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Radiological Society of North America (RSNA)
非盈利组织
Oak Brook，Illinois 85,218 位关注者
Promoting excellence in patient care and health care delivery through education, research and technologic innovation.
关于我们
The Radiological Society of North America (RSNA) is a non-profit organization that represents 31 radiologic subspecialties from 153 countries around the world. We promote excellence in patient care and healthcare delivery through education, research and technologic innovation. We provide high-quality educational resources, including continuing education credits toward physicians’ certification maintenance, host the world’s largest radiology conference and publish five peer-reviewed journals: : Radiology, RadioGraphics, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging and Radiology: Imaging Cancer. We are dedicated to building the future of our profession, whether that's through our Research & Education Foundation, which has funded $60 million in grants since its inception, our solutions to support standards development or educational outreach to low-resource nations.
- 网站
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https://www.rsna.org/
Radiological Society of North America (RSNA)的外部链接
- 所属行业
- 非盈利组织
- 规模
- 201-500 人
- 总部
- Oak Brook，Illinois
- 类型
- 非营利机构
- 创立
- 1915
- 领域
- radiology、medical imaging、continuing medical education、radiology trainee resources、medical imaging informatics和Radiological Society of North America
地点
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主要
获取路线
820 Jorie Blvd
US，Illinois，Oak Brook，60523
Radiological Society of North America (RSNA)员工
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Olga R Brook
Improving patient outcomes through standardization in CT-guided interventions - from preop assessment and prep to determination of concordance with…
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Dennis K. Bielecki, MD
Senior Lecturer (Associate Professor) Diagnostic Imaging, King's College Hospital NHS Trust
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Michelle Bardis
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Laura M. Jacobs
Adjunct Professor | Health, Medical and Science PR, Communications & Marketing
动态
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With new federal legislation impacting radiology practices using AI, RSNA has created a comprehensive FAQ for radiologists about Nondiscrimination in Health Programs and Activities – ACA Section 1557. https://bit.ly/3CHGmoq
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MRI image acquisition speed has improved over time through techniques that include faster imaging sequences, parallel imaging, compressed sensing and deep learning-based reconstruction of incomplete images. Deep learning methods work best when they incorporate principles of image physics. However, these methods usually need access to proprietary raw data and detailed information about an MRI system’s hardware and software. average by 29%. In an article published in Radiology Advances, Laura Onac, MSc, Ezra AI Inc., and colleagues from New York University Grossman School of Medicine assessed a vendor-agnostic AI-based approach to removing image degradation artifacts in highly accelerated MRI scans. The new model’s outputs rated better than the original images, with improvements in quality and feature visibility. Resolution was either improved or maintained, and scan time was reduced on “This vendor-agnostic AI-based method achieved robust scan time savings without loss of image quality, potentially allowing for reduced cost and improved patient experience,” the authors conclude. Read the full article, “An Image-Domain Deep-Learning Denoising Technique for Accelerated Parallel Brain MRI: Prospective Clinical Evaluation.” https://bit.ly/3AESbv2
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The November issue of Radiology: Artificial Intelligence is now online! https://bit.ly/NewRadAI
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Congratulations to all named in the Radiology Business Forty Under 40 Class of 2024! We’re proud to see so many dedicated RSNA volunteers make this list. https://bit.ly/3ATySON
Radiology Business Announces Forty Under 40 Class of 2024
radiologybusiness.com
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Anticipation is building for the latest ideas and innovations in radiologic science, education and technology! https://bit.ly/4fZRoo5
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Each year at the annual meeting, RSNA recognizes select countries for their contributions to radiology and their role in helping shape the profession’s future. This year’s Country Presents sessions will feature Japan and Peru. https://bit.ly/411rAmD
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The Editors’ Choice selection from Radiology: Imaging Cancer is Intraprocedural Diffusion-weighted Imaging for Predicting Ablation Zone during MRI-guided Focused Ultrasound of Prostate Cancer. https://bit.ly/4fntpyI
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Read the Radiology: Cardiothoracic Imaging selection for Editors’ Choice, MRI in Patients with Cardiovascular Implantable Electronic Devices and Fractured or Abandoned Leads. https://bit.ly/4cpPyKG
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The Editors’ Choice selection from Radiology: Artificial Intelligence is Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts. https://bit.ly/4fAje9I
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