Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IO, 52242, USA.
IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IO, 52242, USA.
Med Biol Eng Comput. 2018 Mar;56(3):355-371. doi: 10.1007/s11517-017-1690-2. Epub 2017 Aug 1.
Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD. The GPU version demonstrated an average of 43 times speedup and ~5.2 times speedup over the single-threaded and 12-threaded CPU versions, respectively. Run times with the GPU were as fast as 2 min. The symmetric method improved the inverse consistency, aiding the use of image registration in the QCT-based evaluation of the lung.
肺部定量计算机断层扫描（QCT）在识别先前归入慢性阻塞性肺疾病和哮喘等广泛类别的病理学亚表型方面发挥着越来越重要的作用。用于图像匹配和链接多个肺容积的方法已被证明可用于将结构与功能联系起来，并识别区域纵向变化。在这里，我们通过使用对称多级非刚性配准来提高图像匹配的准确性，该方法采用反向一致（IC）变换的对称多水平非刚性配准，图像可以正向和反向注册。为了开发对称方法，使用了两种相似性度量，平方强度差（SSD）和平方组织体积差（SSTVD）的总和。该方法基于一种新颖的通用数学框架，包括正向和反向变换，同时消除了计算逆变换的需要。使用两种实现来评估所提出的方法：使用 SSD 的二维（2-D）实现和用于三维（3-D）人类肺部数据集的多核 CPU 和图形处理单元（GPU）实现的 SSTVD 的多核心 CPU 和 GPU 实现（六个正常成年人在总肺容量（TLC）和功能残气量（FRC）下进行研究）。根据 IC 变换一致性评估成功，以将 TLC 链接到 FRC。使用对称和非对称 SSD 方法对合成图像进行 2-D 配准，并比较位移场，结果表明对称方法给出了对称网格形状并减少了 IC 误差，IC 误差的平均值降低了 37%。对于人类数据集的对称和非对称变换的结果都表明，在所有情况下，对称方法都能更好地获得 IC 误差，使用 SSD 和 SSTVD 的对称方法的 IC 误差平均值均低于非对称方法。GPU 版本的平均速度分别比单线程和 12 线程 CPU 版本快 43 倍和快 5.2 倍，GPU 的运行时间与 2 分钟一样快。对称方法提高了反向一致性，有助于在基于 QCT 的肺部评估中使用图像配准。
