Rapid registration of multimodal images using a reduced number of voxels

Abstract

Rapid registration of multimodal cardiac images can improve image-guided cardiac surgeries and cardiac disease diagnosis. While mutual information (MI) is arguably the most suitable registration technique, this method is too slow to converge for real time cardiac image registration; moreover, correct registration may not coincide with a global or even local maximum of MI. These limitations become quite evident when registering three-dimensional (3D) ultrasound (US) images and dynamic 3D magnetic resonance (MR) images of the beating heart. To overcome these issues, we present a registration method that uses a reduced number of voxels, while retaining adequate registration accuracy. Prior to registration we preprocess the images such that only the most representative anatomical features are depicted. By selecting samples from preprocessed images, our method dramatically speeds up the registration process, as well as ensuring correct registration. We validated this registration method for registering dynamic US and MR images of the beating heart of a volunteer. Experimental results on <i>in vivo</i> cardiac images demonstrate significant improvements in registration speed without compromising registration accuracy. A second validation study was performed registering US and computed tomography (CT) images of a rib cage phantom. Two similarity metrics, MI and normalized crosscorrelation (NCC) were used to register the image sets. Experimental results on the rib cage phantom indicate that our method can achieve adequate registration accuracy within 10% of the computation time of conventional registration methods. We believe this method has the potential to facilitate intra-operative image fusion for minimally invasive cardio-thoracic surgical navigation.