Landman Bennett A, Wan Hanlin, Bogovic John A, Bazin Pierre-Louis, Prince Jerry L
Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA 21218.
Proc SPIE Int Soc Opt Eng. 2010;7623:76231H. doi: 10.1117/12.844171.
Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. Yet, DTI suffers serious limitations in regions of crossing fibers because traditional tensor techniques cannot represent multiple, independent intra-voxel orientations. Compressed sensing has been proposed to resolve crossing fibers using a tensor mixture model (e.g., Crossing Fiber Angular Resolution of Intra-voxel structure, CFARI). Although similar in spirit to deconvolution approaches, CFARI uses sparsity to stabilize estimation with limited data rather than spatial consistency or limited model order. Here, we extend the CFARI approach to resolve crossing fibers through a strictly positive, parsimonious mixture model. Together with an optimized preconditioned conjugate gradient solver, estimation error and computational burden are greatly reduced over the initial presentation. Reliable estimates of intra-voxel orientations are demonstrated in simulation and in vivo using data representative of typical, low b-value (30 directions, 700 s/mm(2)) clinical DTI protocols. These sequences are achievable in 5 minutes at 3 T, and the whole brain CFARI analysis is tractable for routine analysis. With these improvements, CFARI provides a robust framework for identifying intra-voxel structure with traditional DTI and shows great promise in helping to resolve the crossing fiber problem in current clinical imaging studies.
扩散张量成像（DTI）被广泛用于描述组织微观结构和脑连接性。然而，DTI在交叉纤维区域存在严重局限性，因为传统张量技术无法表示多个独立的体素内取向。有人提出使用张量混合模型（如体素内结构交叉纤维角分辨率，CFARI）的压缩感知来解决交叉纤维问题。尽管在理念上与反卷积方法类似，但CFARI利用稀疏性在有限数据下稳定估计，而非空间一致性或有限模型阶数。在此，我们通过一个严格正定、简洁的混合模型扩展CFARI方法来解决交叉纤维问题。结合优化的预处理共轭梯度求解器，与最初的方法相比，估计误差和计算负担大幅降低。在模拟和体内实验中，使用代表典型低b值（30个方向，700 s/mm(2)）临床DTI协议的数据，证明了体素内取向的可靠估计。这些序列在3 T场强下5分钟即可完成采集，全脑CFARI分析便于进行常规分析。通过这些改进，CFARI为利用传统DTI识别体素内结构提供了一个强大的框架，并在帮助解决当前临床成像研究中的交叉纤维问题方面显示出巨大潜力。
