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
    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

    , Pages: 294-306, ISSN: 1865-0929

    © 2020, Springer Nature Switzerland AG. This paper shows that deep modelling of subtle changes of cardiac motion can help in automated diagnosis of early onset of cardiac disease. In this paper, we model left ventricular (LV) cardiac motion in MRI sequences, based on a hybrid spatio-temporal network. Temporal data over long time periods is used as inputs to the model and delivers a dense displacement field (DDF) for regional analysis of LV function. A segmentation mask of the end-diastole (ED) frame is deformed by the predicted DDF from which regional analysis of LV function endocardial radius, thickness, circumferential strain (Ecc) and radial strain (Err) are estimated. Cardiac motion is estimated over MR cine loops. We compare the proposed technique to two other deep learning-based approaches and show that the proposed approach achieves promising predicted DDFs. Predicted DDFs are estimated on imaging data from healthy volunteers and patients with primary pulmonary hypertension from the UK Biobank. Experiments demonstrate that the proposed methods perform well in obtaining estimates of endocardial radii as cardiac motion-characteristic features for regional LV analysis.

  • Journal article
    Suzuki H, Venkataraman AV, Bai W, Guitton F, Guo Y, Dehghan A, Matthews PMet al., 2019,

    Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance

    , JAMA Network Open, Vol: 2, Pages: 1-19, ISSN: 2574-3805

    Importance Identifying brain regions associated with risk factors for dementia could guide mechanistic understanding of risk factors associated with Alzheimer disease (AD).Objectives To characterize volume changes in brain regions associated with aging and modifiable risk factors for dementia (MRFD) and to test whether volume differences in these regions are associated with cognitive performance.Design, Setting, and Participants This cross-sectional study used data from UK Biobank participants who underwent T1-weighted structural brain imaging from August 5, 2014, to October 14, 2016. A voxelwise linear model was applied to test for regional gray matter volume differences associated with aging and MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use). The potential clinical relevance of these associations was explored by comparing their neuroanatomical distributions with the regional brain atrophy found with AD. Mediation models for risk factors, brain volume differences, and cognitive measures were tested. The primary hypothesis was that common, overlapping regions would be found. Primary analysis was conducted on April 1, 2018.Main Outcomes and Measures Gray matter regions that showed relative atrophy associated with AD, aging, and greater numbers of MRFD.Results Among 8312 participants (mean [SD] age, 62.4 [7.4] years; 3959 [47.1%] men), aging and 4 major MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use) had independent negative associations with specific gray matter volumes. These regions overlapped neuroanatomically with those showing lower volumes in participants with AD, including the posterior cingulate cortex, the thalamus, the hippocampus, and the orbitofrontal cortex. Associations between these MRFD and spatial memory were mediated by differences in posterior cingulate cortex volume (β = 0.0014; SE = 0.0006; P = .02).Conclusions and Relevance This cross-sectional study

  • Conference paper
    Halliday BP, Balaban G, Costa CM, Bai W, Porter B, Hatipoglu S, Fereira ND, Izgi C, Corden B, Tayal U, Ware JS, Plank G, Rinaldi CA, Rueckert D, Prasad SK, Bishop Met al., 2019,

    Improving Arrhythmic Risk Stratification in Non-Ischemic Dilated Cardiomyopathy Through the Evaluation of Novel Scar Characteristics Using CMR

    , Scientific Sessions of the American-Heart-Association, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
  • Journal article
    Rajpal H, Rosas De Andraca FE, Jensen HJ, 2019,

    Tangled worldview model of opinion dynamics

    , Frontiers in Physics, Vol: 7, ISSN: 2296-424X

    We study the joint evolution of worldviews by proposing a model of opinion dynamics, which is inspired in notions fromevolutionary ecology. Agents update their opinion on a specific issue based on their propensity to change – asserted by thesocial neighbours – weighted by their mutual similarity on other issues. Agents are, therefore, more influenced by neighbourswith similar worldviews (set of opinions on various issues), resulting in a complex co-evolution of each opinion. Simulationsshow that the worldview evolution exhibits events of intermittent polarization when the social network is scale-free. This, in turn,triggers extreme crashes and surges in the popularity of various opinions. Using the proposed model, we highlight the role ofnetwork structure, bounded rationality of agents, and the role of key influential agents in causing polarization and intermittentreformation of worldviews on scale-free networks.

  • Journal article
    Cofré R, Herzog R, Corcoran D, Rosas FEet al., 2019,

    A comparison of the maximum entropy principle across biological spatial scales

    , Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 21, Pages: 1-20, ISSN: 1099-4300

    Despite their differences, biological systems at different spatial scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are often hard to grasp due to the highly specialized nature of modern science and the parcelled terminology employed by various scientific sub-disciplines. To explore these common organizational features, this paper provides a comparative study of diverse applications of the maximum entropy principle, which has found many uses at different biological spatial scales ranging from amino acids up to societies. By presenting these studies under a common approach and language, this paper aims to establish a unified view over these seemingly highly heterogeneous scenarios.

  • Journal article
    Bhuva AN, Bai W, Lau C, Davies RH, Ye Y, Bulluck H, McAlindon E, Culotta V, Swoboda PP, Captur G, Treibel TA, Augusto JB, Knott KD, Seraphim A, Cole GD, Petersen SE, Edwards NC, Greenwood JP, Bucciarelli-Ducci C, Hughes AD, Rueckert D, Moon JC, Manisty CHet al., 2019,

    A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis

    , Circulation: Cardiovascular Imaging, Vol: 12, Pages: 1-11, ISSN: 1941-9651

    Background:Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent human inter- and intraobserver error, and ensuring that ML cannot demonstrate superiority. Measuring precision (scan:rescan reproducibility) addresses this. We compared precision of ML and humans using a multicenter, multi-disease, scan:rescan cardiovascular magnetic resonance data set.Methods:One hundred ten patients (5 disease categories, 5 institutions, 2 scanner manufacturers, and 2 field strengths) underwent scan:rescan cardiovascular magnetic resonance (96% within one week). After identification of the most precise human technique, left ventricular chamber volumes, mass, and ejection fraction were measured by an expert, a trained junior clinician, and a fully automated convolutional neural network trained on 599 independent multicenter disease cases. Scan:rescan coefficient of variation and 1000 bootstrapped 95% CIs were calculated and compared using mixed linear effects models.Results:Clinicians can be confident in detecting a 9% change in left ventricular ejection fraction, with greater than half of coefficient of variation attributable to intraobserver variation. Expert, trained junior, and automated scan:rescan precision were similar (for left ventricular ejection fraction, coefficient of variation 6.1 [5.2%–7.1%], P=0.2581; 8.3 [5.6%–10.3%], P=0.3653; 8.8 [6.1%–11.1%], P=0.8620). Automated analysis was 186× faster than humans (0.07 versus 13 minutes).Conclusions:Automated ML analysis is faster with similar precision to the most precise human techniques, even when challenged with real-world scan:rescan data. Assessment of multicenter, multi-vendor, multi-field strength scan:rescan data (available at permits a generalizable assessment of ML precision and may facili

  • Conference paper
    Chen C, Biffi C, Tarroni G, Petersen S, Bai W, Rueckert Det al., 2019,

    Learning shape priors for robust cardiac MR segmentation from multi-view images

    , International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 523-531, ISSN: 0302-9743

    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.

  • Journal article
    Balaban G, Halliday BP, Bai W, Porter B, Malvuccio C, Lamata P, Rinaldi CA, Plank G, Rueckert D, Prasad SK, Bishop MJet al., 2019,

    Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort.

    , PLoS Computational Biology, Vol: 15, Pages: 1-18, ISSN: 1553-734X

    This paper presents a morphological analysis of fibrotic scarring in non-ischemic dilated cardiomyopathy, and its relationship to electrical instabilities which underlie reentrant arrhythmias. Two dimensional electrophysiological simulation models were constructed from a set of 699 late gadolinium enhanced cardiac magnetic resonance images originating from 157 patients. Areas of late gadolinium enhancement (LGE) in each image were assigned one of 10 possible microstructures, which modelled the details of fibrotic scarring an order of magnitude below the MRI scan resolution. A simulated programmed electrical stimulation protocol tested each model for the possibility of generating either a transmural block or a transmural reentry. The outcomes of the simulations were compared against morphological LGE features extracted from the images. Models which blocked or reentered, grouped by microstructure, were significantly different from one another in myocardial-LGE interface length, number of components and entropy, but not in relative area and transmurality. With an unknown microstructure, transmurality alone was the best predictor of block, whereas a combination of interface length, transmurality and number of components was the best predictor of reentry in linear discriminant analysis.

  • Journal article
    Tiotiu A, Kermani NZ, Agapow P, Saqi M, Guo Y-K, Djukanovic R, Chung KF, Adcock IMet al., 2019,

    Differential macrophage activation in asthmatic sputum using U-BIOPRED transcriptomics

  • Journal article
    Kermani NZ, Pavlidis S, Riley JH, Chung FK, Adcock IM, Guo Y-Ket al., 2019,

    Prediction of longitudinal inflammatory phenotypes using baseline sputum transcriptomics in UBIOPRED

  • Journal article
    Cofré R, Videla L, Rosas F, 2019,

    An introduction to the non-equilibrium steady states of maximum entropy spike trains

    , Entropy, Vol: 21, Pages: 1-28, ISSN: 1099-4300

    Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.

  • Conference paper
    Karri SSK, Bach F, Pock T, 2019,

    Fast Decomposable Submodular Function Minimization using Constrained Total Variation

    , Neural Information Processing Systems, 2019

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