Barati Shoorche Amin, Farnia Parastoo, Makkiabadi Bahador, Leemans Alexander
Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS), Tehran, Iran.
Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Neuroradiology. 2025 Jun 4. doi: 10.1007/s00234-025-03637-7.
Human brain fiber tractography using diffusion magnetic resonance imaging is a crucial stage in mapping brain white matter structures, pre-surgical planning, and extracting connectivity patterns. Accurate and reliable tractography, by providing detailed geometric information about the position of neural pathways, minimizes the risk of damage during neurosurgical procedures.
Both tractography itself and its post-processing steps such as bundle segmentation are usually used in these contexts. Many approaches have been put forward in the past decades and recently, multiple data-driven tractography algorithms and automatic segmentation pipelines have been proposed to address the limitations of traditional methods.
Several of these recent methods are based on learning algorithms that have demonstrated promising results. In this study, in addition to introducing diffusion MRI datasets, we review learning-based algorithms such as conventional machine learning, deep learning, reinforcement learning and dictionary learning methods that have been used for white matter tract, nerve and pathway recognition as well as whole brain streamlines or whole brain tractogram creation.
The contribution is to discuss both tractography and tract recognition methods, in addition to extending previous related reviews with most recent methods, covering architectures as well as network details, assess the efficiency of learning-based methods through a comprehensive comparison in this field, and finally demonstrate the important role of learning-based methods in tractography.
利用扩散磁共振成像进行人脑纤维束成像，是绘制脑白质结构、术前规划以及提取连接模式的关键阶段。准确可靠的纤维束成像通过提供有关神经通路位置的详细几何信息，将神经外科手术过程中的损伤风险降至最低。
在这些情况下，通常会使用纤维束成像本身及其诸如束分割等后处理步骤。在过去几十年中已经提出了许多方法，最近，为了解决传统方法的局限性，人们提出了多种数据驱动的纤维束成像算法和自动分割流程。
这些最新方法中有几种是基于学习算法的，这些算法已显示出有前景的结果。在本研究中，除了介绍扩散MRI数据集外，我们还回顾了基于学习的算法，如传统机器学习、深度学习、强化学习和字典学习方法，这些方法已用于白质束、神经和通路识别以及全脑流线或全脑纤维束图创建。
除了用最新方法扩展先前的相关综述，涵盖架构以及网络细节之外，本研究的贡献还在于讨论纤维束成像和纤维束识别方法，通过在该领域的全面比较来评估基于学习的方法的效率，最后展示基于学习的方法在纤维束成像中的重要作用。
