Faiyaz Abrar, Doyley Marvin M, Schifitto Giovanni, Uddin Md Nasir
Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States.
Department of Imaging Sciences, University of Rochester, Rochester, NY, United States.
Front Neurol. 2023 Apr 21;14:1168833. doi: 10.3389/fneur.2023.1168833. eCollection 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, 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.
人工智能（AI）在扩散磁共振成像（dMRI）及其他神经成像模态领域取得了重大进展。这些技术已应用于图像重建、去噪、伪影检测与去除、分割、组织微观结构建模、脑连接性分析及诊断支持等各个领域。先进的人工智能算法有潜力利用dMRI中的优化技术，通过生物物理模型提高敏感性和推理能力。虽然人工智能在脑微观结构中的应用有可能彻底改变我们研究大脑和理解脑部疾病的方式，但我们需要意识到可能存在的陷阱以及能推动该领域进一步发展的新兴最佳实践。此外，由于dMRI扫描依赖于q空间几何结构的采样，这为数据工程留下了创新空间，以便最大程度地进行先验推理。利用固有几何结构已被证明可提高一般推理质量，并且在识别病理差异方面可能更可靠。我们使用这些统一特征对基于人工智能的dMRI方法进行了认可和分类。本文还强调并回顾了通过数据驱动技术进行组织微观结构估计的一般实践和陷阱，并为在此基础上进一步发展提供了方向。
