TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View

IF 3.5 2区 医学 Q1 NEUROIMAGING
Yuqian Chen, Leo Zekelman, Yui Lo, Suheyla Cetin-Karayumak, Tengfei Xue, Yogesh Rathi, Nikos Makris, Fan Zhang, Weidong Cai, Lauren J. O'Donnell
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引用次数: 0

Abstract

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial, or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV. We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios. This data augmentation approach enriches the training set with realistic truncated streamlines, enabling the model to achieve superior generalization. We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV. TractCloud-FOV significantly outperforms several state-of-the-art methods on all testing datasets in terms of streamline classification accuracy, generalization ability, tract anatomical depiction, and computational efficiency. Overall, TractCloud-FOV achieves efficient and consistent tractography parcellation in diffusion MRI with incomplete FOV.

Abstract Image

TractCloud-FOV:基于深度学习的不完整视场弥散核磁共振成像中的鲁棒切迹解析
纤维束分析法将弥散核磁共振成像重建的流线分类为解剖学定义的纤维束,用于临床和研究应用。然而,临床扫描的视场(FOV)往往不完整,大脑区域被部分成像,从而导致部分或截断的纤维束。为了应对这一挑战,我们引入了 TractCloud-FOV,这是一种深度学习框架,能在不完整视场条件下稳健地对纤维束进行解析。我们提出了一种新颖的训练策略--FOV-Cut Augmentation (FOV-CA),通过这种策略,我们对纤维束图进行了合成切割,以模拟现实世界中各种劣质 FOV 截断情况。这种数据扩增方法利用真实的截断流线丰富了训练集,使模型实现了卓越的泛化。我们在合成切割牵引图和两个不完整 FOV 的真实数据集上评估了所提出的 TractCloud-FOV。在所有测试数据集上,TractCloud-FOV 在流线分类准确性、泛化能力、束解剖描绘和计算效率方面都明显优于几种最先进的方法。总体而言,TractCloud-FOV 在不完整 FOV 的扩散磁共振成像中实现了高效、一致的束学解析。
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来源期刊
Human Brain Mapping
Human Brain Mapping 医学-核医学
CiteScore
8.30
自引率
6.20%
发文量
401
审稿时长
3-6 weeks
期刊介绍: Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged. Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.
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