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• Journal article
  Wang C, Jiang M, Li Y, Wei B, Li Y, Wang P, Yang Get al., 2025,

  MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation

  , COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol: 260, ISSN: 0169-2607
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  • Citations: 1
• Journal article
  Ristic M, Chappell KE, Lanz H, McGinley JVM, Gupte C, Amiras Det al., 2025,

  First in-vivo magic angle directional imaging using dedicated low-field MRI

  , Magnetic Resonance in Medicine, Vol: 93, Pages: 1077-1089, ISSN: 0740-3194

  Purpose: To report the first in-vivo results from exploiting the magic angle effect, using a dedicated low-field MRI scanner that can be rotated about two axes. The Magic Angle Directional Imaging (MADI) method is used to depict collagen microstructures with 3D collagen tractography of knee ligaments and the meniscus. Methods: A novel low-field MRI system was developed, based on a transverse field open magnet, where the magnet can be rotated about two orthogonal. Sets of volume scans at various orientations were obtained in healthy volunteers. The experiments focused on the anterior cruciate ligament (ACL) and the meniscus of the knee. The images were co-registered, anatomical regions of interest (RoI) were selected and the collagen fiber orientations in each voxel were estimated from the observed image intensity variations. The 3D collagen tractography was superimposed on conventional volume images. Results: The MADI method was successfully employed for the first time producing in-vivo results comparable to those previously reported for excised animal specimens using conventional MRI. Tractography plots were generated for the ACL and the menisci. These results are consistent with the known microstructure of collagen fibers in these tissues. Conclusion: Images obtained using low-field MRI with 1 mm3 resolution were of sufficient quality for the MADI method, which was shown to produce high quality in-vivo information of collagen microstructures. This was achieved using a cost effective and sustainable low-field magnet making the technique potentially accessible and scalable, potentially changing the way we image injuries or disease in joints.

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• Journal article
  Wang Z, Wang F, Qin C, Lyu J, Ouyang C, Wang S, Li Y, Yu M, Zhang H, Guo K, Shi Z, Li Q, Xu Z, Zhang Y, Li H, Hua S, Chen B, Sun L, Sun M, Li Q, Chu Y-H, Bai W, Qin J, Zhuang X, Prieto C, Young A, Markl M, Wang H, Wu L-M, Yang G, Qu X, Wang Cet al., 2025,

  CMRxRecon2024: A Multimodality, Multiview k-Space Dataset UniversalMachine for Accelerated Cardiac MRI

  , RADIOLOGY-ARTIFICIAL INTELLIGENCE, Vol: 7, ISSN: 2638-6100
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  • Citations: 4
• Journal article
  Tang N, Liao Y, Chen Y, Yang G, Lai X, Chen Jet al., 2025,

  RVM plus : An AI-Driven Vision Sensor Framework for High-Precision, Real-Time Video Portrait Segmentation with Enhanced Temporal Consistency and Optimized Model Design

  , SENSORS, Vol: 25
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• Journal article
  Osugo M, Wall MB, Selvaggi P, Zahid U, Finelli V, Chapman GE, Whitehurst T, Onwordi EC, Statton B, McCutcheon RA, Murray RM, Marques TR, Mehta MA, Howes Oet al., 2025,

  Striatal dopamine D2/D3 receptor regulation of human reward processing and behaviour

  , Nature Communications, Vol: 16, ISSN: 2041-1723

  Signalling at dopamine D2/D3 receptors is thought to underlie motivated behaviour, pleasure experiences and emotional expression based on animal studies, but it is unclear if this is the case in humans or how this relates to neural processing of reward stimuli. Using a randomised, double-blind, placebo-controlled, crossover neuroimaging study, we show in healthy humans that sustained dopamine D2/D3 receptor antagonism for 7 days results in negative symptoms (impairments in motivated behaviour, hedonic experience, verbal and emotional expression) and that this is related to blunted striatal response to reward stimuli. In contrast, 7 days of partial D2/D3 agonism does not disrupt reward signalling, motivated behaviour or hedonic experience. Both D2/D3 antagonism and partial agonism induce motor impairments, which are not related to striatal reward response. These findings identify a central role for D2/D3 signalling and reward processing in the mechanism underlying motivated behaviour and emotional responses in humans, with implications for understanding neuropsychiatric disorders such as schizophrenia and Parkinson’s disease.

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• Journal article
  Xing X, Shi F, Huang J, Wu Y, Nan Y, Zhang S, Fang Y, Roberts M, Schonlieb C-B, Del Ser J, Yang Get al., 2025,

  On the caveats of AI autophagy

  , Nature Machine Intelligence, Vol: 7, Pages: 172-180, ISSN: 2522-5839

  Generative artificial intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimize training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimize outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabelled synthetic data. This trend, known as the AI autophagy phenomenon, suggests a future where generative AI systems may increasingly consume their own outputs without discernment, raising concerns about model performance, reliability and ethical implications. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? To address these research questions, this Perspective examines the existing literature, delving into the consequences of AI autophagy, analysing the associated risks and exploring strategies to mitigate its impact. Our aim is to provide a comprehensive perspective on this phenomenon advocating for a balanced approach that promotes the sustainable development of generative AI technologies in the era of large models.

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• Journal article
  Rajakulasingam R, Ferreira P, Scott A, Khalique Z, Azzu A, Molto M, Conway M, Falaschetti E, Cheng K, Hammersley D, Cantor E, Tindale A, Beattie C, Banerjee A, Wage R, Soundarajan RK, Dalby M, Nielles-Vallespin S, Pennell D, De Silva Pet al., 2025,

  Characterization of dynamic changes in cardiac microstructure after reperfused ST-elevation myocardial infarction by biphasic diffusion tensor cardiovascular magnetic resonance

  , European Heart Journal, Vol: 46, Pages: 454-469, ISSN: 0195-668X

  Background and Aims: Microstructural disturbances underlie dysfunctional contraction and adverse left ventricular (LV) remodelling after ST-elevation myocardial infarction (STEMI). Biphasic diffusion tensor cardiovascular magnetic resonance (DT-CMR) quantifies dynamic reorientation of sheetlets (E2A) from diastole to systole during myocardial thickening, and markers of tissue integrity (mean diffusivity [MD] and fractional anisotropy [FA]). This study investigated whether microstructural alterations identified by biphasic DT-CMR: (i) enable contrast-free detection of acute myocardial infarction (MI); (ii) associate with severity of myocardial injury and contractile dysfunction; and (iii) predict adverse LV remodelling. Methods: Biphasic DT-CMR was acquired 4 days (n=70) and 4 months (n=66) after reperfused STEMI and in healthy volunteers (HVOLs) (n=22). Adverse LV remodelling was defined as an increase in LV end-diastolic volume ≥20% at 4 months. MD and FA maps were compared with late gadolinium enhancement images. Results: Widespread microstructural disturbances were detected post-STEMI. In the acute MI zone, diastolic E2A was raised and systolic E2A reduced, resulting in reduced E2A mobility (all p<0.001 vs adjacent and remote zones and HVOLs). Acute global E2A mobility was the only independent predictor of adverse LV remodelling (odds ratio 0.77; 95% confidence interval 0.63–0.94; p=0.010). MD and FA maps had excellent sensitivity and specificity (all >90%) and inter-observer agreement for detecting MI presence and location.Conclusions: Biphasic DT-CMR identifies microstructural alterations in both diastole and systole after STEMI, enabling detection of MI presence and location as well as predicting adverse LV remodelling. DT-CMR has potential to provide a single contrast-free modality for MI detection and prognostication of patients after acute STEMI.

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• Journal article
  Wu W, Long Y, Gao Z, Yang G, Cheng F, Zhang Jet al., 2025,

  Multi-Level Noise Sampling From Single Image for Low-Dose Tomography Reconstruction

  , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 29, Pages: 1256-1268, ISSN: 2168-2194
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• Journal article
  Guiot J, Henket M, Gester F, André B, Ernst B, Frix A-N, Smeets D, Van Eyndhoven S, Antoniou K, Conemans L, Gote-Schniering J, Slabbynck H, Kreuter M, Sellares J, Tomos I, Yang G, Ribbens C, Louis R, Cottin V, Tomassetti S, Smith V, Walsh SLFet al., 2025,

  Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.

  , Respir Res, Vol: 26

  BACKGROUND: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD. METHODS: We evaluated the potential of an automated ILD quantification system (icolung®) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD. We used a retrospective cohort of 75 SSc-ILD patients to evaluate the potential of the AI-based quantification tool and to correlate image-based quantification with pulmonary function tests and their evolution over time. RESULTS: We evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36-8.12)* vs. 0.59 (0.09-3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively). CONCLUSION: AI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient's outcome and in treatment management.

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• Journal article
  Zhang S-Q, Niu Z, Anisimov A, Shi F, Deng S, Xiao X, Cao S-Q, Pan J-P, Wang H-L, Lagartos-Donate MJ, Bozbas NG, Wang P-J, Ai R, Li Y, Yang G, Lautrup S, Fang EFet al., 2025,

  NR1D1 Inhibition Enhances Autophagy and Mitophagy in Alzheimer's Disease Models

  , Aging and Disease, ISSN: 2152-5250
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