Meta-Radiology最新文献_第5页

Integrating multimodal and multiscale data: A new avenue in cancer research 整合多模态和多尺度数据：癌症研究的新途径

Meta-Radiology Pub Date : 2024-06-01 DOI: 10.1016/j.metrad.2024.100083

Hairong Zheng

引用次数: 0

A survey of emerging applications of diffusion probabilistic models in MRI 核磁共振成像中扩散概率模型的新兴应用概览

Meta-Radiology Pub Date : 2024-05-09 DOI: 10.1016/j.metrad.2024.100082

Yuheng Fan , Hanxi Liao , Shiqi Huang , Yimin Luo , Huazhu Fu , Haikun Qi

{"title":"A survey of emerging applications of diffusion probabilistic models in MRI","authors":"Yuheng Fan , Hanxi Liao , Shiqi Huang , Yimin Luo , Huazhu Fu , Haikun Qi","doi":"10.1016/j.metrad.2024.100082","DOIUrl":"https://doi.org/10.1016/j.metrad.2024.100082","url":null,"abstract":"<div><p>Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest. Despite their huge computational burdens due to the large number of steps involved during sampling, DPMs are widely appreciated in various medical imaging tasks for their high-quality and diversity of generation. Magnetic resonance imaging (MRI) is an important medical imaging modality with excellent soft tissue contrast and superb spatial resolution, which possesses unique opportunities for DPMs. Although there is a recent surge of studies exploring DPMs in MRI, a survey paper of DPMs specifically designed for MRI applications is still lacking. This review article aims to help researchers in the MRI community to grasp the advances of DPMs in different applications. We first introduce the theory of two dominant kinds of DPMs, categorized according to whether the diffusion time step is discrete or continuous, and then provide a comprehensive review of emerging DPMs in MRI, including reconstruction, image generation, image translation, segmentation, anomaly detection, and further research topics. Finally, we discuss the general limitations as well as limitations specific to the MRI tasks of DPMs and point out potential areas that are worth further exploration.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 2","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000353/pdfft?md5=9d1e7a26ec748c31c30e6602d6c1b77a&pid=1-s2.0-S2950162824000353-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Opportunities and challenges in the application of large artificial intelligence models in radiology 大型人工智能模型在放射学中应用的机遇与挑战

Meta-Radiology Pub Date : 2024-05-08 DOI: 10.1016/j.metrad.2024.100080

Liangrui Pan , Zhenyu Zhao , Ying Lu , Kewei Tang , Liyong Fu , Qingchun Liang , Shaoliang Peng

引用次数: 0

Review and prospects of new progress in intelligent imaging research on lymph node metastasis in esophageal carcinoma 食管癌淋巴结转移智能成像研究新进展回顾与展望

Meta-Radiology Pub Date : 2024-05-06 DOI: 10.1016/j.metrad.2024.100081

Dan Gao , Yu-ping Wu , Tian-wu Chen

{"title":"Review and prospects of new progress in intelligent imaging research on lymph node metastasis in esophageal carcinoma","authors":"Dan Gao , Yu-ping Wu , Tian-wu Chen","doi":"10.1016/j.metrad.2024.100081","DOIUrl":"10.1016/j.metrad.2024.100081","url":null,"abstract":"<div><p>Esophagus carcinoma (EC) ranks sixth in cancer-related mortality and seventh in terms of morbidity worldwide, and radical esophagectomy is considered as the basis of comprehensive treatment for locally advanced EC. Accurate preoperative determination of lymph node status is critical for treatment decision-making, assessment of survival time and life quality of patients after surgery. However, the rate of misdiagnosis and missed diagnosis of metastatic lymph nodes by traditional imaging methods is high. With the development of artificial intelligence technology and medical image digitization, medical image analysis methods based on artificial intelligence have brought new ideas to the diagnosis and research of lymph node metastasis secondary to EC. At present, texture analysis, radiomics and deep learning are the most widely used methods. These technologies extract and analyze quantitative features from traditional medical images to provide biological information such as tumor characteristics and heterogeneity to guide clinical practice. Therefore, this review mainly introduces and discusses the current status of imaging research on lymph node metastasis in patients with EC based on texture analysis, radiomics and deep learning, and prospects the important research directions in the future with a view to improving the diagnostic capability of lymph node metastasis in patients with EC in China.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 2","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000341/pdfft?md5=89195e7f5475cf1b4e7c30ee96ca5f0d&pid=1-s2.0-S2950162824000341-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The general intelligence of GPT–4, its knowledge diffusive and societal influences, and its governance GPT-4 的一般智能、其知识传播和社会影响及其管理

Meta-Radiology Pub Date : 2024-03-28 DOI: 10.1016/j.metrad.2024.100078

Mohammad Mahdi Jahani Yekta

{"title":"The general intelligence of GPT–4, its knowledge diffusive and societal influences, and its governance","authors":"Mohammad Mahdi Jahani Yekta","doi":"10.1016/j.metrad.2024.100078","DOIUrl":"10.1016/j.metrad.2024.100078","url":null,"abstract":"<div><p>Recent breakthroughs in artificial intelligence (AI) research include advancements in natural language processing (NLP) achieved by large language models (LLMs), and; in particular, generative pre–trained transformer (GPT) architectures. The latest GPT developed by OpenAI, GPT–4, has shown remarkable intelligence across various domains and tasks. It exhibits capabilities in abstraction, comprehension, vision, computer coding, mathematics, and more, suggesting it to be a significant step towards artificial general intelligence (AGI), a level of AI that possesses capabilities similar to human intelligence. This paper explores this AGI, its knowledge diffusive and societal influences, and its governance. In addition to coverage of the major associated topics studied in the literature, and making up for their loopholes, we scrutinize how GPT-4 can facilitate the diffusion of knowledge across different areas of science by promoting their interpretability and explainability (IE) to inexperts. Where applicable, the topics are also accompanied by their specific potential implications on medical imaging.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 2","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000316/pdfft?md5=769c604700adeb19de2fbe3cfa9f0e33&pid=1-s2.0-S2950162824000316-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140400691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MCFCN: Multi-scale capsule-weighted fusion classification network for lung disease classification based on chest CT scans MCFCN：基于胸部 CT 扫描的多尺度胶囊加权融合肺病分类网络

Meta-Radiology Pub Date : 2024-03-13 DOI: 10.1016/j.metrad.2024.100070

Ao Liu , Shaowu Liu , Cuihong Wen

{"title":"MCFCN: Multi-scale capsule-weighted fusion classification network for lung disease classification based on chest CT scans","authors":"Ao Liu , Shaowu Liu , Cuihong Wen","doi":"10.1016/j.metrad.2024.100070","DOIUrl":"10.1016/j.metrad.2024.100070","url":null,"abstract":"<div><h3>Aim and scope</h3><p>This paper aims to propose a Multi-scale Capsule-weighted Fusion Classification Network (MCFCN), a classification model for automatic diagnosis of lung lesions by CT scanning.</p></div><div><h3>Background</h3><p>The automatic diagnosis of lung lesions based on chest CT scans plays a crucial role in assisting doctors to identify suspicious cases quickly and accurately. However, existing methods struggle to differentiate lesions with similar morphologies, and current feature extraction techniques lack the ability to effectively highlight small-scale targets in a large-scale environment, leading to incomplete extraction of subtle features and ultimately compromising the classification performance.</p></div><div><h3>Method</h3><p>The MCFCN employs a dynamic routing clustering algorithm to emphasize small-scale features, preventing feature loss. Additionally, a scale difference fusion network is utilized to extract precise position scaling parameters by incorporating weighted fusion of information from different scales.</p></div><div><h3>Results</h3><p>MCFCN achieves an accuracy of 99.41% for COVID-19 classification, 93.33% for CAP classification, and 100% for Normal classification, with an overall accuracy of 98.36%.</p></div><div><h3>Conclusion</h3><p>Experimental results on the target dataset demonstrate that MCFCN outperforms state-of-the-art methods. In the future, this model can be further explored and optimized to enhance its application value in clinical practice</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 2","pages":"Article 100070"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000237/pdfft?md5=17397f30f7dbca0890a79385efeda99f&pid=1-s2.0-S2950162824000237-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140272992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas 基于深度学习的人工智能辅助诊断、评估和治疗软组织肉瘤

Meta-Radiology Pub Date : 2024-02-29 DOI: 10.1016/j.metrad.2024.100069

Ruiling Xu , Jinxin Tang , Chenbei Li , Hua Wang , Lan Li , Yu He , Chao Tu , Zhihong Li

{"title":"Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas","authors":"Ruiling Xu , Jinxin Tang , Chenbei Li , Hua Wang , Lan Li , Yu He , Chao Tu , Zhihong Li","doi":"10.1016/j.metrad.2024.100069","DOIUrl":"https://doi.org/10.1016/j.metrad.2024.100069","url":null,"abstract":"<div><p>Soft tissue sarcomas (STSs) represent a group of heterogeneous mesenchymal tumors of which are generally classified as per the histopathology. Despite being rare in incidence and prevalence, STSs are usually correlated with unfavorable prognosis and high mortality rate. Early and accurate diagnosis of STSs are critical in clinical management of STSs. Deep learning (DL) refers to a subtype of artificial intelligence that has been adopted to assist healthcare professionals to optimize personalized treatment for a given situation, particularly in image analysis. Recently, emerging studies have demonstrated that application of DL based on medical images could substantially improve the accuracy and efficiency of clinicians to the identification, diagnosis, treatment, and prognosis prediction of STSs, and thereby facilitating the clinical decision-making. Herein, we aimed to extensively summarize the recent applications of DL-based artificial intelligence in STSs from the aspects of data acquisition, algorithm, and model establishment. Besides, the reinforcement of the model by transfer learning and generative adversarial network (GAN) for data augmentation has also been elaborated. It is worth noting that high-quality data with accurate annotations, as well as optimized algorithmic performance are pivotal in the clinical application of DL in STSs.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 2","pages":"Article 100069"},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000225/pdfft?md5=384e6043295337c1b710b8b02a635ea7&pid=1-s2.0-S2950162824000225-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research and application progress of radiomics in neurodegenerative diseases 放射组学在神经退行性疾病中的研究与应用进展

Meta-Radiology Pub Date : 2024-02-22 DOI: 10.1016/j.metrad.2024.100068

Junbang Feng , Ying Huang , Xiaocai Zhang , Qingning Yang , Yi Guo , Yuwei Xia , Chao Peng , Chuanming Li

{"title":"Research and application progress of radiomics in neurodegenerative diseases","authors":"Junbang Feng , Ying Huang , Xiaocai Zhang , Qingning Yang , Yi Guo , Yuwei Xia , Chao Peng , Chuanming Li","doi":"10.1016/j.metrad.2024.100068","DOIUrl":"https://doi.org/10.1016/j.metrad.2024.100068","url":null,"abstract":"<div><p>Neurodegenerative diseases refer to degenerative diseases of the nervous system caused by neuronal degeneration and apoptosis. Usually, the onset of the disease is insidious, and the progression is slow, which can last for several years to decades. Clinical symptoms only appear in the later stages of pathological changes when the degree of nerve cell loss reaches or exceeds a certain threshold. Traditional electrophysiological and medical imaging techniques lack valuable indicators and markers. Therefore, early diagnosis and differentiation are very difficult. Radiomics is a new medical imaging technology merged in recent years, which can extract a large number of invisible features from raw image data with high throughput, and quantitatively analyze the pathological and physiological changes. It demonstrates important potential value in the diagnosis, grading, and prognosis evaluation of NDs. This review provides an overview of the research progress of radiomics in neurodegenerative diseases, emphasizing the process principles of radiomics and its application in the diagnosis, classification, and prediction of these diseases. This helps to deepen the understanding of neurodegenerative diseases and promote early diagnosis and treatment in clinical practice.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 1","pages":"Article 100068"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000213/pdfft?md5=3fa74b37f4748d062753e6f0b4eb9348&pid=1-s2.0-S2950162824000213-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Magnetic resonance-guided focused ultrasound in intracranial diseases: Clinical applications and future directions 颅内疾病中的磁共振引导聚焦超声：临床应用和未来方向

Meta-Radiology Pub Date : 2024-02-16 DOI: 10.1016/j.metrad.2024.100065

Haoxuan Lu, Yujue Zhong, Yongqin Xiong, Xiaoyu Wang, Jiayu Huang, Yan Li, Xin Lou

{"title":"Magnetic resonance-guided focused ultrasound in intracranial diseases: Clinical applications and future directions","authors":"Haoxuan Lu, Yujue Zhong, Yongqin Xiong, Xiaoyu Wang, Jiayu Huang, Yan Li, Xin Lou","doi":"10.1016/j.metrad.2024.100065","DOIUrl":"https://doi.org/10.1016/j.metrad.2024.100065","url":null,"abstract":"<div><p>Magnetic resonance-guided focused ultrasound (MRgFUS) is a non-invasive technique for neuroregulation that offers several advantages, including non-invasiveness, no need for general anesthesia requirement, real-time target localization, and real-time temperature monitoring. Currently, the U.S. Food and Drug Administration has approved this technology for the treatment of essential tremor and Parkinson's disease, and its indications are continually expanding to encompass various intracranial diseases. In this article, we summarize clinical trials of high-intensity FUS in the treatment of intracranial diseases. Next, we introduce the preclinical and clinical studies on low-intensity FUS-induced blood-brain barrier opening and neuromodulation. Finally, we discuss the challenges and future directions of this technology. This review aims to guide future clinical trials and provide new perspectives for investigating the neural mechanisms of MRgFUS.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"2 1","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162824000183/pdfft?md5=3bd165df87ad0b1870ea309eb505010c&pid=1-s2.0-S2950162824000183-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139915473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lung cancer screening, diagnosis, and treatment: The radiologist's perspective 肺癌筛查、诊断和治疗：放射科医生的视角

Meta-Radiology Pub Date : 2024-02-10 DOI: 10.1016/j.metrad.2024.100066

Bingqing Long , Zeng Xiong , Manzo Habou

引用次数: 0