TY  - JOUR
AU  - Spadea, Maria Francesca
AU  - Maspero, Matteo
AU  - Zaffino, Paolo
AU  - Seco, Joao
TI  - Deep learning-based synthetic-CT generation in radiotherapy and PET: a review.
JO  - Medical physics
VL  - 48
IS  - 11
SN  - 2473-4209
CY  - College Park, Md.
PB  - AAPM
M1  - DKFZ-2021-01885
SP  - 6537-6566
PY  - 2021
N1  - #LA:E041# / 2021 Nov;48(11):6537-6566
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:34407209
DO  - DOI:10.1002/mp.15150
UR  - https://inrepo02.dkfz.de/record/170295
ER  -