Bifurcated Topological Optimization for IVIM.

Bifurcated Topological Optimization for IVIM.
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DOI:
10.3389/fnins.2021.779025
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发表时间:
2021
影响因子:
4.3
通讯作者:
Garyfallidis E
Garyfallidis E
中科院分区:
医学2区
文献类型:
--
作者:
Fadnavis S;Endres S;Wen Q;Wu YC;Cheng H;Koudoro S;Rane S;Rokem A;Garyfallidis E

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在这项工作中,我们揭示的问题,估计体素内非相干运动(IVIM)的扩散和灌注估计的目标函数的特点,使用单纯的同源工具。通过对模型进行拓扑优化,我们给出了一个鲁棒的解,使估计更加可靠和准确。从扩散MRI估计组织微结构本身是一个不适定的非线性逆问题。使用可变投影泛函(VarPro)来拟合标准的双指数IVIM模型,我们使用基于单纯同调的全局优化来执行优化,以更好地理解目标函数表面的拓扑结构。我们从理论上展示了所提出的方法如何通过将其投射到VarPro给出的简化子空间中来更准确和一致地恢复模型参数。此外,我们证明了IVIM模型参数不能准确地重建使用传统的数值优化方法,由于在子空间中的无穷多个解决方案的存在。所提出的方法有助于发现多个全球最小值,通过分析模型的局部几何形状,使模型参数的可靠估计的生成。
In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.
用扩散MRI量化脑微观结构:理论和参数估计。
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