CT-based visualization of aortic valve morphology: from 3D energy-integrating CT to 4D photon counting CT

Background: While 4D contrast-enhanced computed tomography (CT) is used to plan cardiovascular interventions such as transcatheter valve replacement, it is not yet routinely used to characterize minimally calcified aortic valves for planning of surgical valve repair. It is widely recognized that aortic valve morphology has implications for the durability of valve repair surgery. Purpose: The objective is to demonstrate the potential of CT image segmentation for elucidating aortic valve morphology prior to surgery and to illustrate a potential benefit of 4D CT and photon counting CT (PCCT) for patient-specific modeling of dysmorphic aortic valves. Materials and methods: This observational series includes nine patients who were suspected to have minimally calcified bicuspid aortic valve morphology on transthoracic echocardiography (TTE). Mean age was 53 +/- 13 years and seven patients were male. For the seven patients who underwent aortic root surgery, CT-based segmentation of the aortic valve was compared to echocardiographic interpretation and direct intraoperative visualization of valve morphology. Two patients who have not yet undergone aortic surgery were imaged longitudinally with 4D energy-integrating detector CT (EID-CT) and 4D PCCT, and the morphological interpretation of the aortic valve was compared to previous TTE reports. Results: In most surgical cases, CT-based segmentation and direct visualization of the valve revealed morphological features not previously confirmed on TTE, particularly related to the cusp fusion pattern. Moreover, 4D CT enabled morphological assessment at both systole and diastole, which captured maximal cusp separation and valve closure. PCCT images were reconstructed with slice thickness as low as 0.2 mm, and revealed detailed dysmorphic features such as a small accessory cusp with fistula and a double raphe in separate patients. Conclusion: 4D CT-based segmentation has the potential to dynamically capture aortic valve features that are relevant to risk stratification and surgical planning at high spatial resolution.