Multimodality registration of two-dimensional echocardiography and cardiac CT for mitral valve diagnosis and surgical planning

Abstract

A registration method to fuse two-dimensional (2-D) echocardiography images with cardiac computed tomography (CT) volume is presented. The method consists of two major procedures: temporal and spatial registrations. In temporal registration, the echocardiography frames at similar cardiac phases as the CT volume were interpolated based on electrocardiogram signal information, and the noise of the echocardiography image was reduced using the speckle reducing anisotropic diffusion technique. For spatial registration, an intensity-based normalized mutual information method was applied with a pattern search optimization algorithm to produce an interpolated cardiac CT image. The proposed registration framework does not require optical tracking information. Dice coefficient and Hausdorff distance for the left atrium assessments were [Formula: see text] and [Formula: see text], respectively; for left ventricle, they were [Formula: see text] and [Formula: see text], respectively. There was no significant difference in the mitral valve annulus diameter measurement between the manually and automatically registered CT images. The transformation parameters showed small deviations ([Formula: see text] deviation in translation and [Formula: see text] for rotation) between manual and automatic registrations. The proposed method aids the physician in diagnosing mitral valve disease as well as provides surgical guidance during the treatment procedure.

An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment

“…As both images were acquired at different times and have distinct image properties, image registration can be applied to temporally and spatially align both images, thus providing better information during surgical guidance. Such integration has been applied using 2D echocardiography-CT [ 87 , 90 , 91 , 93 , 143 , 144 ], 3D echocardiography-CT [ 91 ], and 2D echocardiography-MRI [ 145 ].…”

“…Selection of spatial transformation algorithms also depends on the image dimensionality, whether involving the registration from 2D to 2D [ 10 , 11 , 136 , 171 , 183 ], 3D to 3D [ 22 , 49 , 74 , 78 , 139 , 162 , 179 , 188 , 189 ], or 2D to 3D [ 79 , 92 , 93 , 143 , 190 , 191 ]. For 2D to 2D registration, where the acquisition tightly controls the geometry of the images, the images can simply be registered via a rotation and two orthogonal translations.…”

“…In intensity-based interpolation, the final result from the interpolation is directly computed from the intensity values of input images. Examples of intensity-based interpolation include linear [ 92 , 93 , 162 , 192 ] and cubic B-spline [ 27 , 49 , 80 , 181 – 184 ] interpolation methods. Given its computational simplicity, intensity-based interpolation has been widely used in cardiac image registration research.…”

“…Registration based on mutual information or normalized mutual information [ 92 , 93 , 109 , 131 , 136 , 139 , 189 , 198 – 204 ] uses a similarity metric in the registration process. This similarity measure is arguably an excellent similarity metric and has been shown to be robust and well suited for multimodal image registration.…”

“…It is based on the principles of natural biological evolution, which operates on a population of potential solutions applying the principle of survival of the fittest to produce better approximations to a solution. One of the examples of genetic optimization algorithms is the generalized pattern search (GPS) [ 92 , 93 ]. The GPS algorithm does not involve the gradient of the objective function to be optimized; thus, it is specifically suited for function that is not differentiable, such as mutual information in its usual form.…”