RANSAC-based global 3DUS to CT/MR rigid registration using liver surface and vessels.

Purpose: Fusion imaging requires initial registration of ultrasound (US) images using computed tomography (CT) or magnetic resonance (MR) imaging. The sweep position of US depends on the procedure. For instance, the liver may be observed in intercostal, subcostal, or epigastric positions. However, no well-established method for automatic initial registration accommodates all positions. A global rigid 3D-3D registration technique aimed at developing an automatic registration method independent of the US sweep position is proposed.

Methods: The proposed technique utilizes the liver surface and vessels, such as the portal and hepatic veins, as landmarks. The algorithm segments the liver region and vessels from both US and CT/MR images using deep learning models. Based on these outputs, the point clouds of the liver surface and vessel centerlines were extracted. The rigid transformation parameters were estimated through point cloud registration using a RANSAC-based algorithm. To enhance speed and robustness, the RANSAC procedure incorporated constraints regarding the possible ranges for each registration parameter based on the relative position and orientation of the probe and body surface.

Results: Registration accuracy was quantitatively evaluated using clinical data from 80 patients, including US images taken from the intercostal, subcostal, and epigastric regions. The registration errors were 7.3 ± 3.2, 9.3 ± 3.7, and 8.4 ± 3.9 mm for the intercostal, subcostal, and epigastric regions, respectively.

Conclusion: The proposed global rigid registration technique fully automated the complex manual registration required for liver fusion imaging and enhanced the workflow efficiency of physicians and sonographers.