  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.
