Differential diagnoses of fibrosing lung diseases

Objectives: To describe the challenges inherent in diagnosing fibrosing lung diseases (FLD) on CT imaging and methodologies by which the diagnostic process may be simplified. / Methods: Extensive searches in online scientific databases were performed to provide relevant and contemporary evidence that describe the current state of knowledge related to FLD diagnosis. This includes descriptions of the utility of a working diagnosis for an individual case discussed in a multidisciplinary team (MDT) setting and challenges associated with the lack of consensus guidelines for diagnosing chronic hypersensitivity pneumonitis. / Results: As well as describing imaging features that indicate the presence of a fibrosing lung disease, those CT characteristics that nuance a diagnosis of the various FLDs are considered. The review also explains the essential information that a radiologist needs to convey to an MDT when reading a CT scan. Lastly, we provide some insights as to the future directions the field make take in the upcoming years. / Conclusions: This review outlines the current state of FLD diagnosis and emphasizes areas where knowledge is limited, and more evidence is required. Fundamentally, however, it provides a guide for radiologists when tackling CT imaging in a patient with FLD. / Advances in knowledge: This review encompasses advice from recent guideline statements and evidence from the latest studies in FLD to provide an up-to-date manual for radiologists to aid the diagnosis of FLD on CT imaging in an MDT setting

Phase-change chalcogenide glass metamaterial

Combining metamaterials with functional media brings a new dimension to their performance. Here we demonstrate substantial resonance frequency tuning in a photonic metamaterial hybridized with an electrically/optically switchable chalcogenide glass. The transition between amorphous and crystalline forms brings about a 10% shift in the near-infrared resonance wavelength of an asymmetric split-ring array, providing transmission modulation functionality with a contrast ratio of 4:1 in a device of sub-wavelength thickness.Comment: 3 pages, 3 figure

Automated template-based brain localization and extraction for fetal brain MRI reconstruction.

Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks

Luminescence of all-dielectric solution-processed perovskite metamaterial

We demonstrate that periodic subwavelength nanostructuring of solution-processed organolead halide perovskite films creates optical resonances, position of which can be controlled by design. Such metamaterial nanostructuring strongly enhances photo- and cathodo-luminescence of the films

Increased leptin and A-FABP levels in relapsing and progressive forms of MS

BACKGROUND: Leptin and adipocyte-fatty acid binding protein (A-FABP) are produced by white adipose tissue and may play a role in chronic inflammation in Multiple Sclerosis (MS). To assess leptin and A-FABP in relapsing and progressive forms of MS. METHODS: Adipokine levels were measured in untreated adult relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS) and Healthy control (HC). Pediatric-onset MS (POMS) and pediatric healthy controls (PHC) were also assessed. Leptin and A-FABP levels were measured in serum by ELISA. Groups were compared using linear mixed-effects model. RESULTS: Excluding two patients with Body Mass Index (BMI) > 50, a significant difference in leptin level was found between RRMS and HC controlling for age (p = 0.007), SPMS and HC controlling for age alone (p = 0.002), or age and BMI (p = 0.007). A-FABP levels were higher in SPMS than HC (p = 0.007), controlling for age and BMI. Differences in A-FABP levels between POMS and PHC was observed after controlling for age (p = 0.019), but not when BMI was added to the model (p = 0.081). CONCLUSION: Leptin and A-FABP levels are highest in SPMS compared to HC, suggesting a role in pathogenesis of this disease subtype. A-FABP levels are increased in POMS patients and may play a role in the early stages of disease

Multi-Modal Neuroimaging Analysis and Visualization Tool (MMVT)

Sophisticated visualization tools are essential for the presentation and exploration of human neuroimaging data. While two-dimensional orthogonal views of neuroimaging data are conventionally used to display activity and statistical analysis, three-dimensional (3D) representation is useful for showing the spatial distribution of a functional network, as well as its temporal evolution. For these purposes, there is currently no open-source, 3D neuroimaging tool that can simultaneously visualize desired combinations of MRI, CT, EEG, MEG, fMRI, PET, and intracranial EEG (i.e., ECoG, depth electrodes, and DBS). Here we present the Multi-Modal Visualization Tool (MMVT), which is designed for researchers to interact with their neuroimaging functional and anatomical data through simultaneous visualization of these existing imaging modalities. MMVT contains two separate modules: The first is an add-on to the open-source, 3D-rendering program Blender. It is an interactive graphical interface that enables users to simultaneously visualize multi-modality functional and statistical data on cortical and subcortical surfaces as well as MEEG sensors and intracranial electrodes. This tool also enables highly accurate 3D visualization of neuroanatomy, including the location of invasive electrodes relative to brain structures. The second module includes complete stand-alone pre-processing pipelines, from raw data to statistical maps. Each of the modules and module features can be integrated, separate from the tool, into existing data pipelines. This gives the tool a distinct advantage in both clinical and research domains as each has highly specialized visual and processing needs. MMVT leverages open-source software to build a comprehensive tool for data visualization and exploration.Comment: 29 pages, 10 figure

Preparation of chalcogenide materials for next generation optoelectronic devices

Chalcogenide materials are finding increasing interest as an active material in next generation optical and electronic devices. There wide range of properties, ranging from photosensitivity, ability to host rare earth ions, electrical conductivity, phase change, exceptional optical non-linearities to name only a few are fueling this interest. Moreover, the ability to synthesize these materials in numerous forms as diverse as 2D monolayers, microspheres, optical fibres, nanowires, thin films as well as bulk glass ingots of over a kilogram in size ensures their application space is vast. We began preparation of chalcogenides, largely based on sulphides, in 1992 and since then have built up an extensive capability for their purification, synthesis and fabrication in various forms. A key aspect of this facility is the ability to process in a flowing atmosphere of hydrogen sulphide which provided the capability of synthesis from elemental, oxide or halide precursors, processing through various chemical vapour deposition reactions as well as post purification.In this talk we describe recent additions to the range of materials we synthesize highlighting transition metal di-chalcogenides for electronic applications, an example of which is shown below, crystalline semiconductors for solar cell applications, ion implanted thin films which provide carrier type reversal, low power phase change memory devices, switchable metamaterial devices as well as traditional chalcogenides glass and optical fibre

Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for anomaly screening. For this ultrasound (US) is employed. While expert sonographers are adept at reading US images, MR images are much easier for non-experts to interpret. Hence in this paper we seek to produce images with MRI-like appearance directly from clinical US images. Our own clinical motivation is to seek a way to communicate US findings to patients or clinical professionals unfamiliar with US, but in medical image analysis such a capability is potentially useful, for instance, for US-MRI registration or fusion. Our model is self-supervised and end-to-end trainable. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise an extractor to determine shared latent features, which are then used for data synthesis. Since paired data was unavailable for our study (and rare in practice), we propose to enforce the distributions to be similar instead of employing pixel-wise constraints, by adversarial learning in both the image domain and latent space. Furthermore, we propose an adversarial structural constraint to regularise the anatomical structures between the two modalities during the synthesis. A cross-modal attention scheme is proposed to leverage non-local spatial correlations. The feasibility of the approach to produce realistic looking MR images is demonstrated quantitatively and with a qualitative evaluation compared to real fetal MR images.Comment: MICCAI-MLMI 201