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• Journal article
  Inglese M, Conti A, Toschi N, 2025,

  Radiomics across modalities: a comprehensive review of neurodegenerative diseases

  , CLINICAL RADIOLOGY, Vol: 85, ISSN: 0009-9260
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  • Citations: 2
• Journal article
  DallArmellina E, Ennis DB, Axel L, Croisille P, Ferreira PF, Gotschy A, Lohr D, Moulin K, Nguyen C, Nielles-Vallespin S, Romero W, Scott AD, Stoeck C, Teh I, Tunnicliffe L, Viallon M, Wang, Young AA, Schneider JE, Sosnovik DEet al., 2025,

  Cardiac diffusion-weighted and tensor imaging: a Society for Cardiovascular Magnetic Resonance (SCMR) special interest group consensus statement

  , Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647

  Thanks to recent developments in Cardiovascular magnetic resonance (CMR), cardiac diffusion-weighted magnetic resonance is fast emerging in a range of clinical applications. Cardiac diffusion-weighted imaging (cDWI) and diffusion tensor imaging (cDTI) now enable investigators and clinicians to assess and quantify the 3D microstructure of the heart. Free-contrast DWI is uniquely sensitized to the presence and displacement of water molecules within the myocardial tissue, including the intra-cellular, extra-cellular and intra-vascular spaces. CMR can determine changes in microstructure by quantifying: a) mean diffusivity (MD) –measuring the magnitude of diffusion; b) fractional anisotropy (FA) – specifying the directionality of diffusion; c) helix angle (HA) and transverse angle (TA) –indicating the orientation of the cardiomyocytes; d) E2A and E2A mobility – measuring the alignment and systolic-diastolic mobility of the sheetlets, respectively.This document provides recommendations for both clinical and research cDWI and cDTI, based on published evidence when available and expert consensus when not. It introduces the cardiac microstructure focusing on the cardiomyocytes and their role in cardiac physiology and pathophysiology. It highlights methods, observations and recommendations in terminology, acquisition schemes, post-processing pipelines, data analysis and interpretation of the different biomarkers. Despite the ongoing challenges discussed in the document and the need for ongoing technical improvements, it is clear that cDTI is indeed feasible, can be accurately and reproducibly performed and, most importantly, can provide unique insights into myocardial pathophysiology.

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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: PP

  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 lowrankness 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 stateof-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
  Ankolekar A, Boie S, Abdollahyan M, Gadaleta E, Hasheminasab SA, Yang G, Beauville C, Dikaios N, Kastis GA, Bussmann M, Chelala C, Khalid S, Kruger H, Lambin P, Papanastasiou Get al., 2025,

  Advancing breast, lung and prostate cancer research with federated learning. A systematic review

  , NPJ DIGITAL MEDICINE, Vol: 8, ISSN: 2398-6352
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  • Citations: 1
• Journal article
  Pan Q, Li Z, Qiao W, Lou J, Yang Q, Yang G, Ji Bet al., 2025,

  AMVLM: Alignment-Multiplicity Aware Vision-Language Model for Semi-Supervised Medical Image Segmentation.

  , IEEE Trans Med Imaging, Vol: PP

  Low-quality pseudo labels pose a significant obstacle in semi-supervised medical image segmentation (SSMIS), impeding consistency learning on unlabeled data. Leveraging vision-language model (VLM) holds promise in ameliorating pseudo label quality by employing textual prompts to delineate segmentation regions, but it faces the challenge of cross-modal alignment uncertainty due to multiple correspondences (multiple images/texts tend to correspond to one text/image). Existing VLMs address this challenge by modeling semantics as distributions but such distributions lead to semantic degradation. To address these problems, we propose Alignment-Multiplicity Aware Vision-Language Model (AMVLM), a new VLM pre-training paradigm with two novel similarity metric strategies. (i) Cross-modal Similarity Supervision (CSS) proposes a probability distribution transformer to supervise similarity scores across fine-granularity semantics through measuring cross-modal distribution disparities, thus learning cross-modal multiple alignments. (ii) Intra-modal Contrastive Learning (ICL) takes into account the similarity metric of coarse-fine granularity information within each modality to encourage cross-modal semantic consistency. Furthermore, using the pretrained AMVLM, we propose a pioneering text-guided SSMIS network to compensate for the quality deficiencies of pseudo-labels. This network incorporates a text mask generator to produce multimodal supervision information, enhancing pseudo label quality and the model's consistency learning. Extensive experimentation validates the efficacy of our AMVLM-driven SSMIS, showcasing superior performance across four publicly available datasets. The code will be available at: https://github.com/QingtaoPan/AMVLM.

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• Journal article
  Hasan MK, Luo Y, Yang G, Yap CHet al., 2025,

  Feedback attention to enhance unsupervised deep learning image registration in 3D echocardiography

  , IEEE Transactions on Medical Imaging, Vol: 44, Pages: 2230-2243, ISSN: 0278-0062

  Cardiac motion estimation is important for assessing the contractile health of the heart, and performing this in 3D can provide advantages due to the complex 3D geometry and motions of the heart. Deep learning image registration (DLIR) is a robust way to achieve cardiac motion estimation in echocardiography, providing speed and precision benefits, but DLIR in 3D echo remains challenging. Successful unsupervised 2D DLIR strategies are often not effective in 3D, and there have been few 3D echo DLIR implementations. Here, we propose a new spatial feedback attention (FBA) module to enhance unsupervised 3D DLIR and enable it. The module uses the results of initial registration to generate a co-attention map that describes remaining registration errors spatially and feeds this back to the DLIR to minimize such errors and improve self-supervision. We show that FBA improves a range of promising 3D DLIR designs, including networks with and without transformer enhancements, and that it can be applied to both fetal and adult 3D echo, suggesting that it can be widely and flexibly applied. We further find that the optimal 3D DLIR configuration is when FBA is combined with a spatial transformer and a DLIR backbone modified with spatial and channel attention, which outperforms existing 3D DLIR approaches. FBA’s good performance suggests that spatial attention is a good way to enable scaling up from 2D DLIR to 3D and that a focus on the quality of the image after registration warping is a good way to enhance DLIR performance. Codes and data are available at: https://github.com/kamruleee51/Feedback_DLIR.

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• Journal article
  Zhang Z, Zhang H, Zeng T, Yang G, Shi Z, Gao Zet al., 2025,

  Bridging multi-level gaps: Bidirectional reciprocal cycle framework for text-guided label-efficient segmentation in echocardiography

  , MEDICAL IMAGE ANALYSIS, Vol: 102, ISSN: 1361-8415
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  • Citations: 4
• Journal article
  Nan Y, Zhou H, Xing X, Papanastasiou G, Zhu L, Gao Z, Frangi AF, Yang Get al., 2025,

  Revisiting medical image retrieval via knowledge consolidation

  , MEDICAL IMAGE ANALYSIS, Vol: 102, ISSN: 1361-8415
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• Journal article
  Schweitzer R, de Marvao A, Shah M, Inglese P, Kellman P, Berry A, Statton B, O'Regan Det al., 2025,

  Establishing cardiac MRI reference ranges stratified by sex and age for cardiovascular function during exercise

  , Radiology: Cardiothoracic Imaging, ISSN: 2638-6135

  Purpose: To evaluate the effects of exercise on left ventricular parameters using exercise cardiac MRI in healthy adults without known cardiovascular disease, and establish reference ranges stratified by age and sex.Materials and Methods: This prospective study included healthy adult participants with no known cardiovascular disease or genetic variants associated with cardiomyopathy, enrolled between January 2018 and April 2021, who underwent exercise cardiac MRI evaluation. Participants were imaged at rest and after exercise, with parameters measured by two readers. Prediction intervals were calculated and compared across sex and age groups.Results: The study included 161 participants (mean age, 49±[SD]14 years; 85 female). Compared with the resting state, exercise caused an increase in heart rate (64±9 bpm vs 133±19 bpm, P < 0.001), left ventricular end-diastolic volume (140±32 ml vs 148±35 ml, P < 0.001), stroke volume (82±18 ml vs 102±25 ml, P < 0.001), ejection fraction (59±6% vs 69±7%, P < 0.001), and cardiac output (5.2±1.1 l/min vs 13.5±3.9 l/min, P < 0.001), and a decrease in left ventricular end-systolic volume (58±18 ml vs 46±15 ml, P < 0.001). There were significant differences in exercise response between groups stratified by sex and age for most parameters.Conclusion: In healthy adults, an increase in cardiac output after exercise is driven by a rise in heart rate with both increased ventricular filling and emptying. Normal ranges for exercise response, stratified by age and sex, are established as a reference for the use of exercise cardiac MRI in clinical practice.

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• Journal article
  Nan Y, Zhou H, Xing X, Yang Get al., 2025,

  Beyond the Hype: A Dispassionate Look at Vision-Language Models in Medical Scenario

  , IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, ISSN: 2162-237X
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