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  • Journal article
    Liu T, Gao Y, Wang H, Zhou Z, Wang R, Chang S, Liu Y, Sun Y, Rui H, Yang G, Firmin D, Dong J, Xu Let al., 2021,

    Association between right ventricular strain and outcomes in patients with dilated cardiomyopathy

    , Heart, Vol: 107, Pages: 1233-1239, ISSN: 1355-6037

    Objective To explore the association between three-dimensional (3D) cardiac magnetic resonance (CMR) feature tracking (FT) right ventricular peak global longitudinal strain (RVpGLS) and major adverse cardiovascular events (MACEs) in patients with stage C or D heart failure (HF) with non-ischaemic dilated cardiomyopathy (NIDCM) but without atrial fibrillation (AF).Methods Patients with dilated cardiomyopathy were enrolled in this prospective cohort study. Comprehensive clinical and biochemical analysis and CMR imaging were performed. All patients were followed up for MACEs.Results A total of 192 patients (age 53±14 years) were eligible for this study. A combination of cardiovascular death and cardiac transplantation occurred in 18 subjects during the median follow-up of 567 (311, 920) days. Brain natriuretic peptide, creatinine, left ventricular (LV) end-diastolic volume, LV end-systolic volume, right ventricular (RV) end-diastolic volume and RVpGLS from CMR were associated with the outcomes. The multivariate Cox regression model adjusting for traditional risk factors and CMR variables detected a significant association between RVpGLS and MACEs in patients with stage C or D HF with NIDCM without AF. Kaplan-Meier analysis based on RVpGLS cut-off value revealed that patients with RVpGLS <−8.5% showed more favourable clinical outcomes than those with RVpGLS ≥−8.5% (p=0.0037). Subanalysis found that this association remained unchanged.Conclusions RVpGLS-derived from 3D CMR FT is associated with a significant prognostic impact in patients with NIDCM with stage C or D HF and without AF.

  • Journal article
    Lv J, Zhu J, Yang G, 2021,

    Which GAN? A comparative study of generative adversarial network (GAN) based fast MRI reconstruction

    , Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 379, Pages: 1-17, ISSN: 1364-503X

    Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. K-space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of k-space data can result in blurring and aliasing artefacts for the reconstructed images. Recently, several studies have been proposed to use deep learning based data-driven models for MRI reconstruction and have obtained promising results. However, the comparison of these methods remains limited because the models have not been trained on the same datasets and the validation strategies may bedifferent. The purpose of this work is to conduct a comparative study to investigate the generative adversarial network (GAN) based models for MRI reconstruction. We reimplemented and benchmarked four widely used GAN based architectures including DAGAN, ReconGAN, RefineGAN and KIGAN. These four frameworks were trained and tested on brain, knee and liver MRI images using 2, 4 and 6- fold accelerations with a random undersampling mask. Both quantitative evaluations and qualitative visualisation have shown that the RefineGAN method has achieved superior performance in reconstruction with better accuracy and perceptual quality compared to other GAN based methods.

  • Journal article
    Porter A, Youngstein T, Babar S, Mason JCet al., 2021,

    A rare life-threatening presentation of Takayasu arteritis

    , RHEUMATOLOGY, Vol: 60, Pages: 6-8, ISSN: 1462-0324
  • Journal article
    Walter E, Ge Y, Mason J, Boyle J, Long Net al., 2021,

    A coumarin-porphyrin FRET break-apart probe for heme oxygenase-1

    , Journal of the American Chemical Society, Vol: 143, Pages: 6460-6469, ISSN: 0002-7863

    Heme oxygenase-1 (HO-1) is a vital enzyme in humans that primarily regulates free heme concentrations. The overexpression of HO-1 is commonly associated with cardiovascular and neurodegenerative diseases including atherosclerosis and ischemic stroke. Currently, there are no known chemical probes to detect HO-1 activity, limiting its potential as an early diagnostic/prognostic marker in these serious diseases. Reported here are the design, synthesis, and photophysical and biological characterization of a coumarin–porphyrin FRET break-apart probe to detect HO-1 activity, Fe–L1. We designed Fe–L1 to “break-apart” upon HO-1-catalyzed porphyrin degradation, perturbing the efficient FRET mechanism from a coumarin donor to a porphyrin acceptor fluorophore. Analysis of HO-1 activity using Escherichia coli lysates overexpressing hHO-1 found that a 6-fold increase in emission intensity at 383 nm was observed following incubation with NADPH. The identities of the degradation products following catabolism were confirmed by MALDI-MS and LC–MS, showing that porphyrin catabolism was regioselective at the α-position. Finally, through the analysis of Fe–L2, we have shown that close structural analogues of heme are required to maintain HO-1 activity. It is anticipated that this work will act as a foundation to design and develop new probes for HO-1 activity in the future, moving toward applications of live fluorescent imaging.

  • Journal article
    Boyle J, Seneviratne A, Cave L, Hyde G, Moestrup SK, Carling D, Mason JC, Haskard DOet al., 2021,

    Metformin directly suppresses atherosclerosis in normoglycemic mice via haematopoietic Adenosine Monophosphate-Activated Protein Kinase (AMPK)

    , Cardiovascular Research, Vol: 117, Pages: 1295-1308, ISSN: 0008-6363

    AimsAtherosclerotic vascular disease has an inflammatory pathogenesis. Heme from intraplaque hemorrhage may drive a protective and pro-resolving macrophage M2-like phenotype, Mhem, via AMPK and ATF1. The anti-diabetic drug metformin may also activate AMPK-dependent signalling.HypothesisMetformin systematically induces atheroprotective genes in macrophages via AMPK and ATF1, and thereby suppresses atherogenesis.Methods and ResultsNormoglycemic Ldlr-/- hyperlipidemic mice were treated with oral metformin, which profoundly suppressed atherosclerotic lesion development (p < 5x10−11). Bone marrow transplantation from AMPK-deficient mice demonstrated that metformin-related atheroprotection required haematopoietic AMPK (ANOVA, p < 0.03). Metformin at a clinically relevant concentration (10μM) evoked AMPK-dependent and ATF1-dependent increases in Hmox1, Nr1h2 (Lxrb), Abca1, Apoe, Igf1 and Pdgf, increases in several M2-markers and decreases in Nos2, in murine bone marrow macrophages. Similar effects were seen in human blood-derived macrophages, in which metformin induced protective genes and M2-like genes, suppressible by si-ATF1-mediated knockdown. Microarray analysis comparing metformin with heme in human macrophages indicated that the transcriptomic effects of metformin were related to those of heme, but not identical. Metformin induced lesional macrophage expression of p-AMPK, p-ATF1 and downstream M2-like protective effects.ConclusionMetformin activates a conserved AMPK-ATF1-M2-like pathway in mouse and human macrophages, and results in highly suppressed atherogenesis in hyperlipidemic mice via haematopoietic AMPK.Translational perspectiveThe work shows that oral antidiabetic drug metformin may suppress atherosclerotic lesion development via hematopoietic AMPK at clinically relevant concentrations, rather than via a hypoglycemic effect. Activating Transcription Factor 1 (ATF1) may mediate induction of key atheroprotective genes

  • Journal article
    Wang C, Yang G, Papanastasiou G, Tsaftaris S, Newby D, Gray C, Macnaught G, MacGillivray Tet al., 2021,

    DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

    , Information Fusion, Vol: 67, Pages: 147-160, ISSN: 1566-2535

    Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.

  • Journal article
    Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L, Weir-McCall JR, Teng Z, Gkrania-Klotsas E, Ruggiero A, Korhonen A, Jefferson E, Ako E, Langs G, Gozaliasl G, Yang G, Prosch H, Preller J, Stanczuk J, Tang J, Hofmanninger J, Babar J, Sánchez LE, Thillai M, Gonzalez PM, Teare P, Zhu X, Patel M, Cafolla C, Azadbakht H, Jacob J, Lowe J, Zhang K, Bradley K, Wassin M, Holzer M, Ji K, Ortet MD, Ai T, Walton N, Lio P, Stranks S, Shadbahr T, Lin W, Zha Y, Niu Z, Rudd JHF, Sala E, Schönlieb CBet al., 2021,

    Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

    , Nature Machine Intelligence, Vol: 3, Pages: 199-217

    Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

  • Conference paper
    Wu Y, Hatipoglu S, Alonso-Álvarez D, Gatehouse P, Firmin D, Keegan J, Yang Get al., 2021,

    Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR

    , Medical Imaging 2021: Computer-Aided Diagnosis, Publisher: SPIE, Pages: 1-7

    Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel automated framework that improves the standard U-Net based methods on these CMR multi-channel data (magnitude and phase) by cross-channel fusion with attention module and shape information based post-processing to achieve accurate delineation of both epicardium and endocardium contours. To evaluate the results, we employ the widely used Dice scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows enhanced performance compared to standard UNet based networks trained with single-channel data. Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.

  • Journal article
    Yu C, Gao Z, Zhang W, Yang G, Zhao S, Zhang H, Zhang Y, Li Set al., 2021,

    Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping

  • Journal article
    Uy CP, Tarkin JM, Gopalan D, Barwick TD, Tombetti E, Youngstein T, Mason JCet al., 2021,

    The impact of integrated non-invasive imaging in the management of takayasu arteritis

    , JACC: Cardiovascular Imaging, Vol: 14, Pages: 495-500, ISSN: 1876-7591

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