Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
Monash Biomedical Imaging, Clayton VIC 3800, Australia.
Med Image Anal. 2024 Feb;92:103046. doi: 10.1016/j.media.2023.103046. Epub 2023 Dec 1.
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.
医学图像合成是临床决策制定中的一个关键研究领域，旨在克服获取多种图像模态以实现准确临床工作流程的挑战。这种方法在最常见的医学成像对比中，如计算机断层扫描（CT）、磁共振成像（MRI）和正电子发射断层扫描（PET）中，从给定的源模态估计所需模态的图像时非常有益。然而，由于复杂和非线性的域映射，在两种图像模态之间进行转换存在困难。基于深度学习的生成模型在合成图像对比度应用方面的表现优于传统的图像合成方法。本综述全面回顾了 2018 年至 2023 年基于伪 CT、合成 MR 和合成 PET 的基于深度学习的医学成像翻译。我们提供了医学成像中合成对比度的概述以及用于医学图像合成的最常用的深度学习网络。此外，我们对每种合成方法进行了详细分析，重点关注基于输入域和网络架构的不同模型设计。我们还分析了新颖的网络架构，从传统的 CNN 到最近的 Transformer 和 Diffusion 模型。这一分析包括比较损失函数、可用数据集和解剖区域，以及图像质量评估和在其他下游任务中的性能。最后，我们讨论了文献中的挑战和确定了解决方案，提出了可能的未来方向。我们希望本综述论文中提供的见解将成为医学图像合成领域研究人员的宝贵路线图。
