揭示脑轴突组织复杂性的MRI信号处理新方法。

IF 2.4 4区 医学 Q3 ENGINEERING, BIOMEDICAL Physical and Engineering Sciences in Medicine Pub Date : 2024-12-23 DOI:10.1007/s13246-024-01504-y
Ashishi Puri, Sanjeev Kumar
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引用次数: 0

摘要

本文介绍了一种创新的方法来揭示大脑白质纤维通路的复杂性,使用扩散MRI。基于传统方法观察到信号强度值在高扩散率方向上显著下降的基本原理,我们的新方法战略性地选择了与信号强度降低一致的扩散敏感梯度方向(dSGDs,代表信号产生的方向)。通过将这些选择的方向作为最大扩散率方向,我们在它们周围生成均匀分布的梯度方向(GDs),这些梯度方向随后被用于重建过程。这种方法解决了现有方法的缺点。它改进了均匀梯度方向法(UGDs)和自适应梯度方向法(AGDs),前者存在梯度方向冗余,后者需要每体素求解两次线性系统。所提出的方法同时解决了这两个限制,提供了更有效和精简的过程。我们提出的方法的有效性通过模拟和涉及真实数据的实验进行了严格的评估,展示了其在揭示大脑中复杂的白质纤维通路方面的卓越性能。
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A novel approach in MRI signal processing for unveiling the intricacies of brain axonal organization.

This article introduces an innovative methodology to unveil the intricacies of white matter fiber pathways in the brain using diffusion MRI. Relying on the rationale that traditional methods observe a significant decrease in signal intensity values in the direction of higher diffusivity, our novel approach strategically selects for diffusion-sensitizing gradient directions (dSGDs, representing the directions along which signals are generated) aligned with reduced signal intensities. By treating these selected directions as maximum diffusivity directions, we generate uniformly distributed gradient directions (GDs) around them, which are subsequently employed in the reconstruction process. This approach addresses the shortcomings of existing methods. It improves upon the uniform gradient directions (UGDs) approach, which suffers from gradient direction redundancy, and the adaptive gradient directions (AGDs) approach, which requires solving the linear system twice per voxel. Proposed method simultaneously addresses both limitations, offering a more efficient and streamlined process. The effectiveness of our proposed methodology is rigorously evaluated through simulations and experiments involving real data, showcasing its superior performance in uncovering the complex white matter fiber pathways in the brain.

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来源期刊
CiteScore
8.40
自引率
4.50%
发文量
110
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