Image-Space Gridding for Nonrigid Motion-Corrected MR Image Reconstruction
Kwang Eun Jang, Mario O. Malav\'e, and Dwight G. Nishimura

TL;DR
This paper introduces a novel model-based method for nonrigid motion correction in free-breathing cardiac MR imaging, leveraging image-space gridding and advanced navigators to improve image sharpness and quality.
Contribution
It develops a new nonrigid motion correction framework using image-space gridding and self-navigators, enabling more accurate correction of complex respiratory motion in MR images.
Findings
Enhanced coronary artery sharpness in preliminary results
Improved image quality over translational correction methods
Effective estimation of nonrigid respiratory motion
Abstract
Motion remains a major challenge in magnetic resonance (MR) imaging, particularly in free-breathing cardiac MR imaging, where data are acquired over multiple heartbeats at varying respiratory phases. We adopt a model-based approach for nonrigid motion correction, addressing two challenges: (a) motion representation and (b) motion estimation. For motion representation, we derive image-space gridding by adapting the nonuniform fast Fourier transform (NUFFT) to represent and compute nonrigid motion, which provides an exact forward-adjoint pair of linear operators. We then introduce nonrigid SENSE operators that incorporate nonrigid motion into the multi-coil MR acquisition model. For motion estimation, we employ both low-resolution 3D image-based navigators (iNAVs) and high-resolution 3D self-navigating image-based navigators (self-iNAVs). During each heartbeat, data are acquired along two…
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Taxonomy
MethodsADaptive gradient method with the OPTimal convergence rate
