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Pulmonary nodule registration: Rigid or nonrigid?

机译:肺结节注册:刚性还是非刚性?

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摘要

>Purpose: The primary aim of this study is to investigate the performance difference of rigid and nonrigid registration schemes in matching corresponding pulmonary nodules depicted on sequential chest computed tomography (CT) examinations.>Methods: A gradient descent based rigid registration algorithm with scaling was developed and it handled the involved geometric transformations (i.e., translation, rescaling, shearing, and rotation) separately instead of optimizing them in a single pass. Given two lung CT examinations, the scaling and translation parameters were simply estimated from the lung volume dimensions (e.g., size and mass center), while the rotation parameters were optimized progressively using gradient descent. To investigate the performance difference of rigid and nonrigid schemes in pulmonary nodule registration, the well-known nonrigid Demons algorithm was implemented and tested along with the developed schemes against 60 diverse low-dose clinical lung CT examinations with average 2-yr follow-up scans. A verified cancer and its correspondence in the follow-up scan as well as their spatial locations (mass center) were identified in each examination. In addition to the computational efficiency, the accuracy of these registration procedures was assessed by computing the Euclidean distances between the corresponding nodules after the registration. To demonstrate the advantage of the developed algorithm, the authors also implemented a fast iterative closest point (ICP) based rigid algorithm and compared their performance.>Results: Our experiments on the collected chest CT examinations showed that the nodule registration errors in 3D Euclidean distance for the developed rigid affine approach, the traditional ICP algorithm, and the refining nonrigid Demons algorithm were 9.6, 9.8, and 10.0 mm, respectively, and the corresponding computational costs in time were 5, 300, and 55 s, respectively.>Conclusions: A rigid solution may be preferred in practice for the pulmonary nodule registration in longitudinal studies because of its relatively high efficiency and sufficient accuracy for the clinical need.
机译:>目的:本研究的主要目的是调查在连续胸部计算机断层扫描(CT)检查中描绘的相应肺结节与刚性和非刚性配准方案的性能差异。>方法:开发了基于梯度下降的带有缩放的刚性配准算法,该算法可分别处理所涉及的几何变换(即平移,重新缩放,剪切和旋转),而不是一次性优化它们。进行两次肺部CT检查,只需从肺部体积尺寸(例如大小和质心)估算缩放和平移参数,然后使用梯度下降逐步优化旋转参数。为了研究刚性和非刚性方案在肺结节注册中的性能差异,实施了众所周知的非刚性恶魔算法,并与已开发的方案一起针对平均两年随访的60种不同的低剂量临床肺部CT检查进行了测试。在每次检查中都确定了已核实的癌症及其在后续扫描中的对应性以及它们的空间位置(质量中心)。除了计算效率外,还通过计算配准后相应结节之间的欧几里得距离来评估这些配准程序的准确性。为了证明开发算法的优势,作者还实现了基于快速迭代最近点(ICP)的刚性算法并比较了它们的性能。>结果:我们对收集的胸部CT检查进行的实验表明,结节改进的刚性仿射方法,传统ICP算法和精细非刚性Demons算法在3D欧氏距离中的配准误差分别为9.6、9.8和10.0 mm,相应的时间计算成本为5、300和55 s >结论:在纵向研究中,刚性溶液在实践中可能是首选的肺结节注册方法,因为其相对较高的效率和足够的临床准确性。

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