Journal Article

DZNE-2026-01056

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png MotionDPS: Motion-Compensated 3-D Brain MRI Reconstruction.

Ortiz-Gonzalez, A. ; Kobler, E. ; Schletter, L.DZNE* ; Effland, A.Extern*

2026
Institute of Electrical and Electronics Engineers, New York, NY

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Please use a persistent id in citations: doi:10.1109/TMI.2026.3705847

Abstract: Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective motion compensation remains challenging, particularly in accelerated acquisitions, due to the ill-posed nature of the joint reconstruction and motion estimation problem. In this work, we propose a unified Bayesian framework for motion-compensated 3-D MRI that jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data. Our approach integrates pretrained 3-D complex-valued score-based diffusion models as expressive anatomical image priors within a physics-based forward model. Inference is performed by alternating diffusion posterior image updates with efficient proximal optimization steps for motion and coil sensitivity estimation, enabling fully unsupervised reconstruction without the need for paired motion-free training data. Experiments on simulated and real-motion brain MRI datasets demonstrate that the proposed method achieves improved image quality and motion robustness compared to state-of-the-art classical and learning-based motion correction techniques, particularly in the presence of severe motion and high acceleration.

Keyword(s): Humans (MeSH) ; Brain: diagnostic imaging (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Imaging, Three-Dimensional: methods (MeSH) ; Algorithms (MeSH) ; Artifacts (MeSH) ; Motion (MeSH) ; Bayes Theorem (MeSH) ; Movement: physiology (MeSH)

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Contributing Institute(s):

1. Clinical Neuroimaging (AG Radbruch)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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