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磁共振血管造影和灌注图像合成的范围综述

A scoping review of magnetic resonance angiography and perfusion image synthesis.

作者信息

Lamontagne-Caron Rémi, Duchesne Simon

机构信息

Centre de recherche de l'institut universitaire en cardiologie et pneumologie de Québec, Québec, QC, Canada.

Département de médecine, Université Laval, Québec, Québec, QC, Canada.

出版信息

Front Dement. 2024 Nov 11;3:1408782. doi: 10.3389/frdem.2024.1408782. eCollection 2024.

Abstract

INTRODUCTION

Deregulation of the cerebrovascular system has been linked to neurodegeneration, part of a putative causal pathway into etiologies such as Alzheimer's disease (AD). In medical imaging, time-of-flight magnetic resonance angiography (TOF-MRA) and perfusion MRI are the most common modalities used to study this system. However, due to lack of resources, many large-scale studies of AD are not acquiring these images; this creates a conundrum, as the lack of evidence limits our knowledge of the interaction between the cerebrovascular system and AD. Deep learning approaches have been used in recent developments to generate synthetic medical images from existing contrasts. In this review, we study the use of artificial intelligence in the generation of synthetic TOF-MRA and perfusion-related images from existing neuroanatomical and neurovascular acquisitions for the study of the cerebrovascular system.

METHOD

Following the PRISMA reporting guidelines we conducted a scoping review of 729 studies relating to image synthesis of TOF-MRA or perfusion imaging, from which 13 met our criteria.

RESULTS

Studies showed that T1-w, T2-w, and FLAIR can be used to synthesize perfusion map and TOF-MRA. Other studies demonstrated that synthetic images could have a greater signal-to-noise ratio compared to real images and that some models trained on healthy subjects could generalize their outputs to an unseen population, such as stroke patients.

DISCUSSION

These findings suggest that generating TOF-MRA and perfusion MRI images holds significant potential for enhancing neurovascular studies, particularly in cases where direct acquisition is not feasible. This approach could provide valuable insights for retrospective studies of several cerebrovascular related diseases such as stroke and AD. While promising, further research is needed to assess their sensitivity and specificity, and ensure their applicability across diverse populations. The use of models to generate TOF-MRA and perfusion MRI using commonly acquired data could be the key for the retrospective study of the cerebrovascular system and elucidate its role in the development of dementia.

摘要

引言

脑血管系统失调与神经退行性变有关,这是通向诸如阿尔茨海默病(AD)等病因的假定因果途径的一部分。在医学成像中,时间飞跃磁共振血管造影(TOF-MRA)和灌注磁共振成像(MRI)是用于研究该系统的最常见方式。然而,由于资源匮乏,许多AD的大规模研究并未获取这些图像;这就产生了一个难题,因为缺乏证据限制了我们对脑血管系统与AD之间相互作用的了解。深度学习方法已在近期的发展中用于从现有对比度生成合成医学图像。在本综述中,我们研究了人工智能在从现有的神经解剖学和神经血管采集生成合成TOF-MRA和灌注相关图像以用于脑血管系统研究方面的应用。

方法

遵循PRISMA报告指南,我们对729项与TOF-MRA或灌注成像的图像合成相关的研究进行了范围综述,其中13项符合我们的标准。

结果

研究表明,T1加权、T2加权和液体衰减反转恢复(FLAIR)图像可用于合成灌注图和TOF-MRA。其他研究表明,与真实图像相比,合成图像可能具有更高的信噪比,并且一些在健康受试者上训练的模型可以将其输出推广到未见过的人群,如中风患者。

讨论

这些发现表明,生成TOF-MRA和灌注MRI图像在增强神经血管研究方面具有巨大潜力,特别是在直接采集不可行的情况下。这种方法可为中风和AD等几种脑血管相关疾病的回顾性研究提供有价值的见解。虽然前景广阔,但需要进一步研究来评估它们的敏感性和特异性,并确保它们在不同人群中的适用性。使用模型通过常用采集数据生成TOF-MRA和灌注MRI可能是脑血管系统回顾性研究的关键,并阐明其在痴呆症发展中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4cd0/11586219/fb30c67997e0/frdem-03-1408782-g0001.jpg

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