Zhu Guangming, Jiang Bin, Tong Liz, Xie Yuan, Zaharchuk Greg, Wintermark Max
Neuroradiology Section, Department of Radiology, Stanford Healthcare, Stanford, CA, United States.
Front Neurol. 2019 Aug 14;10:869. doi: 10.3389/fneur.2019.00869. eCollection 2019.
Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.
许多基于深度学习且与放射学相关的临床应用已在放射学中被提出并研究,用于分类、风险评估、分割任务、诊断、预后,甚至治疗反应预测。人工智能在医学成像的各种技术方面还有许多其他创新应用,特别是应用于图像采集,包括去除图像伪影、图像归一化/协调、提高图像质量、降低辐射和造影剂剂量以及缩短成像研究时长。本文将探讨这一主题,并试图概述深度学习在神经成像技术中的应用。