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• Journal article
  Zeyu T, Xing X, Wang G, Yang Get al., 2025,

  Enhancing super-resolution network efficacy in CT imaging: cost-effective simulation of training data

  , IEEE Open Journal of Engineering in Medicine and Biology, ISSN: 2644-1276

  Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or on sinogram reconstruction, which requires the release of raw data and complex reconstruction algorithms. Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms. The training pairs produced by our method closely resemble real data distributions (PSNR=49.74 vs. 40.66, p<0.05). A multivariate Cox regression analysis involving thick slice CT images with lung fibrosis revealed that only the radiomics features extracted using our method demonstrated a significant correlation with mortality (HR=1.19 and HR=1.14, p<0.005). This paper represents the first to identify and address the challenge of generating appropriate paired training data for Deep Learning-based CT SR models, which enhances the efficacy and applicability of SR models in real-world scenarios.

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• Journal article
  Vano LJ, McCutcheon RA, Sedlacik J, Rutigliano G, Kaar SJ, Finelli V, Lobo MC, Berry A, Statton B, Fazlollahi A, Everall IP, Howes ODet al., 2025,

  The role of low subcortical iron, white matter myelin, and oligodendrocytes in schizophrenia: a quantitative susceptibility mapping and diffusion tensor imaging study

  , MOLECULAR PSYCHIATRY, ISSN: 1359-4184
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• Journal article
  Vano LJ, McCutcheon RA, Sedlacik J, Kaar SJ, Rutigliano G, Nordio G, Finelli V, Townsend L, Berry A, Statton B, Fazlollahi A, Veronese M, Hopkins SC, Koblan KS, Everall IP, Howes ODet al., 2025,

  Reduced Brain Iron and Striatal Hyperdopaminergia in Schizophrenia: A Quantitative Susceptibility Mapping MRI and PET Study

  , AMERICAN JOURNAL OF PSYCHIATRY, Vol: 182, Pages: 830-839, ISSN: 0002-953X
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  • Citations: 3
• Journal article
  Ai R, Xiao X, Deng S, Yang N, Xing X, Watne LO, Selbæk G, Wedatilake Y, Xie C, Rubinsztein DC, Palmer JE, Neerland BE, Chen H, Niu Z, Yang G, Fang EFet al., 2025,

  Artificial intelligence in drug development for delirium and Alzheimer's disease.

  , Acta Pharm Sin B, Vol: 15, Pages: 4386-4410, ISSN: 2211-3835

  Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

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• Journal article
  Li S, Zhuang B, Cui C, He J, Ren Y, Wang H, Francone M, Yang G, Mohiaddin R, Lu M, Xu Let al., 2025,

  Prognostic significance of myocardial fibrosis in men with alcoholic cardiomyopathy: insights from cardiac MRI

  , EUROPEAN RADIOLOGY, Vol: 35, Pages: 5594-5603, ISSN: 0938-7994
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  • Citations: 1
• Conference paper
  Patel KHK, Bajaj N, Statton BK, Bishop MJ, Stoks J, Li X, Sau A, Brezitski M, Ware JS, Purkayastha S, O'Regan D, Coronel R, Cluitmans M, Lambiase PD, Ng FSet al., 2025,

  WEIGHT LOSS REVERSES ADVERSE STRUCTURAL, ELECTROPHYSIOLOGICAL AND AUTONOMIC REMODELLING IN OBESITY

  , Publisher: OXFORD UNIV PRESS, Pages: i3-i3, ISSN: 0032-5473
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• Journal article
  Toma T, Tekle K, Smith J, Quest R, Kelly R, Lord C, Alavi Aet al., 2025,

  Enhancing the detection of paediatric ankle fractures with zero echo time imaging: A case of an occult salter-harris III ankle fracture

  , EMERGENCY RADIOLOGY, ISSN: 1070-3004
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• Journal article
  Zhu J, Liao Y, Chen Y, Gao F, Li R, Yang G, Lai X, Chen Jet al., 2025,

  Multimodal MRI-Based Glioma Segmentation and MGMT Promoter Methylation Status Prediction Using Multitask Learning Architecture

  , INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Vol: 35, ISSN: 0899-9457
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• Journal article
  Zahid U, Osugo M, Selvaggi P, Lythgoe DJ, Fortunato C, Diederen K, Kiemes A, Wall MB, Whitehurst T, Onwordi EC, Statton B, Berry A, Dimitrov M, Lau R, McCutcheon RA, Murray R, Marques TR, Mehta MA, Howes ODet al., 2025,

  The effects of dopamine receptor antagonist and partial agonist antipsychotics on the glutamatergic system: double-blind, randomised, placebo-controlled <SUP>1</SUP>H-MRS cross-over study in healthy volunteers

  , BRITISH JOURNAL OF PSYCHIATRY, ISSN: 0007-1250
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• Journal article
  Jiang C, Xing X, Nan Y, Fang Y, Zhang S, Walsh S, Yang G, Shen Det al., 2025,

  A lung structure and function information-guided residual diffusion model for predicting idiopathic pulmonary fibrosis progression

  , MEDICAL IMAGE ANALYSIS, Vol: 103, ISSN: 1361-8415
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