134 research outputs found

    Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

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    Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

    Ruthenium-thymine acetate binding modes: Experimental and theoretical studies

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    Ruthenium complexes have proved to exhibit antineoplastic activity, related to the interaction of the metal ion with DNA. In this context, synthetic and theoretical studies on ruthenium binding modes of thymine acetate (THAc) have been focused to shed light on the structure-activity relationship. This report deals with the reaction between dihydride ruthenium mer-[Ru(H)2(CO)(PPh3)3], 1 and the thymine acetic acid (THAcOH) selected as model for nucleobase derivatives. The reaction in refluxing toluene between 1 and THAcOH excess, by H2 release affords the double coordinating species k1-(O)THAc-, k2-(O,O)THAc-[Ru(CO)(PPh3)2], 2. The X-ray crystal structure confirms a simultaneous monohapto, dihapto- THAc coordination in a reciprocal facial disposition. Stepwise additions of THAcOH allowed to intercept the monohapto mer-k1(O)THAc-Ru(CO)H(PPh3)3] 3 and dihapto trans(P,P)-k2(O,O)THAc-[Ru(CO)H(PPh3)2] 4 species. Nuclear magnetic resonance (NMR) studies, associated with DFT (Density Function Theory)-calculations energies and analogous reactions with acetic acid, supported the proposed reaction path. As evidenced by the crystal supramolecular hydrogen-binding packing and 1H NMR spectra, metal coordination seems to play a pivotal role in stabilizing the minor [(N=C(OH)] lactim tautomers, which may promote mismatching to DNA nucleobase pairs as a clue for its anticancer activity

    Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

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    © 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used

    The Effect of Active Pharmaceutical Ingredients on Aerosol Electrostatic Charges from Pressurized Metered Dose Inhalers

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    The final publication is available at Springer via: http://dx.doi.org/10.1007/s11095-015-1674-6.Purpose. This study investigated the effect of different active pharmaceutical ingredients (API) on aerosol electrostatic charges and aerosol performances for pressurized metered dose inhalers (pMDIs), using both insulating and conducting actuators. Methods. Five solution-based pMDIs containing different API ingredients including: beclomethasone dipropionate (BDP), budesonide (BUD), flunisolide (FS), salbutamol base (SB) and ipratropium bromide (IPBr) were prepared using pressure filling technique. Actuator blocks made from nylon, polytetrafluoroethylene (PTFE) and aluminium were manufactured with 0.3 mm nominal orifice diameter and cone nozzle shape. Aerosol electrostatics for each pMDI formulation and actuator were evaluated using the electrical low-pressure impactor (ELPI) and drug depositions were analysed using high performance liquid chromatography (HPLC). Results. All three actuator materials showed the same net charge trend across the five active drug ingredients, with BDP, BUD and FS showing positive net charges for both nylon and PTFE actuators, respectively. While SB and IPBr had significantly negative net charges across the three different actuators, which correlates to the ionic functional groups present on the drug molecule structures. Conclusions. The API present in a pMDI has a dominant effect on the electrostatic properties of the formulation, overcoming the charge effect arising from the actuator materials. Results have shown that the electrostatic charges for a solution-based pMDI could be related to the interactions of the chemical ingredients and change in the work function for the overall formulation
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