The Ohio State University, Davis Heart and Lung Research Institute, Columbus, OH 43210, USA.
Magn Reson Imaging. 2013 Jan;31(1):44-52. doi: 10.1016/j.mri.2012.06.006. Epub 2012 Aug 22.
T(2) quantification has been shown to noninvasively and accurately estimate tissue iron content in the liver and heart; applying this to thin-walled carotid arteries introduces a new challenge to the estimation process. With most imaging voxels in a vessel being along its boundaries, errors in parameter estimation may result from partial volume mixing and misregistration due to motion in addition to noise and other common error sources. To minimize these errors, we propose a novel technique to reliably estimate T(2) in thin regions of vessel wall. The technique weights data points to reduce the influence of expected error sources. It uses neighborhoods of data to increase the number of points for fitting and to assess lack of fit for automated outlier detection and deletion. The performance of this method was observed in simulations, phantom and in vivo patient studies and compared to results obtained using a pixelwise linear least squares estimation of T(2). The new proposed method showed a closer match to the expected results, and a 4.2-fold decrease in interobserver variability for in vivo studies. This increased confidence in estimation should improve the ability to reliably quantify iron noninvasively in the arterial wall.
T2 定量已被证明可以无创且准确地估计肝脏和心脏组织中的铁含量；将其应用于薄壁颈动脉会给估计过程带来新的挑战。由于血管中的大多数成像体素都沿着其边界，因此参数估计中的误差可能来自于除噪声和其他常见误差源之外的部分容积混合和运动导致的配准错误。为了最小化这些误差，我们提出了一种可靠估计血管壁薄区 T2 的新技术。该技术对数据点进行加权，以减少预期误差源的影响。它使用数据的邻域来增加拟合点数，并评估拟合不良情况，以便自动进行异常值检测和删除。在模拟、体模和体内患者研究中观察了该方法的性能，并与使用 T2 的像素线性最小二乘估计获得的结果进行了比较。新提出的方法与预期结果更为吻合，并且体内研究的观察者间变异性降低了 4.2 倍。这提高了对估计的信心，应该能够提高无创定量动脉壁铁含量的能力。
