基于ct的肝脏肿瘤消融导航指导

J. Alpers, C. Hansen, K. Ringe, C. Rieder
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引用次数: 4

摘要

图像引导热消融程序,如微波消融(MWA)或射频消融(RFA)已成为临床接受的治疗肝脏肿瘤的选择。这些微创手术的目的是使用针状的器械破坏局灶性肝恶性肿瘤。计算机断层扫描(CT)成像可用于引导涂抹器到目标位置,以实现完全的肿瘤消融。由于图像质量和分辨率有限,在介入内ct图像中可能很难看到治疗目标和风险结构,从而阻碍了对预期涂抹器位置的验证。在这项工作中,我们提出了一种仅基于CT图像的导航引导方法,以支持医生通过附加信息到达目标位置。因此,从介入前图像中提取的规划信息与当前的介入内图像融合。从介入图像中半自动提取可见涂抹器。针具定位通过路径显示、解剖结构投影和纠正建议来指导医生。在一项评估中,我们证明了该方法在提高复杂肝脏肿瘤消融的临床成功率,同时提高准确性并减少所需的介入内CT图像数量方面的潜力。•以人为本的计算→科学可视化;•计算方法→对象检测;•应用计算→卫生信息学;
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CT-Based Navigation Guidance for Liver Tumor Ablation
Image-guided thermal ablation procedures such as microwave ablation (MWA) or radiofrequency ablation (RFA) have become clinically accepted treatment options for liver tumors. The goal of these minimally invasive procedures is the destruction of focal liver malignancies using mostly needle-shaped instruments. Computed tomography (CT) imaging may be used to navigate the applicator to the target position in order to achieve complete tumor ablation. Due to limited image quality and resolution, the treatment target and risk structures may be hardly visible in intra-interventional CT-images, hampering verification of the intended applicator position. In this work, we propose a navigation guidance method based only on CT images to support the physician with additional information to reach the target position. Therefore, planning information extracted from pre-interventional images is fused with the current intra-interventional image. The visible applicator is extracted semi-automatically from the intra-interventional image. The localization of the needle instrument is used to guide the physician by display of the pathway, projection of anatomical structures, and correction suggestions. In an evaluation, we demonstrate the potential of the proposed method to improve the clinical success rate of complex liver tumor ablations while increasing the accuracy and reducing the number of intra-interventional CT images needed. CCS Concepts •Human-centered computing → Scientific visualization; •Computing methodologies → Object detection; •Applied computing → Health informatics;
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