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

arXiv: https://arxiv.org/abs/2102.02734 · HTML: https://mlsanity.com/paper/2102.02734/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 221 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [Deep MR to CT Synthesis using Unpaired Data](https://mlsanity.com/paper/1708.01155/) (2017)
- [Loss Functions for Neural Networks for Image Processing](https://mlsanity.com/paper/1511.08861/) (2015)
- [Deep CT to MR Synthesis using Paired and Unpaired Data](https://mlsanity.com/paper/1805.10790/) (2018)
- [Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation](https://mlsanity.com/paper/1907.10982/) (2019)
- [Evaluating the Impact of Intensity Normalization on MR Image Synthesis](https://mlsanity.com/paper/1812.04652/) (2018)
- [Learning with Known Operators reduces Maximum Training Error Bounds](https://mlsanity.com/paper/1907.01992/) (2019)
- [Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image](https://mlsanity.com/paper/1709.02073/) (2017)
- [Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets](https://mlsanity.com/paper/1901.11369/) (2019)
- [Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning](https://mlsanity.com/paper/1806.06595/) (2018)
- [Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images](https://mlsanity.com/paper/1712.06203/) (2017)
- [Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy](https://mlsanity.com/paper/1908.04809/) (2019)

## Built on by (most cited first, 28 of 28)

- [SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy](https://mlsanity.com/paper/2303.16320/) (2023)
- [SynthRAD2025 Grand Challenge dataset: generating synthetic CTs for radiotherapy](https://mlsanity.com/paper/2502.17609/) (2025)
- [Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation](https://mlsanity.com/paper/2407.00752/) (2024)
- [FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis](https://mlsanity.com/paper/2412.06690/) (2024)
- [Deformation equivariant cross-modality image synthesis with paired non-aligned training data](https://mlsanity.com/paper/2208.12491/) (2022)
- [Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review](https://mlsanity.com/paper/2403.18565/) (2024)
- [Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration](https://mlsanity.com/paper/2510.21358/) (2025)
- [Generalizable synthetic MRI with physics-informed convolutional networks](https://mlsanity.com/paper/2305.12570/) (2023)
- [Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review](https://mlsanity.com/paper/2509.17790/) (2025)
- [Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis](https://mlsanity.com/paper/2303.10202/) (2023)
- [Deformation-aware GAN for Medical Image Synthesis with Substantially Misaligned Pairs](https://mlsanity.com/paper/2408.09432/) (2024)
- [HC3L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis](https://mlsanity.com/paper/2411.01575/) (2024)
- [DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy](https://mlsanity.com/paper/2604.12778/) (2026)
- [Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion](https://mlsanity.com/paper/2107.05238/) (2021)
- [Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network](https://mlsanity.com/paper/2203.16288/) (2022)
- [A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck](https://mlsanity.com/paper/2506.08654/) (2025)
- [Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma](https://mlsanity.com/paper/2603.08385/) (2026)
- [Cross Modality Image Translation In Medical Imaging Using Generative Frameworks](https://mlsanity.com/paper/2605.13686/) (2026)
- [Structure Unbiased Adversarial Model for Medical Image Segmentation](https://mlsanity.com/paper/2205.12857/) (2022)
- [AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality](https://mlsanity.com/paper/2505.00308/) (2025)
- [DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT](https://mlsanity.com/paper/2511.12098/) (2025)
- [Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research](https://mlsanity.com/paper/2512.03054/) (2025)
- [RAFM: Retrieval-Augmented Flow Matching for Unpaired CBCT-to-CT Translation](https://mlsanity.com/paper/2603.00535/) (2026)
- [VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation](https://mlsanity.com/paper/2604.22942/) (2026)
- [EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis](https://mlsanity.com/paper/2605.20470/) (2026)
- [WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis](https://mlsanity.com/paper/2607.06234/) (2026)
- [Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge](https://mlsanity.com/paper/2608.15721/) (2026)
- [TotalSynth: Robust Whole-Body Synthetic CT from MRI and CBCT](https://mlsanity.com/paper/2609.13838/) (2026)

## Abstract (arXiv)

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.