Deep learning based synthetic‐CT generation in radiotherapy and PET: A review. Issue 11 (15th September 2021)

• Record Type: Journal Article
• Authors: Spadea, Maria Francesca; Maspero, Matteo; Zaffino, Paolo; Seco, Joao
• Is Part Of: Medical physics. Volume 48:Issue 11(2021)
• Page Start: 6537
• Page End: 6566
• Publication Date: 2021-09-15
• Subjects: artificial intelligence -- convolutional neural networks -- deep learning -- image synthesis -- machine learning -- pseudo‐CT -- radiotherapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15150
• Languages: English
• ISSNs: 0094-2405

Abstract

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 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.