Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview

Frontiers in Neurology

Neurology

Md Nasir Uddin¹  Abrar Faiyaz²  Marvin M. Doyley³  Giovanni Schifitto⁴ 

[1] Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States;Department of Neurology, University of Rochester, Rochester, NY, United States;Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States;Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States;Department of Imaging Sciences, University of Rochester, Rochester, NY, United States;Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States;Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States;Department of Imaging Sciences, University of Rochester, Rochester, NY, United States;Department of Neurology, University of Rochester, Rochester, NY, United States;

关键词: artificial intelligence;    machine learning;    deep learning;    diffusion MRI (dMRI);    neuroimaging;    brain;    microstructure;    biophysical model;   

DOI  :  10.3389/fneur.2023.1168833

 received in 2023-02-18, accepted in 2023-03-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

【 授权许可】

Unknown   
Copyright © 2023 Faiyaz, Doyley, Schifitto and Uddin.