Mechanism and kinetics of mineral weathering under acid conditions

This study deals with the relationships between crystal structure, grain diameter, surface morphology and dissolution kinetics for feldspar and quartz under acid conditions.Intensively ground samples from large, naturally weathered mineral fragments are frequently used in dissolution studies. The surface area of such samples, estimated from their gas adsorption isotherm (BET method), is normally implied to be all freshly created by grinding. This study revealed that: (1) during natural weathering, micropores (diameters ≈2 nm) develop in feldspar but not in quartz grains; (2) the micropores account for virtually all BET surface area of naturally weathered feldspar grains; and (3) due to the micropores, grinding of large, naturally weathered feldspar fragments is highly ineffective in creating samples with only freshly ground BET surface area.By assuming all BET surface area of ground feldspar samples to be freshly created, experimental dissolution data have been explained from dissolution rates essentially independent of the grain diameter. For ground feldspar samples this study revealed that: (1) the dissolution rate of the freshly created BET surfaces is essentially proportional to the grain diameter; and (2) the dissolution rate of the naturally weathered BET surfaces, still present after grinding, is most likely independent of the grain diameter. Moreover, the dissolution rate, normalized to BET surface area, of unfractured, naturally weathered feldspar grains was essentially independent of the grain diameter. These findings can be explained if: (1) the average density of dissolution sites on freshly created feldspar surfaces is approximately proportional to the grain diameter; (2) micropores develop at dissolution sites during natural weathering; and (3) the BET surface area of the micropore "walls" (i.e. the area perpendicular to the grain surface) is essentially non-reactive.Thermodynamical considerations and Monte Carlo simulations showed that: (1) the formation of micropores in feldspar but not in quartz grains during natural weathering can be explained from enhanced dissolution at crystal defects; and (2) the BET surface area of micropore "walls" from enhanced dissolution at crystal defects is essentially non-reactive. A kinetic model is developed, showing for feldspar that the non-reactivity of the micropore "walls" helps to explain the discrepancy, reported in the literature, between laboratory and field dissolution rates

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A semi-supervised large margin algorithm for white matter hyperintensity segmentation

Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation

Improved cerebellar tissue classification on magnetic resonance images of brain.

PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation. MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object

Macrofilaricidal Activity in Wuchereria bancrofti after 2 Weeks Treatment with a Combination of Rifampicin plus Doxycycline

Infection with the filarial nematode Wuchereria bancrofti can lead to lymphedema, hydrocele, and elephantiasis. Since adult worms cause pathology in lymphatic filariasis (LF), it is imperative to discover macrofilaricidal drugs for the treatment of the infection. Endosymbiotic Wolbachia in filariae have emerged as a new target for antibiotics which can lead to macrofilaricidal effects. In Ghana, a pilot study was carried out with 39 LF-infected men; 12 were treated with 200 mg doxycycline/day for 4 weeks, 16 were treated with a combination of 200 mg doxycycline/day + 10 mg/kg/day rifampicin for 2 weeks, and 11 patients received placebo. Patients were monitored for Wolbachia and microfilaria loads, antigenaemia, and filarial dance sign (FDS). Both 4-week doxycycline and the 2-week combination treatment reduced Wolbachia load significantly. At 18 months posttreatment, four-week doxycycline resulted in 100% adult worm loss, and the 2-week combination treatment resulted in a 50% adult worm loss. In conclusion, this pilot study with a combination of 2-week doxycycline and rifampicin demonstrates moderate macrofilaricidal activity against W. bancrofti

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

The accurate assessment of White matter hyperintensities (WMH) burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data. The manual delineation of WMHs is tedious, costly and time consuming. This is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense. Several automated methods aiming to tackle the challenges of WMH segmentation have been proposed, however cannot differentiate between WMH and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. As far as we know, this is the first time such differentiation task has explicitly been proposed. The proposed fully convolutional CNN architecture, is comprised of an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, were found to be in line with the associations found with the expert-annotated volumes

Post-Acquisition Processing Confounds in Brain Volumetric Quantification of White Matter Hyperintensities

BACKGROUND: Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored. NEW METHOD: 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance. RESULTS: A series of experiments using standard MRI acquisition sequences explored post-acquisition sources-of-variability in the quantification of WMH volumetric data. Sources-of-variability included: the choice of image center, software suite and version, thresholding selection, and manual editing procedures (when used). Controlling for the identified sources-of-variability led to a protocol with less than 0.5% variability between independent raters in post-acquisition WMH volumetric quantification. COMPARISON WITH EXISTING METHOD(S): Post-acquisition processing techniques can introduce an average variance approaching 15% in WMH volume quantification despite identical scan acquisitions. Understanding and controlling for such sources-of-variability can reduce post-acquisition quantitative image processing variance to less than 0.5%. DISCUSSION: Considerations of potential sources-of-variability in MRI volume quantification techniques and reduction in such variability is imperative to allow for reliable cross-site and cross-study comparisons

A large margin algorithm for automated segmentation of white matter hyperintensity

Precise detection and quantification of white matter hyperintensity (WMH) is of great interest in studies of neurological and vascular disorders. In this work, we propose a novel method for automatic WMH segmentation with both supervised and semi-supervised large margin algorithms provided by the framework. The proposed algorithms optimize a kernel based max-margin objective function which aims to maximize the margin between inliers and outliers. We show that the semi-supervised learning problem can be formulated to learn a classifier and label assignment simultaneously, which can be solved efficiently by an iterative algorithm. The model is learned first via the supervised approach and then fine-tuned on a target image by using the semi-supervised algorithm. We evaluate our method on 88 brain fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images from subjects with vascular disease. Quantitative evaluation of the proposed approach shows that it outperforms other well known methods for WMH segmentation

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke

Rationale: Cerebral small vessel disease (SVD) is common in ageing and patients with dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed. Aims: To identify ways of quantifying imaging markers in research of patients with SVD and operationalize the recommendations from the STandards for ReportIng Vascular changes on nEuroimaging guidelines. Here, we report the rationale, design, and methodology of a brain image analysis protocol based on our experience from observational longitudinal studies of patients with nondisabling stroke. Design: The MRI analysis protocol is designed to provide quantitative and qualitative measures of disease evolution including: acute and old stroke lesions, lacunes, tissue loss due to stroke, perivascular spaces, microbleeds, macrohemorrhages, iron deposition in basal ganglia, substantia nigra and brain stem, brain atrophy, and white matter hyperintensities, with the latter separated into intense and less intense. Quantitative measures of tissue integrity such as diffusion fractional anisotropy, mean diffusivity, and the longitudinal relaxation time are assessed in regions of interest manually placed in anatomically and functionally relevant locations, and in others derived from feature extraction pipelines and tissue segmentation methods. Morphological changes that relate to cognitive deficits after stroke, analyzed through shape models of subcortical structures, complete the multiparametric image analysis protocol. Outcomes: Final outcomes include guidance for identifying ways to minimize bias and confounds in the assessment of SVD and stroke imaging biomarkers. It is intended that this information will inform the design of studies to examine the underlying pathophysiology of SVD and stroke, and to provide reliable, quantitative outcomes in trials of new therapies and preventative strategies