Learning efficient non-rigid registration in k-space for accelerated Magnetic Resonance Imaging.

Aya Ghoul¹, Kerstin Hammernik², Andreas Lingg³, Patrick Krumm³, Daniel Rueckert⁴, Sergios Gatidis⁵, Thomas Küstner⁶

¹Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany. Electronic address: aya.ghoul@med.uni-tuebingen.de.

²School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.

³Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.

⁴School of Computation, Information and Technology, Technical University of Munich, Munich, Germany; Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom.

⁵Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany; Department of Radiology, Stanford University, Stanford, CA, USA.

⁶Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany.

PubMed DOI

Abstract

Non-rigid motion estimation at high temporal resolution is essential for dynamic and real-time Magnetic Resonance Imaging (MRI), yet remains challenging due to the spatio-temporal sampling limitations of accelerated acquisitions. Conventional motion estimation pipelines typically reconstruct images before performing registration. However, the high undersampling required to achieve high temporal re…

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