Department Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, Catanzaro, 88100, Italy.
Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan, Utrecht, The Netherlands.
Med Phys. 2021 Nov;48(11):6537-6566. doi: 10.1002/mp.15150. Epub 2021 Sep 15.
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 computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. 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 summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, 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.
最近，基于深度学习（DL）的合成计算机断层扫描（sCT）生成方法作为经典方法的替代方法受到了广泛关注。我们根据其临床应用将这些方法分为三类进行系统综述：（i）替代磁共振（MR）基于治疗计划的计算机断层扫描，（ii）便于基于锥形束 CT 的图像引导自适应放疗，以及（iii）推导正电子发射断层扫描的衰减图。对 2014 年 1 月至 2020 年 12 月期间发表的期刊文章进行了适当的数据库搜索。从每项合格研究中提取了 DL 方法的关键特征，并报告了网络架构和指标之间的综合比较。对每个类别进行了详细的审查，突出了重要贡献，确定了具体挑战，并总结了成就。最后，从各个方面分析了所有引用工作的统计数据，揭示了基于 DL 的 sCT 生成的流行趋势和未来趋势以及潜力。评估了基于 DL 的 sCT 生成的当前状态，评估了所提出方法的临床准备情况。
