KEYWORDS: Image registration, Medical imaging, 3D modeling, 3D image processing, Image quality, Heart, 3D acquisition, Image segmentation, Data modeling, X-rays
Purpose: Our goal is to propose a landmark- and contour-matching (LCM) registration method that uses both landmark information and approximate point correspondences to boost the similarity between image pairs with sparse landmark information.
Approach: A model for registering two-dimensional (2D) medical images with landmark information and contour-approximating landmarks was proposed. The model was also extended to accommodate the registration of three-dimensional (3D) cardiac images. We validated the LCM method on 2D hand x-rays and 3D porcine cardiac magnetic resonance images. The following metrics were used to assess the quality of specific aspects of the registered images: Dice similarity coefficient for the overall image overlap, target registration error for pointwise correspondence, and interior angle for local curvature.
Results: Target registrations were reduced from 27.12 to 0.01 mm post-LCM registration. Implementing the proposed algorithm also led to a 112% average improvement in image similarity in terms of Dice coefficients. In addition, interior angle measurements indicate that the proposed method preserved the local curvature at major reference landmarks and mitigated the appearance of deformities in the registered images.
Conclusions: The proposed method addressed several issues associated with purely landmark-based techniques, such as iterative closest point registration and thin plate spline interpolation. Furthermore, it provided accurate registration results even in the presence of landmark localization errors.
Pharmacokinetic modeling is a mathematical modeling technique that examines the dynamics of concentration curves to reveal information about tissue microvasculature. Typically, image registration is performed as a pre- processing step to remove motion in dynamic contrast-enhanced (DCE) image sequences and ensure accurate pharmacokinetic analysis. In this work, we introduce a registration method for correcting motion in a sequence of DCE images. The proposed method involves the use of Tofts pharmacokinetic model to generate a sequence of reference images. These images simplify the challenging task of registering DCE images by pairing each frame in the motion- corrupted sequence with a reference image that resembles the overall contrast enhancement of the template. Abdominal DCE-MR images were used for validation. Reduction of motion in the registered sequence was observed both visually and quantitatively. Both global and local measures of registration accuracy obtained from the registered sequence and its associated signal intensity curves were smaller than their pre-registration counterparts.
The local arrangement of cardiac fibers provides insight into the electrical and mechanical functions of the heart. Fiber directions can be obtained using diffusion tensor (DT) MR imaging and further integrated into computational heart models for accurate predictions of activation times and contraction. However, this information is not available due to limitations of cardiac in-vivo DTI; thus, an average atlas could be used instead of individual fiber directions. In this work, we present a simple and computationally efficient pipeline for constructing a novel statistical cardiac atlas from ex-vivo high resolution DT images of porcine hearts. Our framework involves normalizing the cardiac geometries, reorienting local directional information on diffusion, and computing the average diffusion tensor field. The registration step eliminates the need for landmarks, while the tensor reorientation strategy enables the transformation of the diffusion tensors and preserves the diffusion tensor orientations.
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