Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression.
Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression.
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DOI：
10.1016/j.jbiomech.2017.05.004
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发表时间：
2017-06-14
影响因子：
2.4
通讯作者：
D'Souza RM
中科院分区：
文献类型：
--
作者：
Bakhshinejad A;Baghaie A;Vali A;Saloner D;Rayz VL;D'Souza RM
时间分辨相位对比磁共振成像4D-PCMR（也称为4D Flow MRI）数据虽然能够无创测量血流速度，但可能受到采集噪声、流动伪影和分辨率限制的影响。在本文中，我们提出了一种新的方法，用于合并4D Flow MRI与计算流体动力学（CFD），以解决这些限制，并重建去噪，无发散的高分辨率流场。适当的正交分解（POD）被用来构建在4D Flow MRI网格的低分辨率水平和CFD网格的高分辨率水平的所有可能的解决方案的流动方程的空间的局部采样的正交基。通过将体内4D Flow MRI数据投影到低分辨率基向量上来获得低分辨率去噪流。岭回归，然后用于重建高分辨率去噪无发散的解决方案。进一步研究了4D Flow MRI网格分辨率和噪声水平对所得速度场的影响。通过脑动脉瘤的流动的数值模型被用来比较使用POD方法获得的结果与那些获得的国家的最先进的去噪方法。在4D Flow MRI网格分辨率下，POD方法显示出比其他方法更好地保留了小的流动结构，同时消除了噪声。此外，该方法被证明可以成功重建在CFD网格分辨率下的细节，而在4D Flow MRI网格分辨率下无法辨别。该方法将提高从体内4D Flow MRI数据计算的临床相关流量衍生参数（例如压力梯度和壁面剪切应力）的准确性。
Time resolved phase-contrast magnetic resonance imaging 4D-PCMR (also called 4D Flow MRI) data while capable of non-invasively measuring blood velocities, can be affected by acquisition noise, flow artifacts, and resolution limits. In this paper, we present a novel method for merging 4D Flow MRI with computational fluid dynamics (CFD) to address these limitations and to reconstruct de-noised, divergence-free high-resolution flow-fields. Proper orthogonal decomposition (POD) is used to construct the orthonormal basis of the local sampling of the space of all possible solutions to the flow equations both at the low-resolution level of the 4D Flow MRI grid and the high-level resolution of the CFD mesh. Low-resolution, de-noised flow is obtained by projecting in-vivo 4D Flow MRI data onto the low-resolution basis vectors. Ridge regression is then used to reconstruct high-resolution de-noised divergence-free solution. The effects of 4D Flow MRI grid resolution, and noise levels on the resulting velocity fields are further investigated. A numerical phantom of the flow through a cerebral aneurysm was used to compare the results obtained using the POD method with those obtained with the state-of-the-art de-noising methods. At the 4D Flow MRI grid resolution, the POD method was shown to preserve the small flow structures better than the other methods, while eliminating noise. Furthermore, the method was shown to successfully reconstruct details at the CFD mesh resolution not discernible at the 4D Flow MRI grid resolution. This method will improve the accuracy of the clinically relevant flow-derived parameters, such as pressure gradients and wall shear stresses, computed from in-vivo 4D Flow MRI data.
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