Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • Journal article
    Bhuva AN, Treibel TA, De Marvao A, Biffi C, Dawes TJW, Doumou G, Bai W, Patel K, Boubertakh R, Rueckert D, O'Regan DP, Hughes AD, Moon JC, Manisty CHet al., 2020,

    Sex and regional differences inmyocardial plasticity in aortic stenosis are revealed by 3D modelmachine learning

    , EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 21, Pages: 417-427, ISSN: 2047-2404
  • Journal article
    Dur TH, Arcucci R, Mottet L, Molina Solana M, Pain C, Guo Y-Ket al., 2020,

    Weak Constraint Gaussian Processes for optimal sensor placement

    , JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 42, ISSN: 1877-7503
  • Journal article
    Jolliffe DA, Stefanidis C, Wang Z, Kermani NZ, Dimitrov V, White JH, McDonough JE, Janssens W, Pfeffer P, Griffiths CJ, Bush A, Guo Y, Christenson S, Adcock IM, Chung KF, Thummel KE, Martineau ARet al., 2020,

    Vitamin D metabolism is dysregulated in asthma and chronic obstructive pulmonary disease.

    , American Journal of Respiratory and Critical Care Medicine, Vol: 202, Pages: 371-382, ISSN: 1073-449X

    RATIONALE: Vitamin D deficiency is common in patients with asthma and COPD. Low 25-hydroxyvitamin D (25[OH]D) levels may represent a cause or a consequence of these conditions. OBJECTIVE: To determine whether vitamin D metabolism is altered in asthma or COPD. METHODS: We conducted a longitudinal study in 186 adults to determine whether the 25(OH)D response to six oral doses of 3 mg vitamin D3, administered over one year, differed between those with asthma or COPD vs. controls. Serum concentrations of vitamin D3, 25(OH)D3 and 1α,25-dihydroxyvitamin D3 (1α,25[OH]2D3) were determined pre- and post-supplementation in 93 adults with asthma, COPD or neither condition, and metabolite-to-parent compound molar ratios were compared between groups to estimate hydroxylase activity. Additionally, we analyzed fourteen datasets to compare expression of 1α,25[OH]2D3-inducible gene expression signatures in clinical samples taken from adults with asthma or COPD vs. controls. MEASUREMENTS AND MAIN RESULTS: The mean post-supplementation 25(OH)D increase in participants with asthma (20.9 nmol/L) and COPD (21.5 nmol/L) was lower than in controls (39.8 nmol/L; P=0.001). Compared with controls, patients with asthma and COPD had lower molar ratios of 25(OH)D3-to-vitamin D3 and higher molar ratios of 1α,25(OH)2D3-to-25(OH)D3 both pre- and post-supplementation (P≤0.005). Inter-group differences in 1α,25[OH]2D3-inducible gene expression signatures were modest and variable where statistically significant. CONCLUSIONS: Attenuation of the 25(OH)D response to vitamin D supplementation in asthma and COPD associated with reduced molar ratios of 25(OH)D3-to-vitamin D3 and increased molar ratios of 1α,25(OH)2D3-to-25(OH)D3 in serum, suggesting that vitamin D metabolism is dysregulated in these conditions.

  • Journal article
    Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert Det al., 2020,

    Deep learning for cardiac image segmentation: A review

    , Frontiers in Cardiovascular Medicine, Vol: 7, Pages: 1-33, ISSN: 2297-055X

    Deep learning has become the most widely used approach for cardiac imagesegmentation in recent years. In this paper, we provide a review of over 100cardiac image segmentation papers using deep learning, which covers commonimaging modalities including magnetic resonance imaging (MRI), computedtomography (CT), and ultrasound (US) and major anatomical structures ofinterest (ventricles, atria and vessels). In addition, a summary of publiclyavailable cardiac image datasets and code repositories are included to providea base for encouraging reproducible research. Finally, we discuss thechallenges and limitations with current deep learning-based approaches(scarcity of labels, model generalizability across different domains,interpretability) and suggest potential directions for future research.

  • Journal article
    Wu P, Sun J, Chang X, Zhang W, Arcucci R, Guo Y, Pain CCet al., 2020,

    Data-driven reduced order model with temporal convolutional neural network

    , COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 360, ISSN: 0045-7825
  • Journal article
    Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King APet al., 2020,

    Fully automated, quality-controlled cardiac analysis from CMR: Validation and large-scale application to characterize cardiac function

    , JACC: Cardiovascular Imaging, Vol: 13, Pages: 684-695, ISSN: 1876-7591

    OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. BACKGROUND: Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. METHODS: The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps' ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. RESULTS: Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. CONCLUSIONS: The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.

  • Journal article
    Dewey M, Siebes M, Kachelrieß M, Kofoed KF, Maurovich-Horvat P, Nikolaou K, Bai W, Kofler A, Manka R, Kozerke S, Chiribiri A, Schaeffter T, Michallek F, Bengel F, Nekolla S, Knaapen P, Lubberink M, Senior R, Tang M-X, Piek JJ, van de Hoef T, Martens J, Schreiber L, Quantitative Cardiac Imaging Study Groupet al., 2020,

    Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia

    , Nature Reviews Cardiology, Vol: 17, Pages: 427-450, ISSN: 1759-5002

    Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.

  • Journal article
    Tarroni G, Bai W, Oktay O, Schuh A, Suzuki H, Glocker B, Matthews P, Rueckert Det al., 2020,

    Large-scale quality control of cardiac imaging in population studies: application to UK Biobank

    , Scientific Reports, Vol: 10, ISSN: 2045-2322

    In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment isunfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) imagesto the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics(heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factorsincluding acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage(i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of thestacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slicemotion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastoliccardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involvedin UKBB CMR acquisition and for the ones who use the dataset for research purposes.

  • Conference paper
    Chen C, Ouyang C, Tarroni G, Schlemper J, Qiu H, Bai W, Rueckert Det al., 2020,

    Unsupervised multi-modal style transfer for cardiac MR segmentation

    , MICCAI STACOM Workshop, Publisher: Springer International Publishing, Pages: 209-219, ISSN: 0302-9743

    In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.

  • Conference paper
    Lu P, Qiu H, Qin C, Bai W, Rueckert D, Noble JAet al., 2020,

    Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features

    , 24th Conference on Medical Image Understanding and Analysis (MIUA), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 294-306, ISSN: 1865-0929
  • Book chapter
    Arcucci R, Moutiq L, Guo Y-K, 2020,

    Neural Assimilation

    , Editors: Krzhizhanovskaya, Zavodszky, Lees, Dongarra, Sloot, Brissos, Teixeira, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 155-168, ISBN: 978-3-030-50432-8
  • Conference paper
    Arcucci R, Mottet L, Casas CAQ, Guitton F, Pain C, Guo Y-Ket al., 2020,

    Adaptive Domain Decomposition for Effective Data Assimilation

    , 25th International Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 583-595, ISSN: 0302-9743
  • Conference paper
    Nadler P, Arcucci R, Guo Y-K, 2020,

    A Scalable Approach to Econometric Inference

    , Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 59-68, ISSN: 0927-5452
  • Conference paper
    Arcucci R, Casas CQ, Xiao D, Mottet L, Fang F, Wu P, Pain C, Guo Y-Ket al., 2020,

    A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA)

    , Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 189-198, ISSN: 0927-5452
  • Conference paper
    Nadler P, Arcucci R, Guo Y, 2020,

    An Econophysical Analysis of the Blockchain Ecosystem

    , Pages: 27-42, ISSN: 2198-7246

    We propose a novel modelling approach for the cryptocurrency ecosystem. We model on-chain and off-chain interactions as econophysical systems and employ methods from physical sciences to conduct interpretation of latent parameters describing the cryptocurrency ecosystem as well as to generate predictions. We work with an extracted dataset from the Ethereum blockchain which we combine with off-chain data from exchanges. This allows us to study a large part of the transaction flows related to the cryptocurrency ecosystem. From this aggregate system view we deduct that movements on the blockchain and price and trading action on exchanges are interrelated. The relationship is one directional: On-chain token flows towards exchanges have little effect on prices and trading volume, but changes in price and volume affect the flow of tokens towards the exchange.

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