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

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

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

© 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

Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study

BACKGROUND: Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY: A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS: Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS: The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses

MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development

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

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