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

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.

arXiv comment59 pages, 4 figures, 5 tables; two first equally contributing authors; Medical Physics. 2021

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Cited by in corpus (8)

• SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
• Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review
• Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy
• SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy
• FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
• Generalizable synthetic MRI with physics-informed convolutional networks
• Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis
• Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion