Ying Zhang1,2, David Mui3, Julio A Chirinos1, Payman Zamani1, Victor A Ferrari1, Yucheng Chen4, Yuchi Han5. 1. Cardiovascular Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA. 2. PLA General Hospital, Beijing, China. 3. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 4. Department of Cardiology, West China Hospital, Chengdu, China. 5. Cardiovascular Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA. yuchi.han@pennmedicine.upenn.edu.
Abstract
BACKGROUND: Cardiovascular magnetic resonance (CMR) myocardial strain analysis using feature tracking (FT) is an increasingly popular method to assess cardiac function. However, different software packages produce different strain values from the same images and there is little guidance regarding which software package would be the best to use. We explored a framework under which different software packages could be compared and used based on their abilities to differentiate disease from health and differentiate disease severity based on outcome. METHOD: To illustrate this concept, we compared 4-chamber left ventricular (LV) peak longitudinal strain (GLS) analyzed from retrospective electrocardiogram gated cine imaging performed on 1.5 T CMR scanners using three CMR post-processing software packages in their abilities to discriminate a group of 45 patients with heart failure with preserved ejection fraction (HFpEF) from 26 controls without cardiovascular disease and to discriminate disease severity based on outcomes. The three different post-processing software used were SuiteHeart, cvi42, and DRA-Trufistrain. RESULTS: All three software packages were able to distinguish HFpEF patients from controls. 4-chamber peak GLS by SuiteHeart was shown to be a better discriminator of adverse outcomes in HFpEF patients than 4-chamber GLS derived from cvi42 or DRA-Trufistrain. CONCLUSION: We illustrated a framework to compare feature tracking GLS derived from different post-processing software packages. Publicly available imaging data sets with outcomes would be important to validate the growing number of CMR-FT software packages.
BACKGROUND: Cardiovascular magnetic resonance (CMR) myocardial strain analysis using feature tracking (FT) is an increasingly popular method to assess cardiac function. However, different software packages produce different strain values from the same images and there is little guidance regarding which software package would be the best to use. We explored a framework under which different software packages could be compared and used based on their abilities to differentiate disease from health and differentiate disease severity based on outcome. METHOD: To illustrate this concept, we compared 4-chamber left ventricular (LV) peak longitudinal strain (GLS) analyzed from retrospective electrocardiogram gated cine imaging performed on 1.5 T CMR scanners using three CMR post-processing software packages in their abilities to discriminate a group of 45 patients with heart failure with preserved ejection fraction (HFpEF) from 26 controls without cardiovascular disease and to discriminate disease severity based on outcomes. The three different post-processing software used were SuiteHeart, cvi42, and DRA-Trufistrain. RESULTS: All three software packages were able to distinguish HFpEF patients from controls. 4-chamber peak GLS by SuiteHeart was shown to be a better discriminator of adverse outcomes in HFpEF patients than 4-chamber GLS derived from cvi42 or DRA-Trufistrain. CONCLUSION: We illustrated a framework to compare feature tracking GLS derived from different post-processing software packages. Publicly available imaging data sets with outcomes would be important to validate the growing number of CMR-FT software packages.
Authors: Ashish Manohar; Gabrielle M Colvert; Juan E Ortuño; Zhennong Chen; James Yang; Brendan T Colvert; W Patricia Bandettini; Marcus Y Chen; María J Ledesma-Carbayo; Elliot R McVeigh Journal: Med Phys Date: 2022-07-06 Impact factor: 4.506
Authors: Fawaz Alenezi; Taylor A Covington; Monica Mukherjee; Stephen C Mathai; Paul B Yu; Sudarshan Rajagopal Journal: Circ Res Date: 2022-04-28 Impact factor: 23.213