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Blind multirigid retrospective motion correction of MR images
Author(s) -
Loktyushin Alexander,
Nickisch Hannes,
Pohmann Rolf,
Schölkopf Bernhard
Publication year - 2015
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25266
Subject(s) - computer science , computer vision , artificial intelligence , motion (physics) , motion estimation , computation , image quality , cuda , image (mathematics) , algorithm , operating system
Purpose Physiological nonrigid motion is inevitable when imaging, e.g., abdominal viscera, and can lead to serious deterioration of the image quality. Prospective techniques for motion correction can handle only special types of nonrigid motion, as they only allow global correction. Retrospective methods developed so far need guidance from navigator sequences or external sensors. We propose a fully retrospective nonrigid motion correction scheme that only needs raw data as an input. Methods Our method is based on a forward model that describes the effects of nonrigid motion by partitioning the image into patches with locally rigid motion. Using this forward model, we construct an objective function that we can optimize with respect to both unknown motion parameters per patch and the underlying sharp image. Results We evaluate our method on both synthetic and real data in 2D and 3D. In vivo data was acquired using standard imaging sequences. The correction algorithm significantly improves the image quality. Our compute unified device architecture (CUDA)‐enabled graphic processing unit implementation ensures feasible computation times. Conclusion The presented technique is the first computationally feasible retrospective method that uses the raw data of standard imaging sequences, and allows to correct for nonrigid motion without guidance from external motion sensors. Magn Reson Med 73:1457–1468, 2015. © 2014 Wiley Periodicals, Inc.