Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.

Aya Ghoul¹, Paxstan Cassal Paulson², Andreas Lingg¹, Patrick Krumm¹, Kerstin Hammernik³, Daniel Rueckert^3,4,5, Sergios Gatidis^1,6, Thomas Küstner¹

¹Medical Image and Data Analysis (MIDAS.lab), Department of Interventional and Diagnostic Radiology, University Hospital of Tuebingen, Tuebingen, Germany.

²Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.

³School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany.

⁴TUM University Hospital, Technical University of Munich (TUM), Munich, Germany.

⁵Department of Computing, Imperial College London, London, UK.

⁶Department of Radiology, Stanford University, Stanford, California, USA.

PubMed PMC DOI PDF

Abstract

PURPOSE: This study aims to develop an automated framework for operator-independent assessment of cardiac ventricular function from highly accelerated images. METHODS: We introduce a deep learning framework that generates reliable ventricular volumetric parameters and strain measures from fully sampled and retrospectively accelerated MR images. This method integrates image registration, motion-co…

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