Advanced visualization software developer Ziosoft is highlighting research presented at this month's Society of Cardiovascular Computed Tomography (SCCT) conference on the use of its software to assist with transcatheter aortic valve replacement (TAVR) interventional procedures.
Medical imaging modalities are typically used to perform preprocedure planning and postprocedure assessment of patients with severe aortic stenosis undergoing TAVR. A key metric is calculation of extracellular volume fraction (ECV), for which cardiac MRI (CMR) is considered the gold standard.
However, CT angiography (CTA) can also be used for ECV assessment. In the paper presented at SCCT 2022, researchers from Allina Health Minneapolis Heart Institute at Abbott Northwestern Hospital used Ziosoft's Ziostation2 software to perform EVC-CTA assessment and compared it with ECV-CMR.
A group led by Dr. João Cavalcante found that ECV-CTA conducted in conjunction with Ziostation2 demonstrated "excellent" correlation with ECV-CMR in patients with severe aortic stenosis undergoing TAVR evaluation. The application supported the coregistration of precontrast and delayed acquisitions and created a multisegmented ECV-CT map,
This technology helped enable ECV-CT assessment making it a viable, noninvasive approach to assess myocardial fibrosis and potential prescreening of cardiac amyloidosis during preplanning TAVR or post-TAVR procedures, Cavalcante found.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
