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

深度学习 计算机科学 人工智能 工作流程 医学影像学 模态(人机交互) 模式 正电子发射断层摄影术 图像合成 机器学习 图像(数学) 放射科 医学 社会科学 数据库 社会学
作者
Sanuwani Dayarathna,Kh Tohidul Islam,Sergio Uribe,Guang Yang,Munawar Hayat,Zhaolin Chen
出处
期刊:Medical Image Analysis [Elsevier BV]
卷期号:92: 103046-103046 被引量:32
标识
DOI:10.1016/j.media.2023.103046
摘要

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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