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心脏 CT 的堆叠转换伪影去除(STAR)。

Stack transition artifact removal (STAR) for cardiac CT.

机构信息

X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.

Siemens Healthineers, 91301, Forchheim, Germany.

出版信息

Med Phys. 2019 Nov;46(11):4777-4791. doi: 10.1002/mp.13786. Epub 2019 Sep 21.

Abstract

INTRODUCTION

In cardiac computed tomography (CT), irregular motion may lead to unique artifacts for scanners with a longitudinal collimation that does not cover the entire heart. Given partial coverage, subvolumes, or stacks, may be reconstructed and used to assemble a final CT volume. Irregular motion, for example, due to cardiac arrhythmia or breathing, may cause mismatch between neighboring stacks and therefore discontinuities within the final CT volume. The aim of this work is the removal of the discontinuities that are hereafter referred to as stack transition artifacts.

METHOD AND MATERIALS

A stack transition artifact removal (STAR) is achieved using a symmetric deformable image registration. A symmetric Demons algorithm was implemented and applied to stacks to remove mismatch and therefore the stack transition artifacts. The registration can be controlled with one parameter that affects the smoothness of the deformation vector field (DVF). The latter is crucial for realistically transforming the stacks. Different smoothness settings as well as an entirely automatic parameter selection that considers the required deformation magnitude for each registration were tested with patient data. Thirteen datasets were evaluated. Simulations were performed on two additional datasets.

RESULTS AND CONCLUSION

STAR considerably improved image quality while computing realistic DVFs. Discontinuities, for example, appearing as breaks or cuts in coronary arteries or cardiac valves, were removed or considerably reduced. A constant smoothing parameter that ensured satisfactory results for all datasets was found. The automatic parameter selection was able to find a proper setting for each individual dataset. Consequently, no over regularization of the DVF occurred that would unnecessarily limit the registration accuracy for cases with small deformations. The automatic parameter selection yielded the best overall results and provided a registration method for cardiac data that does not require user input.

摘要

简介

在心脏计算机断层扫描(CT)中,不规则运动会导致具有不完全覆盖整个心脏的纵向准直的扫描仪产生独特的伪影。由于部分覆盖,可能会重建子体积或堆栈,并将其用于组装最终的 CT 体积。例如,由于心律失常或呼吸,不规则运动会导致相邻堆栈之间不匹配,从而导致最终 CT 体积内部出现不连续性。这项工作的目的是消除以下称为堆栈过渡伪影的不连续性。

方法和材料

使用对称变形图像配准来实现堆栈过渡伪影去除(STAR)。实现了对称的 Demons 算法,并将其应用于堆栈以消除不匹配,从而消除堆栈过渡伪影。可以使用一个影响变形矢量场(DVF)平滑度的参数来控制配准。后者对于真实地转换堆栈至关重要。使用患者数据测试了不同的平滑度设置以及完全自动的参数选择,该参数选择考虑了每个配准所需的变形幅度。评估了 13 个数据集。在另外两个数据集上进行了模拟。

结果和结论

STAR 大大改善了图像质量,同时计算出了逼真的 DVF。例如,在冠状动脉或心脏瓣膜中出现的不连续性,如断裂或切割,得到了消除或大大减少。发现了一个恒定的平滑参数,该参数可确保所有数据集的结果令人满意。自动参数选择能够为每个单独的数据集找到适当的设置。因此,不会发生不必要地限制小变形情况下配准精度的过度正则化。自动参数选择产生了最佳的整体结果,并为不需要用户输入的心脏数据提供了一种配准方法。

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