基于深度学习的图像增强技术用于神经成像中的快速磁共振成像

Roh-Eul Yoo, Seung Hong Choi
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引用次数: 0

摘要

尽管核磁共振成像具有卓越的软组织对比度和非侵入性,但由于其固有的信号采集原理,需要较长的扫描时间,这是核磁共振成像技术进步一直关注的主要缺点。特别是在神经成像中,由于详细结构需要高分辨率成像,而且通常需要进行容积(三维)采集,因此缩短扫描时间是一个自然要求,最近有许多研究试图利用深度学习(DL)技术来缩短扫描时间和提高图像质量。各种基于深度学习的图像重建产品可在现有加速采集方法的基础上进一步缩短扫描时间,同时不影响图像质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

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