Application of an adaptive control grid interpolation technique to morphological vascular reconstruction
Application of an adaptive control grid interpolation technique to morphological vascular reconstruction
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DOI：
10.1109/tbme.2002.807651
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发表时间：
2003-02-01
影响因子：
4.6
通讯作者：
Yoganathan, AP
中科院分区：
文献类型：
--
作者：
Frakes, DH;Conrad, CP;Yoganathan, AP
间隙磁共振 (MR) 图像重建问题在广泛的医疗应用中出现。在这种情况下，需要近似原始对象中存在的信息，由于硬件采样限制，这些信息未反映在连续采集的 MR 图像中。在血管形态重建的背景下，需要这些信息才能使后续的血管可视化和计算分析最有效。为此，我们开发了一种基于自适应控制网格插值（ACGI）的血管形态重建方法，作为可视化和计算分析的先驱。 ACGI 之前已被用于解决包括视频编码和跟踪在内的各种问题。本文重点讨论该技术在医学图像处理中的新颖应用。 ACGI 结合了基于光学的运动估计算法和基于块的运动估计算法的功能，以最小程度的计算复杂性准确地增强密度不足的 MR 数据集。由此产生的增强数据集描述了血管的几何形状。这些重建结果可以用作可视化工具，并与计算流体动力学 (CFD) 模拟结合使用，以提供量化功率损耗所需的压力和速度信息。所提出的 ACGI 方法预计最终将在手术规划中发挥作用，旨在为成功的手术结果产生最佳的血管配置。
The problem of interstice magnetic resonance (MR) image reconstruction arises in a broad range of medical applications. In such cases, there is a need to approximate information present in the original subject that is not reflected in contiguously acquired MR images because of hardware sampling limitations. In the context of vascular morphology reconstruction, this information is required in order for subsequent visualization and computational analysis of blood vessels to be most effective. Toward that end we have developed a method of vascular morphology reconstruction based on adaptive control grid interpolation (ACGI) to function as a precursor to visualization and computational analysis. ACGI has previously been implemented in addressing various problems including video coding and tracking. This paper focuses on the novel application of the technique to medical image processing. ACGI combines features of optical How-based and blockbased motion estimation algorithms to enhance insufficiently dense MR data sets accurately with a minimal degree of computational complexity. The resulting enhanced data sets describe vascular geometries. These reconstructions can then be used as visualization tools and in conjunction with computational fluid dynamics (CFD) simulations to offer the pressure and velocity information necessary to quantify power loss. The proposed ACGI methodology is envisioned ultimately to play a role in surgical planning aimed at producing optimal vascular configurations for successful surgical outcomes.