Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

TL;DR

This review comprehensively analyzes deep learning methods for synthetic CT generation in radiotherapy and PET, highlighting their applications, challenges, achievements, and future trends in clinical settings.

Contribution

It systematically categorizes DL-based sCT methods by clinical application, compares architectures, and evaluates clinical readiness, providing a thorough overview of recent advancements.

Findings

DL methods show promise for clinical application

Network architectures vary with application needs

Future trends indicate increasing clinical adoption

Abstract

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. 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.