Cortical thickness and diffusion tensor imaging in patients with neuropsychiatric systemic lupus erythematosus, with normal structure on brain MRI.

OBJECTIVES: Neuropsychiatric systemic lupus erythematosus (NPSLE) is associated with adverse outcomes; however, imaging abnormalities are only detectable by conventional brain magnetic resonance imaging (MRI) in up to 50% of patients. This study investigated the variability in cortical thickness and diffusion tensor imaging (DTI) parameters among patients with NPSLE whose brain morphology appeared normal on conventional MRI. METHODS: This retrospective study enrolled 27 female patients with NPSLE (median age: 41.0 years, range: 22-63 years) and 34 female healthy controls (median age: 37.0 years, range: 24-55 years). None exhibited evident abnormalities on conventional MRI. Regional volumes, cortical thickness, and DTI parameters, including fractional anisotropy (FA) and mean diffusivity (MD), were compared. Age-adjusted multivariable logistic regression analysis was conducted to detect significant NPSLE-associated differences. RESULTS: No significant differences in grey or white matter volume fractions were observed between the groups. However, the NPSLE group demonstrated significant cortical thinning in the right pars opercularis (2.45 vs 2.52 mm, p = 0.007), reduced FA values in the fornix (0.35 vs 0.40, p = 0.001) and left anterior limb of internal capsule (0.50 vs 0.52, p = 0.012), and increased MD in the fornix (1.71 vs 1.48, p = 0.009) and left posterior corona radiata (0.80 vs 0.76, p = 0.005) compared with those of healthy controls. CONCLUSIONS: Cortical thickness measurements and DTI analyses can be used to detect differential variations in patients with NPSLE who exhibit an otherwise normal brain structure on conventional MRI, indicating the existence of subtle changes despite the absence of obvious macrostructural central nervous system involvement of lupus.

Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images.

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods-kernel CEM (K-CEM) and iterative CEM (I-CEM)-were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types.

Novel Automated Method for the Detection of White Matter Hyperintensities in Brain Multispectral MR Images.

BACKGROUND: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients' conditions can be contained. AIMS: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. METHODS: The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. RESULTS: The most notable findings are as follows: Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images. CONCLUSION: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances the detection of brain lesions.

Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques.

Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.

Robust volume assessment of brain tissues for 3-dimensional fourier transformation MRI via a novel multispectral technique.

A new TRIO algorithm method integrating three different algorithms is proposed to perform brain MRI segmentation in the native coordinate space, with no need of transformation to a standard coordinate space or the probability maps for segmentation. The method is a simple voxel-based algorithm, derived from multispectral remote sensing techniques, and only requires minimal operator input to depict GM, WM, and CSF tissue clusters to complete classification of a 3D high-resolution multislice-multispectral MRI data. Results showed very high accuracy and reproducibility in classification of GM, WM, and CSF in multislice-multispectral synthetic MRI data. The similarity indexes, expressing overlap between classification results and the ground truth, were 0.951, 0.962, and 0.956 for GM, WM, and CSF classifications in the image data with 3% noise level and 0% non-uniformity intensity. The method particularly allows for classification of CSF with 0.994, 0.961 and 0.996 of accuracy, sensitivity and specificity in images data with 3% noise level and 0% non-uniformity intensity, which had seldom performed well in previous studies. As for clinical MRI data, the quantitative data of brain tissue volumes aligned closely with the brain morphometrics in three different study groups of young adults, elderly volunteers, and dementia patients. The results also showed very low rates of the intra- and extra-operator variability in measurements of the absolute volumes and volume fractions of cerebral GM, WM, and CSF in three different study groups. The mean coefficients of variation of GM, WM, and CSF volume measurements were in the range of 0.03% to 0.30% of intra-operator measurements and 0.06% to 0.45% of inter-operator measurements. In conclusion, the TRIO algorithm exhibits a remarkable ability in robust classification of multislice-multispectral brain MR images, which would be potentially applicable for clinical brain volumetric analysis and explicitly promising in cross-sectional and longitudinal studies of different subject groups.

Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine.

PURPOSE: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. MATERIALS AND METHODS: Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICA+SVM method to analyze three sets of MR images, T1-weighted, T2-weighted, and proton density/fluid-attenuated inversion recovery images. RESULTS: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICA+SVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICA+SVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra- and inter-operator coefficient of variations. CONCLUSION: The experiments conducted provide evidence that the ICA+SVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI.

Secure adaptive topology control for wireless ad-hoc sensor networks.

This paper presents a secure decentralized clustering algorithm for wireless ad-hoc sensor networks. The algorithm operates without a centralized controller, operates asynchronously, and does not require that the location of the sensors be known a priori. Based on the cluster-based topology, secure hierarchical communication protocols and dynamic quarantine strategies are introduced to defend against spam attacks, since this type of attacks can exhaust the energy of sensor nodes and will shorten the lifetime of a sensor network drastically. By adjusting the threshold of infected percentage of the cluster coverage, our scheme can dynamically coordinate the proportion of the quarantine region and adaptively achieve the cluster control and the neighborhood control of attacks. Simulation results show that the proposed approach is feasible and cost effective for wireless sensor networks.

Band expansion-based over-complete independent component analysis for multispectral processing of magnetic resonance images.

Independent component analysis (ICA) has found great promise in magnetic resonance (MR) image analysis. Unfortunately, two key issues have been overlooked and not investigated. One is the lack of MR images to be used to unmix signal sources of interest. Another is the use of random initial projection vectors by ICA, which causes inconsistent results. In order to address the first issue, this paper introduces a band-expansion process (BEP) to generate an additional new set of images from the original MR images via nonlinear functions. These newly generated images are then combined with the original MR images to provide sufficient MR images for ICA analysis. In order to resolve the second issue, a prioritized ICA (PICA) is designed to rank the ICA-generated independent components (ICs) so that MR brain tissue substances can be unmixed and separated by different ICs in a prioritized order. Finally, BEP and PICA are combined to further develop a new ICA-based approach, referred to as PICA-BEP to perform MR image analysis.

An Over-Complete Independent Component Analysis (ICA) Approach to Magnetic Resonance Image Analysis.

This paper presents a new application of independent component analysis (ICA) in magnetic resonance (MR) image analysis. One of most successful applications for ICA-based approaches in MR imaging is functional MRI (fMRI) which basically deals with one-dimensional temporal signals. The ICA approach proposed in this paper is rather different and considers a set of MR images acquired by different pulse sequences as a 3-dimensional image cube and performs image analysis rather than signal analysis. One major difference between the fMRI- based ICA approaches and our proposed ICA-based image analysis is that the ICA used in the former is under-complete as opposed to the latter which uses over-complete ICA. Such a fundamental difference results in completely different applications.