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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