Search or filter publications

Filter by type:

Filter by publication type

Filter by year:



  • Showing results for:
  • Reset all filters

Search results

  • 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
    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
  • Journal article
    Zhou Z, Wang R, Wang H, Liu Y, Lu D, Sun Z, Yang G, Xu Let al., 2021,

    Myocardial extracellular volume fraction quantification in an animal model of the doxorubicin-induced myocardial fibrosis: a synthetic hematocrit method using 3T cardiac magnetic resonance

    , Quantitative imaging in medicine and surgery, Vol: 11, Pages: 510-520, ISSN: 2223-4292

    Background: Visualization of diffuse myocardial fibrosis is challenging and mainly relies on histology.Cardiac magnetic resonance (CMR), which uses extracellular contrast agents, is a rapidly developingtechnique for measuring the extracellular volume (ECV). The objective of this study was to evaluate thefeasibility of the synthetic myocardial ECV fraction based on 3.0 T CMR compared with the conventionalECV fraction.Methods: This study was approved by the local animal care and ethics committee. Fifteen beagle modelswith diffuse myocardial fibrosis, including 12 experimental and three control subjects, were generatedby injecting doxorubicin 30 mg/m2 intravenously every three weeks for 24 weeks. Short-axis (SAX) and4-chamber long-axis (LAX) T1 maps were acquired for both groups. The association between hematocrit(Hct) and native T1blood was derived from 9 non-contrast CMR T1 maps of 3 control beagles using regressionanalysis. Synthetic ECV was then calculated using the synthetic Hct and compared with conventional ECVat baseline and the 16th and 24th week after doxorubicin administration. The collagen volume fraction (CVF)value was measured on digital biopsy samples. Bland-Altman plots were used to analyze the agreementbetween conventional and synthetic ECV. Correlation analyses were performed to explore the associationamong conventional ECV, synthetic ECV, CVF, and left ventricular ejection fraction (LVEF).Results: The regression model synthetic Hct = 816.46*R1blood − 0.01 (R2=0.617; P=0.012) was used topredict the Hct from native T1blood values. The conventional and synthetic ECV fractions of experimentalanimals at the 16th and 24th week after modeling were significantly higher than those measured at the baseline(31.4%±2.2% and 36.3%±2.1% vs. 22.9%±1.7%; 29.9%±2.4% and 36.1%±2.6% vs. 22.0%±2.4%; all withP<0.05). Bland-Altman plots showed a bias (1.0%) between conventional and synthetic ECV with 95% limitsof agreement

  • Journal article
    Lv J, Wang C, Yang G, 2021,

    PIC-GAN: a parallel imaging coupled generative adversarial network for accelerated multi-channel MRI reconstruction

    , Diagnostics, Vol: 11, ISSN: 2075-4418

    In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10−5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other c

  • Journal article
    Jin Y, Yang G, Fang Y, Li R, Xu X, Liu Y, Lai Xet al., 2021,

    3D PBV-Net: An automated prostate MRI data segmentation method

  • Journal article
    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

    , Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol: 11597, ISSN: 1605-7422

    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
    Zhang N, Yang G, Zhang W, Wang W, Zhou Z, Zhang H, Xu L, Chen Yet al., 2021,

    Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications

    , European Journal of Radiology, Vol: 134, ISSN: 0720-048X

    ObjectivesTo develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores (CACS) quantification via deep learning on nonenhanced cardiac CT images.MethodsIn this retrospective single-centre study, a multi-task deep learning framework was proposed to detect and quantify coronary artery calcification from CT images collected between October 2018 and March 2019. A total of 232 non-contrast cardiac-gated CT scans were retrieved and studied (80 % for model training and 20 % for testing). CACS results of testing datasets (n = 46), including Agatston score, calcium volume score, calcium mass score, were calculated fully automatically and manually at total and vessel-specific levels, respectively.ResultsNo significant differences were found in CACS quantification obtained using automatic or manual methods at total and vessel-specific levels (Agatston score: automatic 535.3 vs. manual 542.0, P = 0.993; calcium volume score: automatic 454.2 vs. manual 460.6, P = 0.990; calcium mass score: automatic 128.9 vs. manual 129.5, P = 0.992). Compared to the ground truth, the number of calcified vessels can be accurate recognized automatically (total: automatic 107 vs. manual 102, P = 0.125; left main artery: automatic 15 vs. manual 14, P = 1.000 ; left ascending artery: automatic 37 vs. manual 37, P = 1.000; left circumflex artery: automatic 22 vs. manual 20, P = 0.625; right coronary artery: automatic 33 vs. manual 31, P = 0.500). At the patient’s level, there was no statistic difference existed in the classification of Agatston scoring (P = 0.317) and the number of calcified vessels (P = 0.102) between the automatic and manual results.ConclusionsThe proposed framework can achieve reliable and comprehensive quantification for the CACS, including the calcified extent and distr

  • Journal article
    Litvinukova M, Talavera-Lopez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M, Nadelmann ER, Roberts K, Tuck L, Fasouli ES, DeLaughter DM, McDonough B, Wakimoto H, Gorham JM, Samari S, Mahbubani KT, Saeb-Parsy K, Patone G, Boyle JJ, Zhang H, Zhang H, Viveiros A, Oudit GY, Bayraktar OA, Seidman JG, Seidman CE, Noseda M, Hubner N, Teichmann SAet al., 2020,

    Cells of the adult human heart

    , Nature, Vol: 588, Pages: 466-472, ISSN: 0028-0836

    Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and therapeutic strategies require deeper understanding of the healthy heart’s molecular processes. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavor. Here, using state-of-the-art analyses of large-scale single-cell and nuclei transcriptomes, we characterise six anatomical adult heart regions. Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes, and fibroblasts, revealing distinct atrial and ventricular subsets with diverse developmental origins and specialized properties. We define the complexity of the cardiac vasculature and its changes along the arterio-venous axis. In the immune compartment we identify cardiac resident macrophages with inflammatory and protective transcriptional signatures. Further, inference of cell-cell interactions highlight different macrophage-fibroblast-cardiomyocyte networks between atria and ventricles that are distinct from skeletal muscle. Our human cardiac cell atlas improves our understanding of the human heart and provides a healthy reference for future studies.

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

    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, 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.

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=1161&limit=10&respub-action=search.html Current Millis: 1618478042131 Current Time: Thu Apr 15 10:14:02 BST 2021