Moya-Sáez Elisa, de Luis-García Rodrigo, Alberola-López Carlos
Laboratorio de Procesado de Imagen, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
Front Neuroimaging. 2023 Jan 23;2:1055463. doi: 10.3389/fnimg.2023.1055463. eCollection 2023.
Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.
基于钆的造影剂（GBCAs）已成为神经肿瘤学磁共振成像（MRI）采集的关键部分，用于脑肿瘤的检测、特征描述和监测。然而，对比增强（CE）采集不仅引发安全问题，还会导致患者不适、需要更熟练的人力且成本增加。最近，一些提出的深度学习工作旨在减少甚至消除对GBCAs的需求。本研究回顾了已发表的有关从低剂量和/或其原始非CE对应图像合成CE图像的工作。总结了每种方法的数据、神经网络类型、输入模态数量以及评估方法。基于此分析，我们讨论了这些方法为适用于临床应用需要克服的主要问题。我们还推测了该主题研究可能遵循的一些未来趋势。
