Multispectral Magnetic Resonance Research Articles

Shaped Magnetogel Microparticles for Multispectral Magnetic Resonance Contrast and Sensing.

Multispectral magnetic resonance imaging (MRI) contrast agents are microfabricated three-dimensional magnetic structures that encode nearby water protons with discrete frequencies. The agents have a unique radiofrequency (RF) resonance that can be tuned by engineering the geometric parameters of these microstructures. Multispectral contrast agents can be used as sensors by incorporating a stimulus-driven shape-changing response into their structure. These geometrically encoded magnetic sensors (GEMS) enable MRI-based sensing via environmentally induced changes to their geometry and their corresponding RF resonance. Previously, GEMS have been made using thin-film lithography techniques in a cleanroom environment. While these approaches offer precise control of the microstructure, they can be a limitation for researchers who do not have cleanroom access or microfabrication expertise. Here, an alternative approach for GEMS fabrication based on soft lithography is introduced. The fabrication scheme uses cheap, accessible materials and simple chemistry to produce shaped magnetic hydrogel microparticles with multispectral MRI contrast properties. The microparticles can be used as sensors by fabricating them out of shape-reconfigurable, "smart" hydrogels. The change in shape causes a corresponding shift in the resonance of the GEMS, producing an MRI-addressable readout of the microenvironment. Proof-of-principle experiments showing a multispectral response to pH change with cylindrical shell-shaped magnetogel GEMS are presented.

Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging

The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data. The multi-spectral MRSI can effectively separate spectral peaks of multiple substances and is useful to measure spatial distributions of substances. However it is difficult to measure time-varying substance distributions directly by ordinary full sampling because the measurement requires a significantly long time. In this study, we propose a novel method to reconstruct the spatio-temporal distributions of substances from randomly undersampled multi-spectral MRSI data on the basis of compressed sensing (CS) and the partially separable function model with base spectra of substances. In our method, we have employed spatio-temporal sparsity and temporal smoothness of the substance distributions as prior knowledge to perform CS. By directly reconstructing the spatio-temporal distributions of the substances themselves without reconstructing the spectra, this method significantly reduces the amount of MRSI data required per single time frame. We have formulated a regularized minimization problem for reconstruction and solved it by the alternating direction method of multipliers (ADMM). The effectiveness of our method has been evaluated using phantom data sets of glass tubes filled with glucose or lactate solution in increasing amounts over time and animal data sets of a tumor-bearing mouse to observe the metabolic dynamics involved in the Warburg effect in vivo. The reconstructed results are consistent with the expected behaviors, showing that our method can reconstruct the spatio-temporal distribution of substances with a temporal resolution of four seconds which is extremely short time scale compared with that of full sampling. Since this method utilizes only prior knowledge naturally assumed for the spatio-temporal distributions of substances and is independent of the number of the spectral and spatial dimensions or the acquisition sequence of MRSI, it is expected to contribute to revealing the underlying substance dynamics in MRSI data already acquired or to be acquired in the future.

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

Evaluation of Therapeutic Response in Spinal Muscular Atrophy Using Multispectral Optoacoustic Tomography (MSOT) and Magnetic Resonance Imaging (MRI)

Evaluation of Therapeutic Response in Spinal Muscular Atrophy Using Multispectral Optoacoustic Tomography (MSOT) and Magnetic Resonance Imaging (MRI)

Carotid Bifurcation With Tandem Stenosis-A Patient-Specific Case Study Combined in vivo Imaging, in vitro Histology and in silico Simulation.

A patient-specific carotid bifurcation with tandem stenosis found at both internal carotid artery (ICA) and common carotid artery (CCA) was studied. The in vivo pre-carotid endarterectomy (pre-CEA) multi-spectral magnetic resonance imaging (MRI) were performed and in vitro post-CEA carotid plaque tissue sample was collected. MR imaging data and tissue sample staining histology were used to recognize the plaque components. Further, the computational fluid dynamics (CFD) were performed on four MR-based reconstructed 3D carotid bifurcation models (the patient-specific geometry with tandem stenosis and three presumptive geometries by removing the stenosis part). The flow and shear stress behavior affected by the tandem stenosis was analyzed. From the results of MR segmentation and histology analysis, plaque lipid pool and calcification were found at both ICA and CCA. From the result of CFD simulation, the flow shear stress behavior suggested the tandem stenosis as a more “dangerous” situation than a single-stenosis artery. Besides, the CFD results deduced that the stenosis at the CCA location formed initially and led to the subsequent formation of stenosis at ICA. This study suggests that when planning CEA, CFD simulation on the presumptive models could help clinicians to estimate the blood flow behavior after surgery. Particular attention should be paid to the case of tandem stenosis, as the local hemodynamic environment is more complex and treatment of one stenosis may lead to a variation in the hemodynamic loading on the second plaque, which may result in either a higher risk of plaque rupture or restenosis.

A simple and fast adaptive nonlocal multispectral filtering algorithm for efficient noise reduction in magnetic resonance imaging

PurposeWe recently introduced a multispectral (MS) nonlocal (NL) filter based on maximum likelihood estimation (MLE) of voxel intensities, termed MS-NLML. While MS-NLML provides excellent noise reduction and improved image feature preservation as compared to other NL or MS filters, it requires considerable processing time, limiting its application in routine analyses. In this work, we introduced a fast, simple, and robust filter, termed nonlocal estimation of multispectral magnitudes (NESMA), for noise reduction in multispectral (MS) magnetic resonance imaging (MRI). MethodsThrough extensive simulation and in-vivo analyses, we compared the performance of NESMA and MS-NLML in terms of noise reduction and processing efficiency. Further, we introduce two simple adaptive methods that permit spatial variation of similar voxels, R, used in the filtering. The first method is semi-adaptive and permits variation of R across the image by using a relative Euclidean distance (RED) similarity threshold. The second method is fully adaptive and filters the raw data with several RED similarity thresholds to spatially determine the optimal threshold value using an unbiased criterion. ResultsNESMA shows very similar filtering performance as compared to MS-NLML, however, with much simple implementation and very fast processing time. Further, for both filters, the adaptive methods were shown to further reduce noise in comparison with the conventional non-adaptive method in which R is set to a constant value throughout the image. ConclusionsNESMA is fast, robust, and straightforward to implement filter. These features render it suitable for routine clinical use and analysis of large MRI datasets.

Ischemic stroke lesion segmentation using stacked sparse autoencoder

Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion region. Most existing methods mainly rely on designing hand-crafted features followed by a classifier model for ischemic stroke lesion segmentation. Design of these features requires complex domain knowledge and often lacks the ability to differentiate between the stroke lesions and the normal classes. In this work, we propose an unsupervised featured learning approach based on stacked sparse autoencoder (SSAE) framework for automatically learning the features for accurate segmentation of stroke lesions from brain MR images. A deep architecture is designed using sparse autoencoder (SAE) layers, followed by support vector machine (SVM) classifier for classifying the patches into normal or lesions. We validated our approach on a publicly available Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset, with a mean precision of 0.968, mean dice coefficient (DC) of 0.943, mean recall of 0.924 and mean accuracy of 0.904. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods in terms of precision, DC, and recall. Quantitative evaluation was carried out and compared with the existing approaches, which demonstrates that the proposed method is 25.71%, 36.67%, and 16.96% higher in terms of precision, DC and recall values, respectively. The unsupervised features learned via SSAE framework performs better than the hand-crafted features and can be easily trained on large datasets.

Adopting Quaternion Wavelet Transform to Fuse Multi-Modal Medical Images

Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In this paper, we propose a novel multi-modal medical image fusion method based on simplified pulse-coupled neural network and quaternion wavelet transform. The proposed fusion algorithm is capable of combining not only pairs of computed tomography (CT) and magnetic resonance (MR) images, but also pairs of CT and proton-density-weighted MR images, and multi-spectral MR images such as T1 and T2. Experiments on six pairs of multi-modal medical images are conducted to compare the proposed scheme with four existing methods. The performances of various methods are investigated using mutual information metrics and comprehensive fusion performance characterization (total fusion performance, fusion loss, and modified fusion artifacts criteria). The experimental results show that the proposed algorithm not only extracts more important visual information from source images, but also effectively avoids introducing artificial information into fused medical images. It significantly outperforms existing medical image fusion methods in terms of subjective performance and objective evaluation metrics.

Multiobjective fuzzy clustering with coalition formation: the case of brain image processing

ABSTRACTThis paper presents a multi-objective fuzzy clustering approach under iterative modelling for the segmentation of human brain multispectral magnetic resonance image. It optimizes the global spatial fuzzy compactness and fuzzy spatial separation among clusters. Our method generates an ensemble of Pareto solutions among which the final solution will be proposed to the analyst. At this final step, the Shapley value concept is utilized to select the best solution. An empirical study on brain images shows a better performance of our proposed method when compared to competing algorithms.

Multispectral MRI Image Fusion for Enhanced Visualization of Meningioma Brain Tumors and Edema Using Contourlet Transform and Fuzzy Statistics

This paper proposes a multispectral magnetic resonance imaging (MRI) image fusion scheme for improved visualization of anatomical and pathological information of meningioma (MG) brain tumors that combines the contourlet transform and fuzzy statistics. The proposed fusion technique mainly targets the tumor and its surrounding hyperintense (edema) region, which leads to improved brain imaging informatics for radiologists. The developed methodology mainly consists of the contourlet transform for multiscale and directional decomposition, fuzzy entropy for fusing approximation coefficients, and region-based fuzzy energy for fusing detailed coefficients of two input images with the same orientations. Two fusion rules are established here in order to fuse corresponding lower- and higher-frequency subbands of images. The proposed methodology is applied to five various combinations (such as T1-weighted and T2-weighted, T1 post-contrast and T2-weighted etc.) generated from four modalities of MRI images (T1-weighted, T1 post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR)). A total of 150 MRI images (30 images from each of five combinations) are considered from 20 cases of MG brain tumors. A quantitative evaluation of the proposed method is performed in terms of three performance measures. The performance is compared with that of existing medical image fusion techniques tested on the same dataset. Experimental results show the superiority of the proposed methodology in terms of both qualitative and quantitative measures, which also indicates that fused images contain enriched diagnostic information that can aid the detection of tumors and edema. A fusion of post-contrast T1-weighted MRI images with FLAIR and T2-weighted MRI images provided clinically relevant information.

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.

Brain Tumor Segmentation from Multispectral MRIs Using Sparse Representation Classification and Markov Random Field Regularization

Automatic brain tumor segmentation from multispectral magnetic resonance imaging (MRI) data is an important but a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. In this paper, we propose a fully automatic technique for brain tumor segmentation from multispectral human brain MRIs. We first use the intensities of different patches in multispectral MRIs to represent the features of both normal and abnormal tissues and generate a dictionary for following tissue classification. Then, the sparse representation classification (SRC) is applied to classify the brain tumor and normal brain tissue in the whole image. At last, the Markov random field (MRF) regularization introduces spatial constraints to the SRC to take into account the pair-wise homogeneity in terms of classification labels and multispectral voxel intensities. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results.

Magnetic resonance brain tissue classification and volume calculation

This study develops a volume sphering analysis (VSA) approach to tissue classification and volume calculation of multispectral magnetic resonance (MR) brain images. It processes all multispectral MR image slices as an image cube while using only one set of training samples obtained from a single multispectral image slice to perform tissue classification as well as to calculate tissue volumes. In order to make a one slice set of training samples fit for all MR image slices a novel multispectral signature-specified extrapolation algorithm is particularly designed for this purpose so that the selected set of training samples can be extrapolated to create new data samples that are also applicable to other MR image slices. As a consequence, it significantly reduces the tremendous burden on radiologists for selection of training samples as well as computational cost. To further resolve instability and inconsistency issues which may be caused by training sample extrapolation, the proposed VSA also includes a support vector machine to refine training samples and develops an iterative Fisher’s linear discriminant analysis (IFLDA) to make VSA robust and insensitive to new generated training samples so as to improve the traditional slice-by-slice MR image classification. Experimental results demonstrate that VSA in conjunction with IFLDA not only performs comparably to approaches using training samples from individual image slices, but also saves significant time in selecting training samples and computational cost.

Self-Trained Supervised Segmentation of Subcortical Brain Structures Using Multispectral Magnetic Resonance Images.

The aim of this paper is investigate the feasibility of automatically training supervised methods, such as k-nearest neighbor (kNN) and principal component discriminant analysis (PCDA), and to segment the four subcortical brain structures: caudate, thalamus, pallidum, and putamen. The adoption of supervised classification methods so far has been limited by the need to define a representative training dataset, operation that usually requires the intervention of an operator. In this work the selection of the training data was performed on the subject to be segmented in a fully automated manner by registering probabilistic atlases. Evaluation of automatically trained kNN and PCDA classifiers that combine voxel intensities and spatial coordinates was performed on 20 real datasets selected from two publicly available sources of multispectral magnetic resonance studies. The results demonstrate that atlas-guided training is an effective way to automatically define a representative and reliable training dataset, thus giving supervised methods the chance to successfully segment magnetic resonance brain images without the need for user interaction.

DEVELOPMENT OF A NEW DATA FUSION SYSTEM FOR SEGMENTATION OF MR IMAGES

In this research paper, we propose an automatic segmentation method of multispectral magnetic resonance image (MRI) of the human brain using an information fusion approach through the framework of the possibility theory. The fusion process is summarized into three essential steps. First, a data is extracted from the various images and modeled in a common mathematical framework, in this step the fuzzy C-means (FCM) algorithm is chosen. The combination rule is used to combine this information in the second step. A final segmented image is the result of the last phase. Our experimental results using simulated brain MRI datasets show that the proposed approach overcome the impact of the noise and substantially improve the accuracy of image segmentation.

Evaluation of supervised methods for the classification of major tissues and subcortical structures in multispectral brain magnetic resonance images

This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighboring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images.

Ellipsoidal microcavities: electromagnetic properties, fabrication, and use as multispectral MRI agents.

Micro- and nanoscale structuring of electromagnetically susceptible materials underpins many recent advances in biomedical imaging, sensing, and treatment.[1,2] From plasmonic nanoparticles[3] and surface plasmon enhanced detectors,[4] to microengineered multispectral magnetic resonance imaging (MRI) agents,[5–7] tailored material geometries enable new functionalities through locally modified fields and amplified signals. This communication introduces a new class of such field-shaping microstructure: the ellipsoidal microcavity. Ellipsoidal particles are already of considerable interest in mathematical packing and granular media studies,[8] and have been shown to self-assemble into anisotropic materials with unique mechanical and optical properties.[9–11] Here we emphasize that there are additional electromagnetic advantages to their use that stem from their unique ability to generate truly uniform local electromagnetic fields. This communication discusses these ellipsoidal field properties and introduces a new microfabrication protocol that produces almost mathematically exact ellipsoids and ellipsoidal cavities. In addition to providing a new route to colloids with well-defined eccentricities that may enable novel self-assembled structure geoemtries, when hollowed out, the remaining ellipsoidal cavities’ uniform fields makes them ideal candidates for a new class of tunable, multispectral MRI agents.

Multispectral Quantitative MR Imaging of the Human Brain: Lifetime Age-related Effects

Quantitative magnetic resonance (MR) imaging allows visualization of age-related changes in the normal human brain from functional, biochemical, and morphologic perspectives. Findings at quantitative MR imaging support age-related microstructural changes in the brain: (a) volume expansion, increased myelination, and axonal growth, which establish neural connectivity in neurodevelopment, followed by (b) volume loss, myelin breakdown, and axonal degradation, leading to the disruption of neural integrity later in life. A rapid growth change followed by a continuous slower change in quantitative MR parameters can be modeled with a logarithmic or exponential decay function. The age dependencies during adulthood often fit a quadratic model for transitional changes with accelerated aging effects or a linear model for steady changes.Understanding these general trends over the human life span can improve assessment for a specific disease by helping determine appropriate study settings. Once a consensus on acquisition techniques and image processing algorithms has been reached, quantitative MR imaging can play an important role in the assessment of disease states affecting the brain.

Microfabricated iron oxide particles for tunable, multispectral magnetic resonance imaging

Recently, a novel class of magnetic resonance imaging (MRI) contrast agents developed using top-down microfabrication approaches has been reported. To realize the full capacity of this potentially paradigm-shifting approach to MRI contrast agent design, the integration of biocompatible materials with tunable magnetic properties was sought. To this end, deposition techniques yielding iron oxide thin films with a large range of readily tunable saturation magnetic polarization were developed using reactive sputtering under various conditions. Following the characterization of their chemical compositions and crystalline structures, the iron oxide thin films were subsequently utilized in the fabrication of size and shape specific magnetic double-disk microparticles, yielding the advantages of this new class of MRI contrast agents, including multiplexing capability, diffusion-driven signal amplification, and functional imaging capacity. The integration of iron oxides into this class of fabricated contrast agents offers several distinct advantages, including biocompatibility and the additional degree of freedom in the design of these agents achieved by the tunability of the iron oxide thin film magnetism, both of which are critical features in further optimizing these agents.

Quantitative mouse brain phenotyping based on single and multispectral MR protocols

Sophisticated image analysis methods have been developed for the human brain, but such tools still need to be adapted and optimized for quantitative small animal imaging. We propose a framework for quantitative anatomical phenotyping in mouse models of neurological and psychiatric conditions. The framework encompasses an atlas space, image acquisition protocols, and software tools to register images into this space. We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols. We present a flexible approach for segmenting such hyperimages, optimizing registration, and identifying optimal combinations of image channels for particular structures. Brain imaging with T1, T2* and T2 contrasts yielded accuracy in the range of 83% for hippocampus and caudate putamen (Hc and CPu), but only 54% in white matter tracts, and 44% for the ventricles. The addition of diffusion tensor parameter images improved accuracy for large gray matter structures (by >5%), white matter (10%), and ventricles (15%). The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6%; so Dice coefficients for Hc and CPu reached 93%, for white matter 79%, for ventricles 68%, and for substantia nigra 80%. We demonstrate the segmentation pipeline for the widely used C57BL/6 strain, and two test strains (BXD29, APP/TTA). This approach appears promising for characterizing temporal changes in mouse models of human neurological and psychiatric conditions, and may provide anatomical constraints for other preclinical imaging, e.g. fMRI and molecular imaging. This is the first demonstration that multiple MR imaging modalities combined with multivariate segmentation methods lead to significant improvements in anatomical segmentation in the mouse brain.