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肌肉骨骼放射学的科学进展与技术创新

Scientific Advances and Technical Innovations in Musculoskeletal Radiology.

作者信息

Fritz Jan, Runge Val M

机构信息

From the Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, NY.

Department of Diagnostic, Interventional, and Pediatric Radiology, University Hospital of Bern, Inselspital, University of Bern, Bern, Switzerland.

出版信息

Invest Radiol. 2023 Jan 1;58(1):1-2. doi: 10.1097/RLI.0000000000000930. Epub 2022 Nov 23.

Abstract

Decades of technical innovations have propelled musculoskeletal radiology through an astonishing evolution. New artificial intelligence and deep learning methods capitalize on many past innovations in magnetic resonance imaging (MRI) to reach unprecedented speed, image quality, and new contrasts. Similarly exciting developments in computed tomography (CT) include clinically applicable molecular specificity and substantially improved spatial resolution of musculoskeletal structures and diseases. This special issue of Investigative Radiology comprises a collection of expert summaries and reviews on the most impactful innovations and cutting-edge topics in musculoskeletal radiology, including radiomics and deep learning methods for musculoskeletal disease detection, high-resolution MR neurography, deep learning-driven ultra-fast musculoskeletal MRI, MRI-based synthetic CT, quantitative MRI, modern low-field MRI, 7.0 T MRI, dual-energy CT, cone beam CT, kinematic CT, and synthetic contrast generation in musculoskeletal MRI.

摘要

数十年来的技术创新推动了肌肉骨骼放射学的惊人发展。新的人工智能和深度学习方法利用了磁共振成像(MRI)过去的许多创新成果,实现了前所未有的速度、图像质量和新的对比度。计算机断层扫描(CT)方面同样令人兴奋的进展包括临床适用的分子特异性以及肌肉骨骼结构和疾病空间分辨率的大幅提高。《放射学研究》的这一特刊收录了一系列专家总结和综述,内容涉及肌肉骨骼放射学中最具影响力的创新和前沿主题,包括用于肌肉骨骼疾病检测的放射组学和深度学习方法、高分辨率磁共振神经造影、深度学习驱动的超快肌肉骨骼MRI、基于MRI的合成CT、定量MRI、现代低场MRI、7.0T MRI、双能CT、锥形束CT、运动CT以及肌肉骨骼MRI中的合成对比剂生成。

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