Acute ischemic stroke (AIS) remains the leading cause of mortality and disability worldwide. While revascularization therapies-such as intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)-have significantly improved outcomes, their success is strongly influenced by the status of cerebral collateral circulation. Collateral vessels sustain cerebral perfusion during vascular occlusion, limiting infarct growth and extending therapeutic windows. Despite this recognized importance, standardized methods for assessing collateral status and integrating it into treatment strategies are still evolving. This narrative review synthesizes current evidence on the role of collateral circulation in AIS, focusing on its impact on infarct dynamics, treatment efficacy, and functional recovery. We highlight findings from major clinical trials-including MR CLEAN, DAWN, DEFUSE-3, and SWIFT PRIME which consistently demonstrate that robust collateral networks are associated with improved outcomes and expanded eligibility for reperfusion therapies. Advances in neuroimaging, such as multiphase CTA and perfusion MRI, alongside emerging AI-driven automated collateral grading, are reshaping patients' selection and clinical decision-making. We also discuss novel therapeutic strategies aimed at enhancing collateral flow, such as vasodilators, neuroprotective agents, statins, and stem cell therapies. Despite growing evidence supporting collateral-based treatment approaches, real-time clinical implementation remains limited by challenges in standardization and access. Cerebral collateral circulation is a critical determinant of stroke prognosis and treatment response. Incorporating collateral assessment into acute stroke workflows-supported by advanced imaging, artificial intelligence, and personalized medicine-offers a promising pathway to optimize outcomes. As the field moves beyond a strict "time is brain" model, the emerging paradigm of "time is collaterals" may better reflect the dynamic interplay between perfusion, tissue viability, and therapeutic opportunity in AIS management.

Deep learning-based medical image classification techniques are rapidly advancing in medical image analysis, making it crucial to develop accurate and trustworthy models that can be efficiently deployed across diverse clinical scenarios. Concept Bottleneck Models (CBMs), which first predict a set of explainable concepts from images and then perform classification based on these concepts, are increasingly being adopted for explainable medical image classification. However, the inherent explainability of CBMs introduces new challenges when deploying trained models to new environments. Variations in imaging protocols and staining methods may induce concept-level shifts, such as alterations in color distribution and scale. Furthermore, since CBM training requires explicit concept annotations, fine-tuning models solely with image-level labels could compromise concept prediction accuracy and faithfulness - a critical limitation given the high cost of acquiring expert-annotated concept labels in medical domains. To address these challenges, we propose a training-free confusion concept identification strategy. By leveraging minimal new data (e.g., 4 images per class) with only image-level labels, our approach enhances out-of-domain performance without sacrificing source domain accuracy through two key operations: masking misactivated confounding concepts and amplifying under-activated discriminative concepts. The efficacy of our method is validated on both skin and white blood cell images. Our code is available at: https://github.com/riverback/TF-TTI-XMed.

With the advancement of Large Language Model (LLM) for natural language processing, this paper presents an intriguing finding: a frozen pre-trained LLM layer can process visual tokens for medical image segmentation tasks. Specifically, we propose a simple hybrid structure that integrates a pre-trained, frozen LLM layer within the CNN encoder-decoder segmentation framework (LLM4Seg). Surprisingly, this design improves segmentation performance with a minimal increase in trainable parameters across various modalities, including ultrasound, dermoscopy, polypscopy, and CT scans. Our in-depth analysis reveals the potential of transferring LLM's semantic awareness to enhance segmentation tasks, offering both improved global understanding and better local modeling capabilities. The improvement proves robust across different LLMs, validated using LLaMA and DeepSeek.

LLMs have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scattered across multiple centers. Centralizing these data is exceptionally challenging due to privacy regulations, thereby impeding model development and broader adoption of LLM-driven MRG models. To address this challenge, we present FedMRG, the first framework that leverages Federated Learning (FL) to enable privacy-preserving, multi-center development of LLM-driven MRG models, specifically designed to overcome the critical challenge of communication-efficient LLM training under multi-modal data heterogeneity. To start with, our framework tackles the fundamental challenge of communication overhead in FL-LLM tuning by employing low-rank factorization to efficiently decompose parameter updates, significantly reducing gradient transmission costs and making LLM-driven MRG feasible in bandwidth-constrained FL settings. Furthermore, we observed the dual heterogeneity in MRG under the FL scenario: varying image characteristics across medical centers, as well as diverse reporting styles and terminology preferences. To address this, we further enhance FedMRG with (1) client-aware contrastive learning in the MRG encoder, coupled with diagnosis-driven prompts, which capture both globally generalizable and locally distinctive features while maintaining diagnostic accuracy; and (2) a dual-adapter mutual boosting mechanism in the MRG decoder that harmonizes generic and specialized adapters to address variations in reporting styles and terminology. Through extensive evaluation of our established FL-MRG benchmark, we demonstrate the generalizability and adaptability of FedMRG, underscoring its potential in harnessing multi-center data and generating clinically accurate reports while maintaining communication efficiency.

Emergency radiology has evolved into a significant subspecialty over the past 2 decades, facing unique challenges including escalating imaging volumes, increasing study complexity, and heightened expectations from clinicians and patients. This review provides a comprehensive overview of the key requirements for an effective emergency radiology unit. Emergency radiologists play a crucial role in real-time decision-making by providing continuous 24/7 support, requiring expertise across various organ systems and close collaboration with emergency physicians and specialists. Beyond image interpretation, emergency radiologists are responsible for organizing staff schedules, planning equipment, determining imaging protocols, and establishing standardized reporting systems. Operational considerations in emergency radiology departments include efficient scheduling models such as circadian-based scheduling, strategic equipment organization with primary imaging modalities positioned near emergency departments, and effective imaging management through structured ordering systems and standardized protocols. Preparedness for mass casualty incidents requires a well-organized workflow process map detailing steps from patient transfer to image acquisition and interpretation, with clear task allocation and imaging pathways. Collaboration between emergency radiologists and physicians is essential, with accurate communication facilitated through various channels and structured reporting templates. Artificial intelligence has emerged as a transformative tool in emergency radiology, offering potential benefits in both interpretative domains (detecting intracranial hemorrhage, pulmonary embolism, acute ischemic stroke) and non-interpretative applications (triage systems, protocol assistance, quality control). Despite implementation challenges including clinician skepticism, financial considerations, and ethical issues, AI can enhance diagnostic accuracy and workflow optimization. Teleradiology provides solutions for staff shortages, particularly during off-hours, with hybrid models allowing radiologists to work both on-site and remotely. This review aims to guide stakeholders in establishing and maintaining efficient emergency radiology services to improve patient outcomes.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.

Post-traumatic stress disorder (PTSD) is a delayed-onset or prolonged persistent psychiatric disorder caused by individuals experiencing an unusually threatening or catastrophic stressful event or situation. Due to its long duration and recurrent nature, unimodal neuroimaging tools such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalography (EEG) have been widely used in the diagnosis and treatment of PTSD for early intervention. However, as compared with an unimodal approach, a multimodal imaging approach can better capture integrated neural mechanisms underlying the occurrence and development of PTSD, including predisposing factors, changes in neural activity, and physiological mechanisms of symptoms. Moreover, a multimodal neuroimaging approach can aid the diagnosis and treatment of PTSD, facilitate searching for biomarkers at different stages of PTSD, and explore biomarkers for symptomatic improvement. However, at present, the majority of PTSD studies remain unimodal, while the combination of multimodal brain imaging data with machine learning will become an important direction for future research.

Deep neural networks are increasingly being used to detect and diagnose medical conditions using medical imaging. Despite their utility, these models are highly vulnerable to adversarial attacks and distribution shifts, which can affect diagnostic reliability and undermine trust among healthcare professionals. In this study, we propose a robust training algorithm with data augmentation (RTDA) to mitigate these vulnerabilities in medical image classification. We benchmark classifier robustness against adversarial perturbations and natural variations of RTDA and six competing baseline techniques, including adversarial training and data augmentation approaches in isolation and combination, using experimental data sets with three different imaging technologies (mammograms, X-rays, and ultrasound). We demonstrate that RTDA achieves superior robustness against adversarial attacks and improved generalization performance in the presence of distribution shift in each image classification task while maintaining high clean accuracy.

This study aims to explore research hotspots and development trends in molecular imaging of glioma from 2014 to 2024. A total of 2957 publications indexed in the web of science core collection (WoSCC) were analyzed using bibliometric techniques. To visualize the research landscape, co-citation clustering, keyword analysis, and technological trend mapping were performed using CiteSpace and Excel. Publication output peaked in 2021. Emerging research trends included the integration of radiomics and artificial intelligence and the application of novel imaging modalities such as positron emission tomography and magnetic resonance spectroscopy. Significant progress was observed in blood-brain barrier disruption techniques and the development of molecular probes, especially those targeting IDH and MGMT mutations. Molecular imaging has been pivotal in advancing glioma research, contributing to improved diagnostic accuracy and personalized treatment strategies. However, challenges such as clinical translation and standardization remain. Future studies should focus on integrating advanced technologies into routine clinical practice to enhance patient care.

Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation challenging. Artificial intelligence (AI) is recognized as a promising tool to address these challenges. Therefore, this review aims to explore the recent advances in AI applications in liver tumor imaging, focusing on key areas such as image reconstruction, image quality enhancement, lesion detection, tumor characterization, segmentation, and radiomics. Among these, AI-based image reconstruction has already been widely integrated into clinical workflows, helping to enhance image quality while reducing radiation exposure. While the adoption of AI-assisted diagnostic tools in liver imaging has lagged behind other fields, such as chest imaging, recent developments are driving their increasing integration into clinical practice. In the future, AI is expected to play a central role in various aspects of liver cancer care, including comprehensive image analysis, treatment planning, response evaluation, and prognosis prediction. This review offers a comprehensive overview of the status and prospects of AI applications in liver tumor imaging.