Deep learning based synthesis of MRI, CT and PET: Review and analysis

Dayarathna, Sanuwani; Islam, Kh Tohidul; Uribe, Sergio; Yang, Guang; Hayat, Munawar; Chen, Zhaolin

doi:10.1016/j.media.2023.103046

reviewMedical Image AnalysisDec 1, 2023HYBRID OA

Deep learning based synthesis of MRI, CT and PET: Review and analysis

SDSanuwani Dayarathna KTKh Tohidul Islam SUSergio Uribe GYGuang Yang MHMunawar Hayat

Monash University · Imperial College London

PubMed

Indexed incrossrefpubmed

Abstract

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…

Citation impact

203

total citations

FWCI: 45.36
Percentile: 100%
References: 260

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Authors

6

Topics & keywords

Topics

Keywords

Deep learning
Computer science
Artificial intelligence
Workflow
Medical imaging
Modality (human–computer interaction)
Modalities
Positron emission tomography

UN Sustainable Development Goals

Peace, Justice and strong institutions

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