Sunday, November 30 | 11:55 a.m.-12:05 p.m. | SSA02-08 | Room S502AB
A new technique that fuses cardiac CT and electroanatomic mapping (EAM) could boost the success rate for ventricular tachycardia (VT) mapping and ablation procedures, a more complex process than ablation for atrial fibrillation and related conditions.Because most recurrent ventricular tachycardia involves a myocardial scar substrate, imaging that assesses cardiac scars and anatomy could help plan and guide VT ablation, Dr. Antonio Esposito, from Vita-Salute San Raffaele University in Milan, told AuntMinnie.com.
The study sought to evaluate the feasibility and usefulness of integrating MDCT data with EAM for VT substrate assessment, VT mapping, and ablation guidance in 20 patients suffering from recurrent VT episodes.
The protocol included coronary CT angiography and a low-kV delayed scan 10 minutes after contrast media administration. The 3D CT model was uploaded and coregistered with high-density bipolar maps using CartoMerge software (Biosense Webster). Next, a point-by-point correlation was performed between low-voltage areas at bipolar EAM and scars on the 3D CT model using homegrown software, according to the abstract.
Correlation between the two models was high except in a few cases that lacked low-voltage regions, the authors reported. Details will be discussed during the presentation.
"Coregistration of the CT-based 3D model of the heart and the EAMs on the Carto system is feasible and may be useful for identification of the VT substrate, potentially improving ablation success," Esposito concluded.
![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&fit=crop&h=100&q=70&w=100)
![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)
