Real Magnetic Resonance Imaging Research Articles

Cardiac Motion Estimation in Magnetic Resonance Images Using Optical Flow

This paper reports an optical based method for quantification of cardiac motion in MRI images. The cardiac motion is quantified in a Short-Axis (SAX) slice located at the mid-cavity of the left ventricle. In this slice, the left ventricle wall is segmented for extracting the endocardium, the epicardium and the mid‑wall. The velocity field for these contours is available after performing the optical flow estimation for the given slice. Post-processing of this motion field enables estimation of the radial displacement. The core of this cardiac motion quantification method is a sparse based algorithm for optical flow estimation. This algorithm is presented and compared with several classical optical flow estimation algorithms. The comparison is performed using several video sequences available from the Middlebury benchmark where the ground truth optical flow is known. The algorithms with better performance are also tested using a 4‑ D cardiac Magnetic Resonance Image (MRI) sequence. Results show that he sparse algorithm estimates an optical flow field that represents the motion of contraction during the systole interval. Moreover, results about radial displacement estimation on real MRI sequences for a normal subject and a patient with hypertrophy show that the proposed quantification method could be useful for quantification of left ventricle motion.

3D cerebral MR image segmentation using multiple-classifier system.

The three soft brain tissues white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) identified in a magnetic resonance (MR) image via image segmentation techniques can aid in structural and functional brain analysis, brain's anatomical structures measurement and visualization, neurodegenerative disorders diagnosis, and surgical planning and image-guided interventions, but only if obtained segmentation results are correct. This paper presents a multiple-classifier-based system for automatic brain tissue segmentation from cerebral MR images. The developed system categorizes each voxel of a given MR image as GM, WM, and CSF. The algorithm consists of preprocessing, feature extraction, and supervised classification steps. In the first step, intensity non-uniformity in a given MR image is corrected and then non-brain tissues such as skull, eyeballs, and skin are removed from the image. For each voxel, statistical features and non-statistical features were computed and used a feature vector representing the voxel. Three multilayer perceptron (MLP) neural networks trained using three different datasets were used as the base classifiers of the multiple-classifier system. The output of the base classifiers was fused using majority voting scheme. Evaluation of the proposed system was performed using Brainweb simulated MR images with different noise and intensity non-uniformity and internet brain segmentation repository (IBSR) real MR images. The quantitative assessment of the proposed method using Dice, Jaccard, and conformity coefficient metrics demonstrates improvement (around 5% for CSF) in terms of accuracy as compared to single MLP classifier and the existing methods and tools such FSL-FAST and SPM. As accurately segmenting a MR image is of paramount importance for successfully promoting the clinical application of MR image segmentation techniques, the improvement obtained by using multiple-classifier-based system is encouraging.

Information theoretic optimal vocal tract region selection from real time magnetic resonance images for broad phonetic class recognition

We propose an information theoretic region selection algorithm from the real time magnetic resonance imaging (rtMRI) video frames for a broad phonetic class recognition task. Representations derived from these optimal regions are used as the articulatory features for recognition. A set of connected and arbitrary shaped regions are selected such that the articulatory features computed from such regions provide maximal information about the broad phonetic classes. We also propose a tree-structured greedy region splitting algorithm to further segment these regions so that articulatory features from these split regions enhance the information about the phonetic classes. We find that some of the proposed articulatory features correlate well with the articulatory gestures from the Articulatory Phonology theory of speech production. Broad phonetic class recognition experiment using four rtMRI subjects reveals that the recognition accuracy with optimal split regions is, on average, higher than that using only acoustic features. Combining acoustic and articulatory features further reduces the error-rate by ∼8.25% (relative).

Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

A combined interpolation - 3D Level Set Method (I+3DLSM) based segmentation process is presented. The performance in terms of accuracy of the 3-dimensional (3D) level set method (LSM) in the segmentation of throat regions from highly anisotropic magnetic resonance imaging (MRI) volumes, with and without an interpolation step is evaluated. Qualitative and quantitative results from real MRI data suggest that performing interpolation, to reconstruct isotropic MRI volumes, prior to 3D LSM improves the accuracy of the segmentation results, compared to interpolation post 3D LSM and no interpolation at all.

MR image bias field harmonic approximation with histogram statistical analysis

This study investigates a method to correct the intensity inhomogeneity field of magnetic resonance image. The algorithm takes full advantage of the properties of the magnetic resonance image, namely, the piecewise character (piecewise constant and piecewise smooth) of true image which characterizes a physical property of the tissue anatomical structure and the smoothly varying property of bias field which accounts for the intensity inhomogeneity. An energy function was constructed by embedding this characters into the image model. We can get the estimation of bias field and the segmentation of tissue by minimizing the energy function. The initial parameter of energy function is calculated automatically by statistical analysis. By mixing the fitting basis function with cosine function and polynomial function, we can obtain an accurate approximation of the bias field. A comparative performance evaluation is carried out over a large set of experiments using synthetic magnetic resonance data. Besides, a set of tests on real prostate magnetic resonance image with severe intensity inhomogeneity field is shown to demonstrate the validity of our method.

Cardiac image segmentation by random walks with dynamic shape constraint

The quantitative analysis of the left ventricle (LV) contractile function is one of the key steps in the assessment of cardiovascular disease. Such analysis greatly depends on the accurate delineation of LV boundary from cardiac sequences. However, segmentation of the LV still remains a challenging problem due to its subtle boundary, occlusion, and image inhomogeneity. To overcome such difficulties, the authors propose a novel segmentation method by incorporating a dynamic shape constraint into the weighting function of the random walks segmentation algorithm. This approach involves iterative updates on the intermediate result to achieve the desired solution. The inclusion of a shape constraint restricts the solution space of the segmentation result to handle misleading information that may come from noise, weak boundaries and clutter, leading to increased robustness of the algorithm. The authors describe the details of the proposed method and demonstrate its effectiveness in segmenting the LV from real cardiac magnetic resonance (CMR) image sets. The experimental results demonstrate that the proposed method obtains better segmentation performance than the standard method.

Improving the MR Imaging Sensitivity of Upconversion Nanoparticles by an Internal and External Incorporation of the Gd3+ Strategy for in Vivo Tumor-Targeted Imaging

Gd(3+)-ion-doped upconversion nanoparticles (UCNPs), integrating the advantages of upconversion luminescence and magnetic resonance imaging (MRI) modalities, are capturing increasing attention because they are promising to improve the accuracy of diagnosis. The embedded Gd(3+) ions in UCNPs, however, have an indistinct MRI enhancement owing to the inefficient exchange of magnetic fields with the surrounding water protons. In this study, a novel approach is developed to improve the MR imaging sensitivity of Gd(3+)-ion-doped UCNPs. Bovine serum albumin (BSA) bundled with DTPA-Gd(3+) (DTPA(Gd)) is synthesized both as the MR imaging sensitivity synergist and phase-transfer ligand for the surface engineering of UCNPs. The external Gd(3+) ion attachment strategy is found to significant improve the MR imaging sensitivity of Gd(3+)-ion-doped UCNPs. The relaxivity analysis shows that UCNPs@BSA·DTPA(Gd) exhibit higher relaxivity values than do UCNPs@BSA without DTPA(Gd) moieties. Another relaxivity study discloses a striking message that the relaxivity value does not always reflect the realistic MRI enhancement capability. The high concentration of Gd(3+)-ion-containing UCNPs with further surface-engineered BSA·DTPA(Gd) (denoted as UCNPs-H@BSA·DTPA(Gd)) exhibits a more pronounced MRI enhancement capability compared to the other two counterparts [UCNPs-N@BSA·DTPA(Gd) and UCNPs-L@BSA·DTPA(Gd) (-N and -L are denoted as zero and low concentrations of Gd(3+) ion doping, respectively)], even though it holds the lowest r1 of 1.56 s(-1) per mmol L(-1) of Gd(3+). The physicochemical properties of UCNPs are essentially maintained after BSA·DTPA(Gd) surface decoration with good colloidal stability, in addition to improving the MR imaging sensitivity. In vivo T1-weighted MRI shows potent tumor-enhanced MRI with UCNPs-H@BSA·DTPA(Gd). An in vivo biodistribution study indicates that it is gradually excreted from the body via hepatobiliary and renal processing with no obvious toxicity. It could therefore be concluded, with improved MR imaging sensitivity by an internal and external incorporation of Gd(3+) strategy, that UCNPs-H@BSA·DTPA(Gd) presents great potential as an alternative in tumor-targeted MR imaging.

Computational Fluid Dynamics Analysis of the Aortic Coarctation

In this work, we first investigated the hemodynamic parameters in the case of a normal aortic arch anatomy and in the case of aortic coarctation anatomy, both generated by using CFX-ICEM-ANSYS simulations. Then, we compared these results with those obtained for a proposed model without and with aortic coarctation, while introducing a real tridimensional magnetic resonance imaging geometry in the simulation process. The conclusion is that our proposed model reproduces, with a high agreement, the real case obtained from imaging data.

Multicontrast MRI Reconstruction with Structure-Guided Total Variation

Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different contrasts depending on the acquisition parameters. Many clinical imaging studies acquire MRI data for more than one of these contrasts---such as for instance T1 and T2 weighted images---which makes the overall scanning procedure very time consuming. As all of these images show the same underlying anatomy one can try to omit unnecessary measurements by taking the similarity into account during reconstruction. We will discuss two modifications of total variation---based on i) location and ii) direction---that take structural a priori knowledge into account and reduce to total variation in the degenerate case when no structural knowledge is available. We solve the resulting convex minimization problem with the alternating direction method of multipliers that separates the forward operator from the prior. For both priors the corresponding proximal operator can be implemented as an extension of the fast gradient projection method on the dual problem for total variation. We tested the priors on six data sets that are based on phantoms and real MRI images. In all test cases exploiting the structural information from the other contrast yields better results than separate reconstruction with total variation in terms of standard metrics like peak signal-to-noise ratio and structural similarity index. Furthermore, we found that exploiting the two dimensional directional information results in images with well defined edges, superior to those reconstructed solely using a priori information about the edge location.

Reliability of dissimilarity measures for multi-voxel pattern analysis

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries.

An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images

Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model more flexible, which is crucial in presence of strong inhomogeneities. An efficient minimization procedure, attempting to find the global minimum, is applied to the energy functional. The algorithm is evaluated qualitatively and quantitatively using a synthetic example and real MR images of different organs. Comparisons with several state-of-the-art methods demonstrate the superior performance of the proposed technique. Desirable results are obtained even for images with strong and complicated inhomogeneity fields and sparse tissue structures.

Research on modeling approach of brain function network based on anatomical distance

The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging (fMRI) network. Finally, the result shows that the best model only is based on anatomical distance.

Real time dynamic MRI by exploiting spatial and temporal sparsity

Online imaging requires that the reconstruction of current frame only depends on the previous frames, and real time imaging is the desired case. In this work, we propose a novel scheme for real time dynamic magnetic resonance imaging (dMRI) reconstruction. Different from previous methods, the reconstructions of the second frame to the last frame are independent in our scheme, which only require the first frame as the reference image. Therefore, this scheme can be naturally implemented in parallel. After the first frame is reconstructed, all the later frames can be processed as soon as the k-space data are acquired. As an extension of the conventional spatial total variation, a new online model called dynamic total variation is used to exploit the sparsity on both spatial and temporal domains in dMRI. In real time dMRI, each frame is required to be reconstructed very fast. We then design a novel reweighted least squares algorithm to solve the challenging problem. Motivated by the special structure of partial Fourier transform in sparse MRI, this algorithm is accelerated by the preconditioned conjugate gradient descent method. The proposed method is compared with 4 state-of-the-art online and offline methods on two in-vivo cardiac dMRI datasets. The experimental results show that our method significantly outperforms previous online methods, and is comparable to the offline methods in terms of reconstruction accuracy.

Robust fuzzy clustering using nonsymmetric student׳s t finite mixture model for MR image segmentation

Accurate tissue segmentation from magnetic resonance (MR) images is an essential step in clinical practice. In this paper, we introduce a robust fuzzy clustering scheme for finite mixture model fitting, which exploits the merits of the mixture of the nonsymmetric Student׳s t-distribution and mean template to reduce the sensitivity of the segmentation results with respect to noise. This approach utilizes a fuzzy objective function regularized by the Kullback–Leibler (KL) divergence term and sets the dissimilarity function as the negative log-likelihood of the nonsymmetric Student׳s t-distribution and mean template. The advantage of this fuzzy clustering scheme is that the spatial relationships among neighbouring pixels are taken into account with the help of the mean template so that the proposed method is more robust to noise than several other existing fuzzy c-means (FCM)-based algorithms. Another advantage is that the application of the nonsymmetric Student׳s t-distribution mixture model allows the proposed model to fit different shapes of observed data. Experiments using synthetic and real MR images show that the proposed model has considerably better segmentation accuracy and robustness against noise compared with several well-known finite mixture models.

Possibilistic Intuitionistic Fuzzy c-Means Clustering Algorithm for MRI Brain Image Segmentation

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov’s intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.

SEGMENTATION OF BRAIN MRIs BY SUPPORT VECTOR MACHINE: DETECTION AND CHARACTERIZATION OF STROKES

The aim of our work is the segmentation of healthy and pathological brains to obtain brain structures and extract strokes. We used real magnetic resonance (MR) images weighted on diffusion. The brain was isolated, and the images were filtered by an anisotropic filter, and then segmented by support vector machines (SVMs). We first applied the method on synthetic images to test the performance of the algorithm and adjust the parameters. Then, we compared our results with those obtained by a cooperative approach proposed in a previous paper.

Stratified mixture modeling for segmentation of white-matter lesions in brain MR images

Accurate characterization of white-matter lesions from magnetic resonance (MR) images has increasing importance for diagnosis and management of treatment of certain neurological diseases, and can be performed in an objective and effective way by automated lesion segmentation. This usually involves modeling the whole-brain MR intensity distribution, however, capturing various sources of MR intensity variability and lesion heterogeneity results in highly complex whole-brain MR intensity models, thus their robust estimation on a large set of MR images presents a huge challenge. We propose a novel approach employing stratified mixture modeling, where the main premise is that the otherwise complex whole-brain model can be reduced to a tractable parametric form in small brain subregions. We show on MR images of multiple sclerosis (MS) patients with different lesion loads that robust estimators enable accurate mixture modeling of MR intensity in small brain subregions even in the presence of lesions. Recombination of the mixture models across strata provided an accurate whole-brain MR intensity model. Increasing the number of subregions and, thereby, the model complexity, consistently improved the accuracy of whole-brain MR intensity modeling and segmentation of normal structures. The proposed approach was incorporated into three unsupervised lesion segmentation methods and, compared to original and three other state-of-the-art methods, the proposed modeling approach significantly improved lesion segmentation according to increased Dice similarity indices and lower number of false positives on real MR images of 30 patients with MS.

A Robust Gradient-Based Algorithm to Correct Bias Fields of Brain MR Images

We developed a novel algorithm to estimate bias fields from brain magnetic resonance (MR) images using a gradient-based method. The bias field is modeled as a multiplicative and slowly varying surface. We fit the bias field by a low-order polynomial. The polynomial’s parameters are directly obtained by minimizing the sum of square errors between the gradients of MR images (both in the x-direction and y-direction) and the partial derivatives of the desired polynomial in the log domain. Compared to the existing retrospective algorithms, our algorithm combines the estimation of the gradient of the bias field and the reintegration of the obtained gradient polynomial together so that it is more robust against noise and can achieve better performance, which are demonstrated through experiments with both real and simulated brain MR images.

A two-step optimization approach for nonlocal total variation-based Rician noise reduction in magnetic resonance images.

Magnetic resonance imaging (MRI) often suffers from apparent noise during image acquisition and transmission. The degraded data can easily result in nonrobust postprocessing steps in medical image analysis. The purpose of this study is to eliminate noise effects and improve image quality using a nonlocal feature-preserving denoising method. From a statistical point of view, the magnitude MR images in the presence of noise are usually modeled using a Rician distribution. In the maximum a posteriori framework, a nonlocal total variation (NLTV)-based feature-preserving MRI Rician denoising model is proposed by taking full advantage of high degree of selfsimilarity and redundancy within MR images. However, the nonconvex data-fidelity term and nonsmooth NLTV regularizer make the denoising problem difficult to solve. To guarantee solution stability, a piecewise convex function is first introduced to approximate the nonconvex version. In what follows, a two-step optimization approach is developed to solve the resulting convex denoising model. In each step of this approach, the subproblem can be efficiently solved using existing optimization algorithms. The method performance is evaluated using synthetic and clinical MRI data sets as well as one diffusion tensor MRI (DTI) data set. Extensive experiments are conducted to compare the proposed method with several state-of-the-art denoising methods. For the synthetic and clinical MRI data sets, the proposed method considerably outperformed other competing denoising methods in terms of both quantitative and visual quality evaluations. It was capable of effectively removing noise in MR images and enhancing tissue characterization. The advantage of the proposed method became more significant as the noise level increased. For the DTI data set, compared with other denoising methods, the proposed method not only preserved the apparent diffusion coefficient but also generated more regular fractional anisotropy (FA) and color-coded FA without obvious visual artifacts. This study describes and validates a nonlocal feature-preserving method for Rician noise reduction on synthetic and real MRI data sets. By exploiting the feature-preserving capability of NLTV regularizer, the proposed method maintains a good balance between noise reduction and fine detail preservation. The experiments have demonstrated a huge potential of the proposed method for routine clinical practice.

3-dimensional throat region segmentation from MRI data based on Fourier interpolation and 3-dimensional level set methods.

A new algorithm for 3D throat region segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm initially pre-processes the MRI data to increase the contrast between the throat region and its surrounding tissues and to reduce artifacts. Isotropic 3D volume is reconstructed using the Fourier interpolation. Furthermore, a cube encompassing the throat region is evolved using level set method to form a smooth 3D boundary of the throat region. The results of the proposed algorithm on real and synthetic MRI data are used to validate the robustness and accuracy of the algorithm.