Deep learning based synthesis of MRI, CT and PET: Review and analysis Article Swipe
Sanuwani Dayarathna
,
Kh Tohidul Islam
,
Sergio Uribe
,
Guang Yang
,
Munawar Hayat
,
Zhaolin Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.media.2023.103046
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.media.2023.103046
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.media.2023.103046
- OA Status
- hybrid
- Cited By
- 136
- References
- 260
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389221690
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389221690Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.media.2023.103046Digital Object Identifier
- Title
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Deep learning based synthesis of MRI, CT and PET: Review and analysisWork title
- Type
-
reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-01Full publication date if available
- Authors
-
Sanuwani Dayarathna, Kh Tohidul Islam, Sergio Uribe, Guang Yang, Munawar Hayat, Zhaolin ChenList of authors in order
- Landing page
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https://doi.org/10.1016/j.media.2023.103046Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.media.2023.103046Direct OA link when available
- Concepts
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Deep learning, Computer science, Artificial intelligence, Workflow, Medical imaging, Modality (human–computer interaction), Modalities, Positron emission tomography, Image synthesis, Machine learning, Image (mathematics), Radiology, Medicine, Database, Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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136Total citation count in OpenAlex
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2025: 105, 2024: 31Per-year citation counts (last 5 years)
- References (count)
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260Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.estimating | 33 |
| abstract_inverted_index.frequently | 134 |
| abstract_inverted_index.functions, | 188 |
| abstract_inverted_index.generative | 84 |
| abstract_inverted_index.modalities | 22, 71 |
| abstract_inverted_index.non-linear | 79 |
| abstract_inverted_index.pseudo-CT, | 115 |
| abstract_inverted_index.represents | 3 |
| abstract_inverted_index.suggesting | 215 |
| abstract_inverted_index.synthesis. | 142, 243 |
| abstract_inverted_index.Transformer | 179 |
| abstract_inverted_index.assessments | 197 |
| abstract_inverted_index.directions. | 218 |
| abstract_inverted_index.literature, | 214 |
| abstract_inverted_index.performance | 89, 199 |
| abstract_inverted_index.researchers | 236 |
| abstract_inverted_index.translating | 67 |
| abstract_inverted_index.translation | 109 |
| abstract_inverted_index.applications | 94 |
| abstract_inverted_index.conventional | 97, 174 |
| abstract_inverted_index.difficulties | 73 |
| abstract_inverted_index.Additionally, | 143 |
| abstract_inverted_index.architectures, | 171 |
| abstract_inverted_index.architectures. | 165 |
| abstract_inverted_index.learning-based | 83, 106 |
| abstract_inverted_index.comprehensively | 103 |
| abstract_inverted_index.decision-making, | 11 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5088500474 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I56590836 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.99907104 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |