Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
Beijing Shen Mindray Medical Electronics Technology Research Institute Co. Ltd, Beijing 100085, China.
Comput Biol Med. 2016 Dec 1;79:99-109. doi: 10.1016/j.compbiomed.2016.10.008. Epub 2016 Oct 13.
Ultrasound fusion imaging is an emerging tool and benefits a variety of clinical applications, such as image-guided diagnosis and treatment of hepatocellular carcinoma and unresectable liver metastases. However, respiratory liver motion-induced misalignment of multimodal images (i.e., fusion error) compromises the effectiveness and practicability of this method. The purpose of this paper is to develop a subject-specific liver motion model and automatic registration-based method to correct the fusion error.
An online-built subject-specific motion model and automatic image registration method for 2D ultrasound-3D magnetic resonance (MR) images were combined to compensate for the respiratory liver motion. The key steps included: 1) Build a subject-specific liver motion model for current subject online and perform the initial registration of pre-acquired 3D MR and intra-operative ultrasound images; 2) During fusion imaging, compensate for liver motion first using the motion model, and then using an automatic registration method to further correct the respiratory fusion error. Evaluation experiments were conducted on liver phantom and five subjects.
In the phantom study, the fusion error (superior-inferior axis) was reduced from 13.90±2.38mm to 4.26±0.78mm by using the motion model only. The fusion error further decreased to 0.63±0.53mm by using the registration method. The registration method also decreased the rotation error from 7.06±0.21° to 1.18±0.66°. In the clinical study, the fusion error was reduced from 12.90±9.58mm to 6.12±2.90mm by using the motion model alone. Moreover, the fusion error decreased to 1.96±0.33mm by using the registration method.
The proposed method can effectively correct the respiration-induced fusion error to improve the fusion image quality. This method can also reduce the error correction dependency on the initial registration of ultrasound and MR images. Overall, the proposed method can improve the clinical practicability of ultrasound fusion imaging.
超声融合成像是一种新兴的工具，可应用于多种临床领域，如肝癌和不可切除肝转移的影像引导诊断和治疗。然而，多模态图像（即融合误差）的呼吸肝运动导致的失准会影响该方法的有效性和实用性。本文旨在开发一种基于个体的肝运动模型和自动配准方法来纠正融合误差。
采用在线构建的个体肝运动模型和二维超声-三维磁共振（MR）图像自动配准方法来补偿呼吸肝运动。关键步骤包括：1）在线构建当前个体的个体特异性肝运动模型，并对预采集的 3D MR 和术中超声图像进行初始配准；2）在融合成像过程中，首先使用运动模型补偿肝运动，然后使用自动配准方法进一步校正呼吸融合误差。在肝体模和五名患者中进行了评估实验。
在体模研究中，仅使用运动模型可将融合误差（上下轴）从 13.90±2.38mm 减小至 4.26±0.78mm。通过使用配准方法，融合误差进一步减小至 0.63±0.53mm。配准方法还将旋转误差从 7.06±0.21°减小至 1.18±0.66°。在临床研究中，仅使用运动模型可将融合误差从 12.90±9.58mm 减小至 6.12±2.90mm。此外，通过使用配准方法，融合误差减小至 1.96±0.33mm。
所提出的方法可以有效地纠正呼吸引起的融合误差，从而提高融合图像质量。该方法还可以降低对超声和 MR 图像初始配准的误差校正依赖性。总体而言，该方法可以提高超声融合成像的临床实用性。
