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Abstract #3906

Radial and Circumferential Strain using feature tracking from Cine SSFP Imaging with Compressed Sensing at Rest and with MRI Exercise Ergometry

Christian Hamilton-Craig 1,2 , Wendy Strugnell 1 , Qurain Alshammari 2,3 , Mark Chapman 1 , Norman Morris 4 , Helen Seale 5 , Fiona Kermeen 5 , Benjamin Schmitt 6 , Michael Zenge 7 , Jonathan Chan 8 , and Andre La Gerche 9

1 Richard Slaughter Centre of Excellence in CVMRI, The Prince Charles Hospital, Brisbane, Queensland, Australia, 2 Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia, 3 College of Applied Medical Science, Hail, Saudi Arabia, 4 Griffith Health Institute, Griffith University, Queensland, Australia, 5 Heart Lung Institute, The Prince Charles Hospital, Queensland, Australia, 6 Imaging & Therapy Systems Division, Siemens Ltd. Australia, NSW, Australia, 7 Imaging & Therapy Division, Siemens AG, Erlangen, Germany, 8 Heart Research Centre, Griffith University, Queensland, Australia, 9 St Vincent's Hospital and University of Melbourne, Victoria, Australia

MRI strain using grid tagging is time consuming to analyze, and is not widely applied for clinical deformation imaging. Recently, MRI strain imaging using post-processing feature tracking on standard SSFP images is a novel technique without need for additional tagging acquisitions. Two elite athletes were imaged at rest and during exercise on a 1.5T system using an MRI pedal ergometer with ultra-fast ECG-triggered cine SSFP imaging and iterative compressed sense reconstruction. Rest and exercise (100 Watts) datasets were analysed with feature tracking for circumferential and radial strain. Myocardial deformation is feasible using feature tracking on ultra-fast cine SSFP image sets.

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