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• Journal article
  Ma J, Jiang M, Fang X, Chen J, Wang Y, Yang Get al., 2026,

  Hybrid aggregation strategy with double inverted residual blocks for lightweight salient object detection

  , NEURAL NETWORKS, Vol: 194, ISSN: 0893-6080
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• Journal article
  Wang Z, Xiao M, Zhou Y, Wang C, Wu N, Li Y, Gong Y, Chang S, Chen Y, Zhu L, Zhou J, Cai C, Wang H, Jiang X, Guo D, Yang G, Qu Xet al., 2025,

  Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.

  , IEEE Trans Biomed Eng, Vol: 72, Pages: 3642-3654

  OBJECTIVE: Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. METHODS: We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. RESULTS: Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. CONCLUSION: DeepSSL is efficient under highly limited training data and adaptive to patients and prospective undersampling. SIGNIFICANCE: This approach holds promise in addressing the escalating demand for high-dimensional data reconstruction in MRI applications.

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• Journal article
  Jin W, Tian X, Wang N, Wu B, Shi B, Zhao B, Yang Get al., 2025,

  Representation-driven sampling and adaptive policy resetting for improving multi-Agent reinforcement learning

  , NEURAL NETWORKS, Vol: 192, ISSN: 0893-6080
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• Journal article
  Zhang S, Nan Y, Fang Y, Wang S, Liu Y, Papanastasiou G, Gao Z, Li S, Walsh S, Yang Get al., 2025,

  Dynamical multi-order responses and global semantic-infused adversarial learning: A robust airway segmentation method.

  , Med Image Anal, Vol: 108

  Automated airway segmentation in computerized tomography (CT) images is crucial for the accurate diagnosis of lung diseases. However, the scarcity of manual annotations hinders the efficacy of supervised learning, while unconstrained intensities and sample imbalance lead to discontinuity and false-negative issues. To address these challenges, we propose a novel airway segmentation model named Dynamical Multi-order responses and Global Semantic-infused Adversarial network (DMGSA), integrating the unsupervised and supervised learning in parallel to alleviate the label scarcity of airway. In the unsupervised branch, (1) we propose several novel strategies of Dynamic Mask-Ratio (DMR) to empower the model to perceive context information of varying sizes, mimicking the laws of human learning vividly; (2) we present a novel target of Multi-Order Normalized Responses (MONR), exploiting the distinct order exponential operation of raw images and oriented gradients to enhance the textural representations of bronchioles; (3) we introduce the Adversarial Learning (AL) on the top of MONR module to discern nuances between real and fake images, focusing on capturing the textural features of terminal bronchioles. For the supervised branch, we propose an innovative Generalized Mean pooling based Global Semantic-infused (GMGS) module to ulteriorly improve the robustness. Ultimately, we have verified the method performance and robustness by training on normal lung disease datasets, while testing on lung cancer, COVID-19 and Lung fibrosis datasets. All experimental results have proved that our method exceeds state-of-the-art methods significantly.

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• Journal article
  Connor S, Lally P, Pai I, Brnawi H, Touska P, Ourselin S, Hajnal JVet al., 2025,

  7-Tesla sodium magnetic resonance imaging of the inner ears in unilateral Ménière’s disease and endolymphatic hydrops: an exploratory study

  , BMC Medical Imaging, Vol: 25, ISSN: 1471-2342

  BackgroundWhilst delayed post-gadolinium MRI has led to a shift in the diagnostic paradigm of Meniere’s Disease (MD), there remains a strong desire to develop a non-contrast enhanced MRI technique to detect and monitor MD. The endolymphatic space (ES) undergoes hydropic expansion in Ménière’s Disease (MD) and the concentration of sodium ions in the endolymph is at least 10 times lower than that in the perilymph. It was hypothesised that the lower sodium (23Na) concentration in the endolymph relative to the surrounding perilymph would result in a differential reduction in 23Na-MRI signal in inner ears with endolymphatic hydrops (EH). This proof of principle study explored the feasibility of 7-Tesla (7T) 23Na-MRI to lateralise EH ears in unilateral MD.MethodsIn this prospective study, 7T 23Na-MRI was performed in participants with both unilateral definite MD and severe vestibulo-cochlear EH on a delayed post-gadolinium real inversion recovery sequence. Two blinded independent observers qualitatively graded the visibility and anatomical compatibility of inner ear 23Na MRI signal intensity (NaSI), before and after registering to 3D T2-weighted (T2w) MRI and determined the certainty of EH laterality. The internal auditory meatus (IAM), cochlea and vestibule were segmented using 3D Slicer and NaSI was quantified. Inner ear median NaSI were scaled to the adjacent IAM median NaSI and compared between the two ears.ResultsIn 4 unilateral MD participants (mean age 60.3 years, 2 men), both observers correctly predicted EH laterality in 1/4 before and 3/4 participants after fusion to 3D T2w MRI. There was no incorrect lateralisation of EH by either observer, either before or after registration and fusion. In the 3 participants correctly lateralised, quantitative analysis revealed the median inner ear NaSI scaled to the ipsilateral IAM was 1.2–2.8 times higher in the normal cochlea and 1.9–2.9 times higher in the vestibule, compared to

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• Journal article
  Halliday BP, Owen R, Ragavan A, Smith KL, Statton B, Berry A, Kasiakogias A, Tsoumani Z, Shanmugnathan M, Dungu JN, de Marvao A, Tayal U, Ware JS, O'Regan DP, Pennell DJ, Cleland JGF, Prasad SK, Gregson J, Murphy MP, Rider O, Valkovič Let al., 2025,

  A double-blind, randomised placebo-controlled trial examining the effect of MitoQ on myocardial energetics in patients with dilated cardiomyopathy.

  , Eur Heart J Cardiovasc Imaging
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• Journal article
  Tänzer M, Scott AD, Khalique Z, Molto M, Rajakulasingam R, Silva RD, Pennell DJ, Ferreira PF, Yang G, Rueckert D, Nielles-Vallespin Set al., 2025,

  Accelerating cDTI with deep learning-based tensor de-noising and breath hold reduction. a step towards improved efficiency and clinical feasibility

  , Journal of Cardiovascular Magnetic Resonance, ISSN: 1097-6647

  BackgroundCardiac Diffusion Tensor Imaging (cDTI) non-invasively provides unique insights into cardiac microstructure. Current protocols require multiple breath-hold repetitions to achieve adequate signal-to-noise ratio, resulting in lengthy scan times. The aim of this study was to develop a cDTI de-noising method that would enable the reduction of repetitions while preserving image quality.MethodsWe present a novel de-noising framework for cDTI acceleration centred on three fundamental advances: (1) a paradigm shift from image-based to tensor-space de-noising that better preserves structural information, (2) an ensemble of Vision Transformer-based models specifically optimised for tensor processing through adversarial training, and (3) a sophisticated data augmentation strategy that maximises training data utilisation through dynamic repetition selection.ResultsOur approach reduces scan times by a factor of up to 4 while achieving a 20% reduction in cDTI maps errors over existing de-noising methods (Table 1) and preserving anatomical features such as infarct characterisation and transmural cardiomyocyte orientation patterns. Crucially, our proposed method succeeds in clinical cases where other algorithms previously failed.ConclusionsThis demonstrates substantial improvements in cDTI acquisition efficiency, achieving up to 4-fold scan time reduction (3-5 breath-holds) while maintaining diagnostic accuracy across diverse cardiac pathologies.

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• Journal article
  Ai R, Mao L, Jin X, Campos-Marques C, Zhang S-Q, Pan J, Lagartos-Donate MJ, Cao S-Q, Barros-Santos B, Nobrega-Martins R, Katsaitis F, Yang G, Xie C, Kang X, Wang P, Novello M, Hu Y, Bergersen LH, Storm-Mathisen J, Kuroyanagi H, Escobar-Doncel B, Gonzalez NV, Chaudhry FA, Wang Z, Zhang Q, Lu G, Sotiropoulos I, Niu Z, Chen G, Nair RR, Silva JM, Luo OJ, Fang EFet al., 2025,

  NAD<SUP>+</SUP> reverses Alzheimer's neurological deficits via regulating differential alternative RNA splicing of <i>EVA1C</i>

  , SCIENCE ADVANCES, Vol: 11
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• Journal article
  Wang J, Ruan D, Li Y, Tan T, Wu L, Yang G, Jiang Met al., 2025,

  Dynamic mask stitching-guided region consistency for semi-supervised 3D medical image segmentation

  , EXPERT SYSTEMS WITH APPLICATIONS, Vol: 292, ISSN: 0957-4174
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• Journal article
  Fang EF, Fang Y, Chen G, Wang H-L, Zhang J, Wu C, Liao J, Xie C, Liu X, Wang K, Liu Y, Yang G, Wang Q, He L-T, Li J, Chen H-Z, Kang L, Jiang Y, Su H, Jiang H, He N, Tao J, Leng SX, Siow RC, Liu C, Khan HTA, Liu Y, Kato H, Sasaki T, Kim JI, Maier AB, Zhang L, Rasmussen LJ, Woo J, Wu J, Zou Het al., 2025,

  Adapting health, economic and social policies to address population aging in China

  , NATURE AGING, Vol: 5, Pages: 2176-2187
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