- Walking poster presentation
- Open Access
Realtime cardiac function using compressed sensing: initial clinical evaluation
© Paul et al. 2016
- Published: 27 January 2016
- Demand Evaluation
- Short Axis Slice
- Show Correlation
- Initial Clinical Evaluation
- Functional Heart
In current clinical routine, functional heart parameters are derived from breathhold CINE MRI. To enable free-breathing acquisitions and imaging e.g. in highly arrhythmic patients, realtime imaging is gaining interest. The increasing clinical availability of this new technique demands evaluation of functional parameters compared to the gold standard .
Study population and acquisitions
14 patients with different cardiac pathologies were randomly selected. All patients underwent our clinical routine imaging protocol and additionally real-time (RT) imaging in three short axis slices (apical, medial, basal). RT imaging was performed with a radial Tiny Golden Angle (Ψ7≍23.628°)  balanced acquisitions (α=60°) at 1.5T (Philips Achieva 1.5T).
Reconstruction from the retrospective gated CINE data (REF) were compared to compressed sensing realtime reconstruction via TyGRASP  (CS). Reconstructions were performed with a spatial resolution of 1.4 × 1.4 × 8 mm3 and temporal resolution of about 60 ms (CS) and 30 ms (REF).
ESV, EDV, SV, and EF were determined for LV and RV in the different reconstructions from the slices CS and matching slices of REF. Functional parameters were compared via correlation (Pearson, p-values below 5% were considered significant) and Bland-Altman analysis. Further analysis was performed by pairwise t-tests (α=5%, with Bonferroni correction for multiple testing).
The findings are in line with , where no significant bias was found for EF from realtime GRAPPA vs CINE in LV. The results suggest that the realtime technique is a promising for evaluation of cardiac function parameters. Further investigation is needed with more patients, full slice coverage and to evaluate the realtime technique during free-breathing. Additionally, sliding window realtime reconstruction without additional compressed sensing constraints might be investigated.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.