Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of Positron Emission Tomography (PET). Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends, and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
翻译:最近,制作合成计算断层造影术的深层学习(DL)方法作为古典成像学的替代方法,受到大量研究关注,我们在此对这些方法进行系统审查,按照临床应用将其分为三类:一)在磁共振治疗规划中取代CT,二)促进基于磁共振成像制成透析(CBCT)成像制成图制成的适应性放射疗法,以及三)为校正 Positron 排层造影(PET)绘制了缩写图,对2014年1月至2020年12月出版的期刊文章进行了适当的数据库搜索,从每份符合要求的研究中提取了DL方法的关键特征,并报告了网络结构和指标之间的全面比较,对每一类作了详细审查,重点介绍了主要贡献,查明了具体挑战,总结了成就,最后,分析了所有从各方面引用的作品的统计数据,揭示了光谱的流行程度和未来趋势,以及基于DL CT 生成的流成像的潜力。对基于DL的SCT生成的当前状况进行了评估,评估了以SCT生成方法的临床准备情况。