Implications of resolution and noise for in vivo micro-MRI of trabecular bone
Implications of resolution and noise for in vivo micro-MRI of trabecular bone
复制标题
DOI：
10.1118/1.3005598
复制
发表时间：
2008-12-01
期刊：
影响因子：
3.8
通讯作者：
Wehrli, Felix W.
中科院分区：
文献类型：
--
作者：
Li, Charles Q.;Magland, Jeremy F.;Wehrli, Felix W.
骨质疏松性骨丢失伴随着骨小梁网络的结构完整性受损，导致骨的总体机械性能降低。“虚拟骨活检”（VBB），磁共振显微成像（μ MRI）和数字图像处理技术相结合的方法的发展，以前已被证明量化的拓扑结构和规模的人骨小梁非侵入性。这项工作的目的是确定在何种程度上获得的图像在有限的空间分辨率制度的体内成像的结构参数是敏感的分辨率和噪声，并进一步，是否在这些条件下，少量的骨丢失及其相关的结构表现可以检测到。为了实现这些目标，在人尸体骨芯的mu CT图像的基础上生成了代表多个解剖位置的松质骨的3D模型。这些图像被二值化，并且通过k空间的笛卡尔采样对表示纯骨（质子密度=0）和纯骨髓（质子密度=255）的所得数据阵列进行模拟MR成像，在3D傅立叶重建之后产生当前在体内可实现的体素尺寸。随后，现实水平的高斯噪声叠加在复杂的数据和震级图像计算。随后对所得图像进行VBB处理，以获得一系列信噪比（SNR）值和图像体素大小。为了将预测的行为与体内数据进行比较，还评价了来自最近患者研究的图像。导出的结构参数随着SNR的降低而逐渐变化的系统性变化被注意到，并且结果表明，使用简单的线性变换，误差是可校正的，从而允许数据被归一化。预测的结构参数对SNR的依赖性也密切平行于在体内观察到的。最后，为了评估VBB处理算法在疾病进展或治疗消退期间检测骨丢失的灵敏度，高分辨率标本数据通过均匀或不均匀侵蚀以及在体内分辨率和SNR下模拟的mu MR图像接受5%骨丢失。在典型的体内SNR（SNR=12）和有效图像分辨率（160 μ m各向同性和137 × 137 × 410 μ m（3））下，VBB算法能够检测出具有高度统计学显著性的骨体积分数损失5%的结构含义。
Osteoporotic bone loss is accompanied by impaired structural integrity of the trabecular network, leading to a decrease in the overall mechanical properties of the bone. The development of the "virtual bone biopsy" (VBB), a method combining magnetic resonance microimaging (mu MRI) and digital image processing techniques, has previously been shown to quantify topology and scale of human trabecular bone noninvasively. The aim of this work was to determine the extent to which structural parameters derived from images acquired in the limited spatial resolution regime of in vivo imaging are sensitive to resolution and noise and further, whether under these conditions, a small amount of bone loss and its associated structural manifestations can be detected. Toward these goals 3D models of trabecular bone representing multiple anatomic locations were generated on the basis of mu CT images of human cadaveric bone cores. These images were binarized and the resulting data arrays representing pure bone (proton density=0) and pure marrow (proton density=255) subjected to simulated MR imaging by Cartesian sampling of k space, yielding, after 3D Fourier reconstruction, voxel sizes currently achievable in vivo. Subsequently, realistic levels of Gaussian noise were superimposed on the complex data and magnitude images were computed. The resulting images were subsequently VBB processed for a range of signal-to-noise ratio (SNR) values and image voxel sizes. For comparison of the predicted behavior to in vivo data, images from a recent patient study were evaluated as well. Systematic changes of the derived structural parameters changing progressively with decreasing SNR were noted, and it is shown that the errors are correctable using simple linear transformations, thereby allowing the data to be normalized. The predicted dependence of the structural parameters on SNR also closely parallel those observed in vivo. Finally, in order to assess the sensitivity of the VBB processing algorithms to detect bone loss during disease progression or regression in response to treatment, the high-resolution specimen data were subjected to 5% bone loss either by homogeneous or heterogeneous erosion and mu MR images simulated at in vivo resolution and SNR. At typical in vivo SNR (SNR=12) and effective image resolution (160 mu m isotropic and 137x137x410 mu m(3)), VBB algorithms were able to detect the structural implications of a 5% loss in bone volume fraction with high statistical significance.