This paper describes a quantitative assessment of respiratory motion of the heart and the construction of a model of respiratory motion. Three-dimensional magnetic resonance scans were acquired on eight normal volunteers and ten patients. The volunteers were imaged at multiple positions in the breathing cycle between full exhalation and full inhalation while holding their breath. The exhalation volume was segmented and used as a template to which the other volumes were registered using an intensity-based rigid registration algorithm followed by nonrigid registration. The patients were imaged at inhale and exhale only. The registration results were validated by visual assessment and consistency measurements indicating subvoxel registration accuracy. For all subjects, we assessed the nonrigid motion of the heart at the right coronary artery, right atrium, and left ventricle. We show that the rigid-body motion of the heart is primarily in the craniocaudal direction with smaller displacements in the right-left and anterior-posterior directions; this is in agreement with previous studies. Deformation was greatest for the free wall of the right atrium and the left ventricle; typical deformations were 3-4 mm with deformations of up to 7 mm observed in some subjects. Using the registration results, landmarks on the template surface were mapped to their correct positions through the breathing cycle. Principal component analysis produced a statistical model of the motion and deformation of the heart. We discuss how this model could be used to assist motion correction.

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There is considerable interest in performing free-breathing acquisitions of the heart in order to obtain high-quality images without the need for multiple, long breathholds. In this article a 3D motion-correction method is described that is based on image registration of in-plane data and through-plane slice tracking. A number of fast radial undersampled images are acquired, each of which is free of motion artifacts. Initially, in-plane translational and rotational motion between each image was corrected before combining the data to give a fully sampled image. At the next stage, correction of in-plane deformation, in addition to translations and rotations, was performed in the image domain. Through-plane translational motion was compensated using a navigator echo to move the acquisition plane. Using this method, information on the motion of the heart was captured at the same time as acquiring the image data. No motion model, assumptions about the motion, or training data are required. The method is demonstrated on phantom data and cardiac images acquired on free-breathing volunteers.

Abstract. We describe a dynamic atlas that can be customized to an individual study subject in near-real-time. The atlas comprises 180 brain volumes each of which has been automatically segmented into grey matter, white matter and CSF, and also non-rigidly registered to the Montreal BrainWeb reference dataset providing automatic delineation of brain structures of interest. To create a dynamic atlas, the user loads a study dataset (eg: a patient) and queries the atlas database to identify similar subjects. All selected database subjects are then aligned with the study subject using affine registration, and average tissue probability maps and structure delineations produced. The system can run on distributed data and distributed CPUs illustrating the potential of computational grids in medical image analysis.

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