A review of cardiac image registration methods

Abstract

In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

Image-based large-eddy simulation in a realistic left heart

“…It is a classical image processing technique, which consists in finding a transformation to map two images called to the template image and the reference image. It is commonly applied to cardiac images [56], notably to obtain clinical information about the myocardial contractile function. Here, a classical image registration problem [57] is solved to reconstruct the movement of the frontiers of the computational domain.…”

Development of a geometry‐based respiratory motion– simulating patient model for radiation treatment dosimetry

“…Image registration is generally based on either matching geometric image features or voxel similarity measures. ( 53 , 54 ) Currently, studies about 4D image registration are based mainly on voxel similarity. ( 55 – 61 ) However, geometric image features may be more useful for deriving information such as DVH curves, because the organ contours are available from the treatment planning system.…”

Cardiac Image Registration

“…The latter method has been used in cardiac image registration. 20,21 For rigid structures such as the brain, three non collinear fiducial points may be sufficient to establish the transformation between the 3D image volumes. However, the larger the number of points used, the more any error occurring during collection of the points is averaged out.…”

“…It divides almost immediately, within 5 to 10 mm, into superior images from two different image coordinate spaces, such that multiple anatomic features or points of interest are aligned. 1,19,20 These points, called the fiducial points, are marked on the two image spaces. During the actual registration process, the image to be registered undergoes a mathematical transformation in all of its degrees of freedom, such that its image coordinate space is mapped onto that of the other image.…”