Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview - Faiyaz, Abrar; Doyley, Marvin M.; Schifitto, Giovanni; Uddin, Md Nasir (Frontiers in Neurology, 2023)
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, detection and removal of 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. Further, 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 the quality of inference in general and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI through these unifying characteristics. This article also highlights and reviews general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and directions to build on them.