背景:CT扫描通常是大脑成像的第一种也是唯一一种形式,由于其具有时间和成本效益的性质,因此可以为神经系统患者的治疗计划提供信息。然而,MR图像可提供更详细的组织结构和特征图片,并且更有可能发现异常和病变。本文的目的是回顾使用深度学习方法生成MRI和CT等模态的合成医学图像的研究。
方法:于2023年3月进行了文献检索,并选择并分析了相关文章。出版的那一年,数据集大小,输入模态,综合模态,深度学习架构,动机,并对评价方法进行了分析。
结果:本综述共纳入103项研究,所有这些都是自2017年以来发布的。其中,74%的研究调查了MRI到CT合成,其余的研究调查了CT到MRI,交叉核磁共振,PET到CT,核磁共振到PET。此外,58%的研究是通过合成MRI的CT扫描来进行仅MRI放射治疗的动机。其他动机包括合成扫描以帮助诊断,并通过合成丢失的扫描来完成数据集。
结论:已经对MRI到CT合成进行了相当多的研究,尽管CT到MRI合成产生特定的益处。医学图像合成的一个限制是医学数据集,尤其是不同模态的配对数据集,缺乏规模和可用性;因此,建议建立一个全球联盟,以获取和提供更多的数据集供使用。最后,建议开展工作,以建立医学扫描合成在临床实践中的所有用途,并发现哪些评估方法适合评估这些需求的合成图像。
BACKGROUND: CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue structure and characteristics and are more likely to pick up abnormalities and lesions. The purpose of this paper is to review studies which use deep learning methods to generate synthetic medical images of modalities such as MRI and CT.
METHODS: A literature search was performed in March 2023, and relevant articles were selected and analyzed. The year of publication, dataset size, input modality, synthesized modality, deep learning architecture, motivations, and evaluation methods were analyzed.
RESULTS: A total of 103 studies were included in this review, all of which were published since 2017. Of these, 74% of studies investigated MRI to CT synthesis, and the remaining studies investigated CT to MRI, Cross MRI, PET to CT, and MRI to PET. Additionally, 58% of studies were motivated by synthesizing CT scans from MRI to perform MRI-only radiation therapy. Other motivations included synthesizing scans to aid diagnosis and completing datasets by synthesizing missing scans.
CONCLUSIONS: Considerably more research has been carried out on MRI to CT synthesis, despite CT to MRI synthesis yielding specific benefits. A limitation on medical image synthesis is that medical datasets, especially paired datasets of different modalities, are lacking in size and availability; it is therefore recommended that a global consortium be developed to obtain and make available more datasets for use. Finally, it is recommended that work be carried out to establish all uses of the synthesis of medical scans in clinical practice and discover which evaluation methods are suitable for assessing the synthesized images for these needs.