Advanced MRI techniques: biomarkers in neuropsychiatric lupus.

Objectives The objective of this study was to determine whether advanced MRI could provide biomarkers for diagnosis and prognosis in neuropsychiatric systemic lupus erythematosus (NPSLE). Methods Our prospective study included 28 systemic lupus erythematosus (SLE) patients with primary central NPSLE, 22 patients without NPSLE and 20 healthy controls. We used visual scales to evaluate atrophy and white matter hyperintensities, voxel-based morphometry and Freesurfer to measure brain volume, plus diffusion-tensor imaging (DTI) to assess white matter (WM) and gray matter (GM) damage. We compared the groups and correlated MRI abnormalities with clinical data. Results NPSLE patients had less GM and WM than controls ( p = 0.042) in the fronto-temporal regions and corpus callosum. They also had increased diffusivities in the temporal lobe WM ( p < 0.010) and reduced fractional anisotropy in the right frontal lobe WM ( p = 0.018). High clinical scores, longstanding disease, and low serum C3 were associated with atrophy, lower fractional anisotropy and higher diffusivity in the fronto-temporal lobes. Antimalarial treatment correlated negatively with atrophy in the frontal cortex and thalamus; it was also associated with lower diffusivity in the fronto-temporal WM clusters. Conclusions Atrophy and microstructural damage in fronto-temporal WM and GM in NPSLE correlate with severity, activity and the time from disease onset. Antimalarial treatment seems to give some brain-protective effects.

Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields.

BACKGROUND AND PURPOSE: Detection of disease activity, defined as new/enlarging T2 lesions on brain MR imaging, has been proposed as a biomarker in MS. However, detection of new/enlarging T2 lesions can be hindered by several factors that can be overcome with image subtraction. The purpose of this study was to improve automated detection of new T2 lesions and reduce user interaction to eliminate inter- and intraobserver variability. MATERIALS AND METHODS: Multiparametric brain MR imaging was performed at 2 time points in 36 patients with new T2 lesions. Images were registered by using an affine transformation and the Demons algorithm to obtain a deformation field. After affine registration, images were subtracted and a threshold was applied to obtain a lesion mask, which was then refined by using the deformation field, intensity, and local information. This pipeline was compared with only applying a threshold, and with a state-of-the-art approach relying only on image intensities. To assess improvements, we compared the results of the different pipelines with the expert visual detection. RESULTS: The multichannel pipeline based on the deformation field obtained a detection Dice similarity coefficient close to 0.70, with a false-positive detection of 17.8% and a true-positive detection of 70.9%. A statistically significant correlation (r = 0.81, P value = 2.2688e-09) was found between visual detection and automated detection by using our approach. CONCLUSIONS: The deformation field-based approach proposed in this study for detecting new/enlarging T2 lesions resulted in significantly fewer false-positives while maintaining most true-positives and showed a good correlation with visual detection annotations. This approach could reduce user interaction and inter- and intraobserver variability.

Evaluating the effects of white matter multiple sclerosis lesions on the volume estimation of 6 brain tissue segmentation methods.

BACKGROUND AND PURPOSE: The accuracy of automatic tissue segmentation methods can be affected by the presence of hypointense white matter lesions during the tissue segmentation process. Our aim was to evaluate the impact of MS white matter lesions on the brain tissue measurements of 6 well-known segmentation techniques. These include straightforward techniques such as Artificial Neural Network and fuzzy C-means as well as more advanced techniques such as the Fuzzy And Noise Tolerant Adaptive Segmentation Method, fMRI of the Brain Automated Segmentation Tool, SPM5, and SPM8. MATERIALS AND METHODS: Thirty T1-weighted images from patients with MS from 3 different scanners were segmented twice, first including white matter lesions and then masking the lesions before segmentation and relabeling as WM afterward. The differences in total tissue volume and tissue volume outside the lesion regions were computed between the images by using the 2 methodologies. RESULTS: Total gray matter volume was overestimated by all methods when lesion volume increased. The tissue volume outside the lesion regions was also affected by white matter lesions with differences up to 20 cm(3) on images with a high lesion load (≈50 cm(3)). SPM8 and Fuzzy And Noise Tolerant Adaptive Segmentation Method were the methods less influenced by white matter lesions, whereas the effect of white matter lesions was more prominent on fuzzy C-means and the fMRI of the Brain Automated Segmentation Tool. CONCLUSIONS: Although lesions were removed after segmentation to avoid their impact on tissue segmentation, the methods still overestimated GM tissue in most cases. This finding is especially relevant because on images with high lesion load, this bias will most likely distort actual tissue atrophy measurements.

Life cycle and human health risk assessments as tools for decision making in the design and implementation of nanofiltration in drinking water treatment plants.

A combined methodology using life cycle assessment (LCA) and human health risk assessment (HHR) is proposed in order to select the percentage of water in drinking water treatment plants (DWTP) that should be nanofiltered (NF). The methodological approach presented here takes into account environmental and social benefit criteria evaluating the implementation of new processes into conventional ones. The inclusion of NF process improves drinking water quality, reduces HHR but, in turn, increases environmental impacts as a result of energy and material demand. Results from this study lead to balance the increase of the impact in various environmental categories with the reduction in human health risk as a consequence of the respective drinking water production and consumption. From an environmental point of view, the inclusion of NF and recommended pretreatments to produce 43% of the final drinking water means that the environmental impact is nearly doubled in comparison with conventional plant in impact categories severely related with electricity production, like climate change. On the other hand, the carcinogenic risk (HHR) associated to trihalomethane formation potential (THMFP) decreases with the increase in NF percentage use. Results show a reduction of one order of magnitude for the carcinogenic risk index when 100% of drinking water is produced by NF.

Spectral clustering of shape and probability prior models for automatic prostate segmentation.

Imaging artifacts in Transrectal Ultrasound (TRUS) images and inter-patient variations in prostate shape and size challenge computer-aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose to use multiple mean parametric models derived from principal component analysis (PCA) of shape and posterior probability information to segment the prostate. In contrast to traditional statistical models of shape and intensity priors, we use posterior probability of the prostate region determined from random forest classification to build, initialize and propagate our model. Multiple mean models derived from spectral clustering of combined shape and appearance parameters ensure improvement in segmentation accuracies. The proposed method achieves mean Dice similarity coefficient (DSC) value of 0.96±0.01, with a mean segmentation time of 0.67±0.02 seconds when validated with 46 images from 23 datasets in a leave-one-patient-out validation framework.

Joint probability of shape and image similarities to retrieve 2D TRUS-MR slice correspondence for prostate biopsy.

This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The method combines both shape and image intensity information. The segmented prostate contours in both the imaging modalities are described by shape-context representations and matched using the Chi-square distance. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find image similarities. Finally, the joint probability values comprising shape and image similarities are used in a rule-based framework to provide the MR slice that closely resembles the TRUS slice acquired during the biopsy procedure. The method is evaluated for 20 patient datasets, of which 18 results match at least one of the two clinical expert choices.

A textural approach for mass false positive reduction in mammography.

During the last decade several algorithms have been proposed for automatic mass detection in mammographic images. However, almost all these methods suffer from a high number of false positives. In this paper we propose a new approach for tackling this false positive reduction problem. The key point of our proposal is the use of Local Binary Patterns (LBP) for representing the textural properties of the masses. We extend the basic LBP histogram descriptor into a spatially enhanced histogram which encodes both the local region appearance and the spatial structure of the masses. Support Vector Machines (SVM) are then used for classifying the true masses from the ones being actually normal parenchyma. Our approach is evaluated using 1792 ROIs extracted from the DDSM database. The experiments show that LBP are effective and efficient descriptors for mammographic masses. Moreover, the comparison with current methods illustrates that our proposal obtains a better performance.