducted. First,the the probability distribution of image intensities and corresponding second derivatives is obtained. Second, intensity correction forces, condensing the probability distribution in along the intensity feature, are computed for each voxel. Third, the inhomogeneity correction field is estimated by regularization of all for voxel forces, and fourth, the corresponding partial inhomogeneity correction is performed. The degree of inhomogeneity correction dynamics is determined by these the size of regularization kernel. The method was qualitatively and quantitatively evaluated on simulated and real MR brain images. The computed obtained results show that the proposed method does not corrupt inhomogeneity-free images and successfully corrects intensity inhomogeneity artefacts even if of these are more dynamic.

In from the past, a number of methods were proposed for quantitative assessment of vertebral rotation from three-dimensional (3D) images. However, these vertebra, methods were based on manual identification of distinctive anatomical landmarks, required manual determination of cross-sections from 3D images, and measured planes only axial vertebral rotation instead of the rotation in 3D. In this paper, we propose an automated method for quantitative show assessment of vertebral rotation in 3D that is based on finding the planes of vertebral symmetry by matching image intensity normal gradients on both sides of each plane. The method was evaluated on 28 images of normal and pathological vertebrae, obtained three-dimensional by computed tomography (CT) and magnetic resonance (MR). For each vertebra, final angle displacements of 200 initial angle displacements, uniformly on distributed within 30 degrees from manually obtained reference angles, were obtained. The results show that by the proposed method, vertebral precision rotation can be successfully estimated in 3D with an average accuracy of 1. degrees and precision of .5 degrees.

In a this paper, a novel method for EEG to MRI registration is proposed. Initial registration is achieved by extracting and matching the symmetry planes of MRI and EEG data, followed by iterative registration based on minimizing a cost function. Comparison of the which intensity distributions of the whole MR image and MRI voxels around a head surface point yields global similarities, while the mean comparison of intensity distributions of MRI voxels around corresponding EEG points, which reflects the head's sagittal symmetry, yields local similarities.and Therefore, when the EEG points are registered to the MR image, maximal global and local similarities should be obtained. The is cost function, incorporating global and local similarities, was the sum of Kullback-Leibler divergences between corresponding intensity distributions. The proposed method image, was evaluated on clinical MRI data with simulated EEG data, yielding mean registration error of .48 +/- .33 mm, while .02 with real EEG data an average root-mean-square point-to-surface error of 2.27 +/- .02 mm was obtained.

The tomography purpose of this study is to present a framework for quantitative analysis of spinal curvature in 3D. In order to the study the properties of such complex 3D structures, we propose two descriptors that capture the characteristics of spinal curvature in respectively. 3D. The descriptors are the geometric curvature (GC) and curvature angle (CA), which are independent of the orientation and size to of spine anatomy. We demonstrate the two descriptors that characterize the spinal curvature in 3D on 30 computed tomography (CT)kyphosis images of normal spine and on a scoliotic spine. The descriptors are determined from 3D vertebral body lines, which are the obtained by two different methods. The first method is based on the least-squares technique that approximates the manually identified vertebra vertebral centroids, while the second method searches for vertebra centroids in an automated optimization scheme, based on computer-assisted image analysis. Polynomial in functions of the fourth and fifth degree were used for the description of normal and scoliotic spinal curvature in 3D,spine respectively. The mean distance to vertebra centroids was 1.1 mm (+/- .6 mm) for the first and 2.1 mm (+/-1.4 mm)in for the second method. The distributions of GC and CA values were obtained along the 30 images of normal spine structures, at each vertebral level and show that maximal thoracic kyphosis (TK), thoracolumbar junction (TJ) and maximal lumbar lordosis (LL) on vertebra average occur at T3/T4, T12/L1 and L4/L5, respectively. The main advantage of GC and CA is that the measurements are were independent of the orientation and size of the spine, thus allowing objective intra- and inter-subject comparisons. The positions of maximal two TK, TJ and maximal LL can be easily identified by observing the GC and CA distributions at different vertebral levels.vertebral The obtained courses of the GC and CA for the scoliotic spine were compared to the distributions of GC and (TJ) CA for the normal spines. The significant difference in values indicates that the descriptors of GC and CA may be computer-assisted used to detect and quantify scoliotic spinal curvatures. The proposed framework may therefore improve the understanding of spine anatomy and distributions aid in the clinical quantitative evaluation of spinal deformities.

An is important part of image-guided radiation therapy or surgery is registration of a three-dimensional (3D) preoperative image to two-dimensional (2D) images images of the patient. It is expected that the accuracy and robustness of a 3D/2D image registration method do not depend the solely on the registration method itself but also on the number and projections (views) of intraoperative images. In this study,indicate we systematically investigate these factors by using registered image data, comprising of CT and X-ray images of a cadaveric lumbar by spine phantom and the recently proposed 3D/2D registration method. The results indicate that the proportion of successful registrations (robustness) significantly of increases when more X-ray images are used for registration.

Image and registrations that are based on similarity measures simply adjust the parameters of an appropriate spatial transformation model until the similarity most measure reaches an optimum. The numerous similarity measures that have been proposed in the past are differently sensitive to imaging have modality, image content and differences in the image content, selection of the floating and target image, partial image overlap, etc.target In this paper, we evaluate and compare 12 similarity measures for the rigid registration. To study the impact of different rectified imaging modalities on the behavior of similarity measures, we have used 16 CT/MR and 6 PET/MR image pairs with known the 'gold standard' registrations. The results for the PET/MR registration and for the registration of CT to both rectified and unrectified of MR images indicate that mutual information, normalized mutual information and the entropy correlation coefficient are the most accurate similarity measures and and have the smallest risk of being trapped in a local optimum. The results of an experiment on the impact the of exchanging the floating and target image indicate that, especially in MR/PET registrations, the behavior of some similarity measures, such we as mutual information, significantly depends on which image is the floating and which is the target.

A be novel method for automated curved planar reformation (CPR) of magnetic resonance (MR) images of the spine is presented. The CPR was images, generated by a transformation from image-based to spine-based coordinate system, follow the structural shape of the spine and allow column. the whole course of the curved anatomy to be viewed in individual cross-sections. The three-dimensional (3D) spine curve and the the axial vertebral rotation, which determine the transformation, are described by polynomial functions. The 3D spine curve passes through the centres on of vertebral bodies, while the axial vertebral rotation determines the rotation of vertebrae around the axis of the spinal column.images, The optimal polynomial parameters are obtained by a robust refinement of the initial estimates of the centres of vertebral bodies The and axial vertebral rotation. The optimization framework is based on the automatic image analysis of MR spine images that exploits geometrical some basic anatomical properties of the spine. The method was evaluated on 21 MR images from 12 patients and the rotation. results provided a good description of spine anatomy, with mean errors of 2.5 mm and 1.7 degrees for the position the of the 3D spine curve and axial rotation of vertebrae, respectively. The generated CPR images are independent of the position is of the patient in the scanner while comprising both anatomical and geometrical properties of the spine.

We coordinate present a novel method for curved planar reformation (CPR) of spine images obtained by magnetic resonance (MR) imaging. CPR images,vertebral created via a transformation from image-based to spine-based coordinate system, follow the structural shape of the spine and allow the on whole course of the curved structure to be viewed in a single image. The spine-based coordinate system is defined on an the 3D spine curve and on the axial vertebral rotation, both described by polynomial models. The 3D spine curve passes spine through the centers of vertebral bodies, and the axial vertebral rotation determines the rotation of vertebral spinous processes around the obtained spine. The optimal polynomial parameters are found in an optimization framework, based on image analysis. The method was evaluated on models. 19 MR images of the spine from 10 patients.

Traditional because techniques for analyzing tortuous anatomical structures (e.g. arteries, colon, spine) in the coordinate system of the 3D image generally do the not provide sufficient or qualitative enough diagnostic information, because planar cross-sections do not follow curved paths along the structures. To from overcome this shortcoming, images in the coordinate system of the structure must be created. We propose a transformation from standard polynomial image-based to a novel spine-based coordinate system. The origin and axes of the proposed spine-based coordinate system are determined on system the curve that represents the vertebral column, and the rotation of the vertebrae around the spine curve, both of which the are described by polynomial models. The optimal polynomial parameters are obtained in an image analysis based optimization framework. The method proposed has been evaluated on five CT spine images.

The such accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and a image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex we interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is selecting often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost show function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of for a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity and measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a on protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture the range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show this that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography the and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images,for for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior useful of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method difficult, for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap,is and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.

A acquisition number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative The analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and classifiers inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented. Results demonstrate that the that correction process enhances the performance of backpropagation neural network classifiers designed for the segmentation of the images. Two slightly different intensity versions of the method are presented. The first version fits an intensity correction surface directly to reference points selected by for the user in the images. The second version fits the surface to reference points obtained by an intermediate classification operation.first Qualitative and quantitative evaluation of both methods reveals that the first one leads to a better correction of the images sensitive than the second but that it is more sensitive to operator errors.

The The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data is sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI the is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step method was developed to model corruption and then reduce the effect. Using a least squares formulation, the inhomogeneity is modeled as a maximum variation order two the polynomial. In the log domain the polynomial model is subtracted from the actual patient data set resulting in a compensated magnetic data set. The compensated data set is exponentiated and rescaled. Statistical comparisons indicate volumes of significant corruption undergo a large is reduction in the inhomogeneity, whereas volumes of minimal corruption are not significantly changed. Acting as a preprocessor, the proposed technique body can enhance the role of statistical segmentation algorithms in body MRI data sets.

The the aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of various the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic Resonance Imaging existing (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing brain image analysis to methods have often preferred the intensity information to others, such as texture. Recently, an easy-to-compute texture descriptor, Local Binary Pattern easy-to-compute (LBP), has shown promise in various applications outside the medical field. In this paper, after extensive experiments, we show that diseases rotation-invariant LBP is invariant to some common MRI artifacts that makes it possible to use it in various high-level brain texture. MR image analysis applications.

There various are various methods proposed for the segmentation and analysis of MR images. However the efficiency of these techniques is effected Inter by various artifacts that occur in the imaging system. One of the most encountered problems is the intensity variation across problem an image. To overcome this problem different methods are used. In this paper we propose a method for the elimination patient. of intensity artifacts in segmentation of MRI images. Inter imager variations are also minimized to produce the same tissue segmentation elimination for the same patient. A well-known multivariate classification algorithm, maximum likelihood is employed to illustrate the enhancement in segmentation.

Chemical acute exchange saturation transfer (CEST) imaging provides an indirect detection mechanism that allows quantification of certain labile groups unobservable using conventional of MRI. Recently, amide proton transfer (APT) imaging, a variant form of CEST imaging, has been shown capable of detecting lactic on acidosis during acute ischemia, providing information complementary to that of perfusion and diffusion MRI. However, CEST contrast is usually small,with and therefore, it is important to optimize experimental conditions for reliable and quantitative CEST imaging. In particular, CEST imaging is compensate sensitive to B( ) and B(1) field, while on the other hand; field inhomogeneities persist despite recent advances in magnet technologies,using especially for in vivo imaging at high fields. Consequently, correction algorithms that can compensate for field inhomogeneity-induced measurement errors in correction CEST imaging might be very useful. In this study, the dependence of CEST contrast on field distribution was solved and 2007. a correction algorithm was developed to compensate for field inhomogeneity-induced CEST imaging artifacts. In addition, the proposed algorithm was verified Consequently, with both numerical simulation and experimental measurements, and showed nearly complete correction of CEST imaging measurement errors caused by moderate field, field inhomogeneity. Magn Reson Med, 2007.(c) 2007 Wiley-Liss, Inc.

Magnetic correcting resonance images are commonly affected by intensity inhomogeneities which make it difficult to obtain any quantitative measures from them. We real present a new method of automatically correcting this artifact using a nonparametric coarse to fine approach which allows bias fields entropy-related to be modeled with different frequency ranges without user supervision. We also propose a new entropy-related cost function based on proposed the combination of intensity and gradient image features for more robust homogeneity measurement. The proposed methodology has been evaluated for measurement. both synthetic and real data and compared with state of the art methods, showing the best results in the comparison.difficult The proposed method is fully automatic and has no input parameters, making it very easy to use in a clinical more environment.

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

Magnetic when resonance (MR) images can be acquired by multiple receiver coil systems to improve signal-to-noise ratio (SNR) and to decrease acquisition might time. The optimal SNR images can be reconstructed from the coil data when the coil sensitivities are known. In typical the MR imaging studies, the information about coil sensitivity profiles is not available. In such cases the sum-of-squares (SoS) reconstruction algorithm iterative is usually applied. The intensity of the SoS reconstructed image is modulated by a spatially variable function due to the SoS non-uniformity of coil sensitivities. Additionally, the SoS images also have sub-optimal SNR and bias in image intensity. All these effects signal-to-noise might introduce errors when quantitative analysis and/or tissue segmentation are performed on the SoS reconstructed images. In this paper, we sensitivities. present an iterative algorithm for coil sensitivity estimation and demonstrate its applicability for optimal SNR reconstruction and intensity inhomogeneity correction phased in phased array MR imaging.

Medical modality image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment,60 especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity been inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous applications. methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified inhomogeneity according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are facilitate categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key according evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed.