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A scoping review of magnetic resonance angiography and perfusion image synthesis
Rémi Lamontagne-Caron1,2, Simon Duchesne1,3
1Centre de recherche de l'institut universitaire en cardiologie et pneumologie de Québec, Québec, QC, Canada.
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.
Insights
Artificial intelligence can generate synthetic medical images for studying the cerebrovascular system and neurodegeneration. This approach aids Alzheimer's disease research when direct imaging is not possible.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Cerebrovascular System
Background:
- Cerebrovascular system deregulation is linked to neurodegeneration and Alzheimer's disease (AD).
- Time-of-flight magnetic resonance angiography (TOF-MRA) and perfusion MRI are crucial for studying the cerebrovascular system in AD.
- Resource limitations often prevent large-scale AD studies from acquiring these vital imaging modalities.
Purpose of the Study:
- To review the application of artificial intelligence (AI) in generating synthetic TOF-MRA and perfusion MRI images.
- To explore the potential of AI-driven image synthesis for cerebrovascular research, especially in AD.
- To assess the feasibility of using AI to overcome data acquisition challenges in neuroimaging studies.
Main Methods:
- Conducted a scoping review following PRISMA guidelines.
- Screened 729 studies related to synthetic TOF-MRA or perfusion imaging.
- Included 13 studies that met the review's criteria for AI-based image synthesis.
Main Results:
- T1-weighted, T2-weighted, and FLAIR MRI sequences can be used to synthesize TOF-MRA and perfusion maps.
- Synthetic images may exhibit improved signal-to-noise ratios compared to original scans.
- AI models trained on healthy subjects demonstrated generalization capabilities to patient populations, including stroke survivors.
Conclusions:
- AI-generated synthetic TOF-MRA and perfusion MRI hold significant potential for neurovascular studies, particularly for retrospective analyses of cerebrovascular diseases like stroke and AD.
- This AI-driven approach can enhance research in cases where direct imaging acquisition is not feasible, providing valuable insights into the cerebrovascular system's role in dementia.
- Further research is required to validate the sensitivity, specificity, and generalizability of these synthetic imaging techniques across diverse populations.
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