Attention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging.

Siying Xu¹, Kerstin Hammernik², Andreas Lingg³, Jens Kübler³, 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: siying.xu@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

Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accelerate imaging and enhance reconstruction quality. Existing networks exhibit some common limitations that constrain further acceleration possibilities,…

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