Meta-Radiology最新文献

Brain tumors have shown extreme mortality and increasing incidence during recent years, which bring enormous challenges for the timely diagnosis and effective treatment of brain tumors. Concretely, accurate brain tumor segmentation on multi-modal Magnetic Resonance Imaging (MRI) is essential and important since most normal tissues are unresectable in brain tumor surgery. In the past decade, with the explosive development of artificial intelligence technologies, a series of deep learning-based methods are presented for brain tumor segmentation and achieved excellent performance. Among them, vision transformers with non-local receptive fields show superior performance compared with the classical Convolutional Neural Networks (CNNs). In this review, we focus on the representative transformer-based works for brain tumor segmentation proposed in the last three years. Firstly, this review divides these transformer-based methods as the pure transformer methods and the hybrid transformer methods according to their transformer architectures. Then, we summarize the corresponding theoretical innovations, implementation schemes and superiorities to help readers better understand state-of-the-art transformer-based brain tumor segmentation methods. After that, we introduce the most commonly-used Brain Tumor Segmentation (BraTS) datasets, and comprehensively analyze and compare the performance of existing methods through multiple quantitative statistics. Finally, we discuss the current research challenges and describe the future research trends.

Physiological and physical motions of the subjects, e.g., patients, are the primary sources of image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring, low signal-to-noise ratio, or ghosting. To overcome motion artifacts, various deep learning strategies, and models have been investigated to enable retrospective and prospective motion correction for MRI. This review article provides a survey on current deep learning-based rigid motion correction methods that have been used for MRI. Also, deep learning motion correction methods are compared to conventional motion correction methods and hybrid methods. Furthermore, we discuss the advantages and limitations of the current deep learning motion correction methods, leading to some suggestions for the future development of deep learning motion correction methods and their potential applications in improving clinical MRI.

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI.

This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging. With the aid of public data, these models, which possess remarkable language understanding and generation capabilities, are augmenting the interpretive skills of radiologists, enhancing patient-physician communication, and streamlining clinical workflows. The paper introduces an analytic framework for presenting the complex interactions between LLMs and the broader ecosystem of medical imaging stakeholders, including businesses, insurance entities, governments, research institutions, and hospitals (nicknamed BIGR-H). Through detailed analyses, illustrative use cases, and discussions on the broader implications and future directions, this perspective seeks to raise discussion in strategic planning and decision-making in the era of AI-enabled healthcare.

Substance use disorder (SUD) is a type of addictive encephalopathy resulting from drug abuse, which leads to abnormal cerebral alterations indicating neurotoxicity that is manifested through various biomarkers. However, biological mechanisms underlying addiction are still not thoroughly explored. Omics approaches, including radiomics, connectomics, immunomics, transcriptomics, metabolomics, genomics, and proteomics, offer high-throughput means of discovering potential biological markers of SUDs. Nonetheless, omics research in addiction is dispersed, and summarization is needed to capture the general direction. This review provides an overview of omics application in SUDs and highlights dominant biomarkers that have been identified for predicting SUDs’ initiation, therapeutic responses, and targeting specific molecular targets of personalized treatment, which can help to improve the understanding of critical issues in drug addiction research.