Neugebauer Mathias, Glöckler Martin, Goubergrits Leonid, Kelm Marcus, Kuehne Titus, Hennemuth Anja
Fraunhofer Institute for Medical Image Computing - MEVIS, Universitätsallee 29, 28359, Bremen, Germany.
University Hospital Erlangen - Pediatric Cardiology, Erlangen, Germany.
Int J Comput Assist Radiol Surg. 2016 Jan;11(1):133-44. doi: 10.1007/s11548-015-1220-3. Epub 2015 May 16.
The coarctation of the aorta (CoA), a local narrowing of the aortic arch, accounts for 7 % of all congenital heart defects. Stenting is a recommended therapy to reduce the pressure gradient. This procedure is associated with complications such as the development of adverse flow conditions. A computer-aided treatment planning based on flow simulations can help to predict possible complications. The virtual stent planning is an important, intermediate step in the treatment planning pipeline. We present a novel approach that automatically suggests a stent setup and provides a set of intuitive parameters that allow for an interactive adaption of the suggested stent placement and induced deformation.
A high-quality mesh and a centerline are automatically generated. The stent-induced deformation is realized through a deformation of the centerline and a vertex displacement with respect to the deformed centerline and additional stent parameters. The parameterization is automatically derived from the underlying data and can be optionally altered through a condensed set of clinically sound parameters.
The automatic deformation can be generated in about 25 s on a consumer system. The interactive adaption can be performed in real time. Compared with manual expert reconstructions of the stented vessel section, the mean difference of vessel path and diameter is below 1 mm.
Our approach enables a medical user to easily generate a plausibly deformed vessel mesh which is necessary as input for a simulation-based treatment planning of CoA.
主动脉缩窄（CoA）是主动脉弓的局部狭窄，占所有先天性心脏缺陷的7%。支架置入是降低压力梯度的推荐治疗方法。该手术会引发诸如不良血流状况发展等并发症。基于血流模拟的计算机辅助治疗规划有助于预测可能的并发症。虚拟支架规划是治疗规划流程中的一个重要中间步骤。我们提出一种新颖的方法，该方法能自动建议支架设置，并提供一组直观的参数，允许对建议的支架放置和诱导变形进行交互式调整。
自动生成高质量网格和中心线。支架诱导的变形通过中心线的变形以及相对于变形中心线的顶点位移和其他支架参数来实现。参数化是从基础数据中自动推导得出的，并且可以通过一组精简的临床合理参数进行选择性更改。
在消费级系统上大约25秒即可生成自动变形。交互式调整可实时进行。与手动专家对支架置入血管段的重建相比，血管路径和直径的平均差异低于1毫米。
我们的方法使医疗用户能够轻松生成合理变形的血管网格，这是基于模拟的CoA治疗规划所需的输入。
