BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, 54100 Massa, Italy.
Department of Information Engineering, University of Pisa, 56122 Pisa, Italy.
Sensors (Basel). 2024 Feb 7;24(4):1072. doi: 10.3390/s24041072.
The multimodal and multidomain registration of medical images have gained increasing recognition in clinical practice as a powerful tool for fusing and leveraging useful information from different imaging techniques and in different medical fields such as cardiology and orthopedics. Image registration could be a challenging process, and it strongly depends on the correct tuning of registration parameters. In this paper, the robustness and accuracy of a landmarks-based approach have been presented for five cardiac multimodal image datasets. The study is based on 3D Slicer software and it is focused on the registration of a computed tomography (CT) and 3D ultrasound time-series of post-operative mitral valve repair. The accuracy of the method, as a function of the number of landmarks used, was performed by analysing root mean square error (RMSE) and fiducial registration error (FRE) metrics. The validation of the number of landmarks resulted in an optimal number of 10 landmarks. The mean RMSE and FRE values were 5.26 ± 3.17 and 2.98 ± 1.68 mm, respectively, showing comparable performances with respect to the literature. The developed registration process was also tested on a CT orthopaedic dataset to assess the possibility of reconstructing the damaged jaw portion for a pre-operative planning setting. Overall, the proposed work shows how 3D Slicer and registration by landmarks can provide a useful environment for multimodal/unimodal registration.
医学图像的多模态和多领域配准在临床实践中得到了越来越多的认可,它是融合和利用来自不同成像技术和不同医学领域(如心脏病学和骨科)的有用信息的有力工具。图像配准可能是一个具有挑战性的过程,它强烈依赖于正确调整配准参数。本文提出了一种基于特征点的方法,用于五个心脏多模态图像数据集。该研究基于 3D Slicer 软件,专注于术后二尖瓣修复的计算机断层扫描(CT)和 3D 超声时间序列的配准。该方法的准确性(作为使用特征点数的函数)通过分析均方根误差(RMSE)和基准点配准误差(FRE)指标来评估。通过验证特征点数,得到了 10 个特征点的最佳数量。平均 RMSE 和 FRE 值分别为 5.26 ± 3.17mm 和 2.98 ± 1.68mm,与文献相比表现相当。所开发的配准过程还在 CT 骨科数据集上进行了测试,以评估在术前规划环境中重建受损颌骨部分的可能性。总体而言,所提出的工作展示了 3D Slicer 和基于特征点的配准如何为多模态/单模态配准提供有用的环境。