We present a deep learning (DL) registration framework for fast mono-modal and multi-
modal image registration using differentiable mutual information and diffeomorphic B-spline …

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR)
imaging enables the assessment of regional myocardial function and is important for …

Using cine magnetic resonance imaging (cine MRI) images to track cardiac motion helps
users to analyze the myocardial strain, and is of great importance in clinical applications. At …

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for
the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current …

Learning spatial-temporal correspondences in cardiac motion from images is important for
understanding the underlying dynamics of cardiac anatomical structures. Many methods …

Myocardial motion and deformation are rich descriptors that characterize cardiac function.
Image registration, as the most commonly used technique for myocardial motion tracking, is …

Purpose To assess the feasibility of a newly developed algorithm, called deep learning
synthetic strain (DLSS), to infer myocardial velocity from cine steady-state free precession …

Abstract 3D motion estimation from cine cardiac magnetic resonance (CMR) images is
important for the assessment of cardiac function and diagnosis of cardiovascular diseases …

In order to tackle the difficulty associated with the ill-posed nature of the image registration
problem, regularization is often used to constrain the solution space. For most learning …

Image registration is fundamental in medical imaging applications, such as disease
progression analysis or radiation therapy planning. The primary objective of image …