Assessment of Global Longitudinal and Circumferential Strain Using Computed Tomography Feature Tracking: Intra-Individual Comparison with CMR Feature Tracking and Myocardial Tagging in Patients with Severe Aortic Stenosis.
Emilija MiskinytePaulius BuciusJennifer ErleySeyedeh Mahsa ZamaniRadu TanacliChristian StehningChristopher SchneeweisTomas LapinskasBurkert PieskeVolkmar FalkRolf GebkerGianni PedrizzettiNatalia SolowjowaSebastian KellePublished in: Journal of clinical medicine (2019)
In this study, we used a single commercially available software solution to assess global longitudinal (GLS) and global circumferential strain (GCS) using cardiac computed tomography (CT) and cardiac magnetic resonance (CMR) feature tracking (FT). We compared agreement and reproducibility between these two methods and the reference standard, CMR tagging (TAG). Twenty-seven patients with severe aortic stenosis underwent CMR and cardiac CT examinations. FT analysis was performed using Medis suite version 3.0 (Leiden, The Netherlands) software. Segment (Medviso) software was used for GCS assessment from tagged images. There was a trend towards the underestimation of GLS by CT-FT when compared to CMR-FT (19.4 ± 5.04 vs. 22.40 ± 5.69, respectively; p = 0.065). GCS values between TAG, CT-FT, and CMR-FT were similar (p = 0.233). CMR-FT and CT-FT correlated closely for GLS (r = 0.686, p < 0.001) and GCS (r = 0.707, p < 0.001), while both of these methods correlated moderately with TAG for GCS (r = 0.479, p < 0.001 for CMR-FT vs. TAG; r = 0.548 for CT-FT vs. TAG). Intraobserver and interobserver agreement was excellent in all techniques. Our findings show that, in elderly patients with severe aortic stenosis (AS), the FT algorithm performs equally well in CMR and cardiac CT datasets for the assessment of GLS and GCS, both in terms of reproducibility and agreement with the gold standard, TAG.
Keyphrases
- computed tomography
- aortic stenosis
- dual energy
- left ventricular
- image quality
- contrast enhanced
- transcatheter aortic valve replacement
- ejection fraction
- positron emission tomography
- aortic valve replacement
- magnetic resonance
- transcatheter aortic valve implantation
- aortic valve
- machine learning
- magnetic resonance imaging
- deep learning
- early onset
- coronary artery disease
- convolutional neural network
- data analysis
- single cell
- neural network
- clinical evaluation