Multi-chamber three-dimensional myocardial strain assessment by computed tomography: a comparison with speckle tracking echocardiography and association with pulmonary hypertension in severe aortic stenosis - Takeaways - MDSpire

Multi-chamber three-dimensional myocardial strain assessment by computed tomography: a comparison with speckle tracking echocardiography and association with pulmonary hypertension in severe aortic stenosis

  • By

  • Vitaliy Androshchuk

  • Edouard Long

  • Charles Sillett

  • Omar Chehab

  • Natalie Montarello

  • Joshua Wilcox

  • Marina Strocchi

  • Benedict McDonaugh

  • Jonathon Leipsic

  • Ronak Rajani

  • Bernard Prendergast

  • Steven Niederer

  • Tiffany Patterson

  • Simon Redwood

  • May 13, 2026

  • 0 min

Share

  • 1

    Three-dimensional myocardial strain assessment using CT provides a geometry-independent evaluation of cardiac function in aortic stenosis patients.

  • 2

    The study found strong agreement between CT and TTE-derived longitudinal strain measurements, particularly for left and right ventricular strains.

  • 3

    Patients with high probability pulmonary hypertension exhibited significantly reduced peak longitudinal strain compared to those with low probability.

  • 4

    TTE-derived LV-GLS was superior to CT-derived LV-GLS for detecting high probability pulmonary hypertension in severe aortic stenosis.

  • 5

    The findings support the use of TAVI CT as a valuable adjunct to TTE for assessing myocardial strain and risk stratification in aortic stenosis.

Original Source(s)

Multi-chamber three-dimensional myocardial strain assessment by computed tomography: a comparison with speckle tracking echocardiography and association with pulmonary hypertension in severe aortic stenosis

  • by Vitaliy Androshchuk, Edouard Long, Charles Sillett, Omar Chehab, Natalie Montarello, Joshua Wilcox, Marina Strocchi, Benedict McDonaugh, Jonathon Leipsic, Ronak Rajani, Bernard Prendergast, Steven Niederer, Tiffany Patterson, Simon Redwood

  • May 13, 2026

Related Content

Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules

Diagnostic accuracy of deep learning vs. human raters for detecting osteoporotic vertebral compression fractures in routine CT scans

Deep learning models for radiography body-part classification and chest radiograph projection/orientation classification: a multi-institutional study