Planning transcatheter pulmonary valve implantation in the dysfunctional native RVOT: A semi-automated pipeline for dynamic analysis based on 4D-CT imaging

•Semi-automated pipeline leveraging optical flow for TPVI planning in native RVOT.•Personalized dynamic 3D analysis of TPVI landing zone based on 4D CT imaging.•Comprehensive assessment of geometrical time-dependent changes of TPVI landing zone.•Minimum landing zone perimeter significantly impacts TPVI feasibility.•Key geometric features of RVOT can enhance the selection process for TPVI recipients. Dysfunction of the right ventricular outflow tract (RVOT) is a common long-term complication following surgical repair in patients with congenital heart disease. Transcatheter pulmonary valve implantation (TPVI) offers a viable alternative to surgical pulmonary valve replacement (SPVR) for treating pulmonary regurgitation but not all RVOT anatomies are suitable for TPVI. To identify a suitable landing zone (LZ) for TPVI, three-dimensional multiphase (4D) computed tomography (CT) is used to evaluate the size, shape, and dynamic behavior of the RVOT throughout the cardiac cycle. However, manually extracting measurements from multiplanar CT reformats is operator-dependent and time-consuming. Leveraging an optical-flow (OF) algorithm, we proposed a novel semi-automated pipeline for dynamic and comprehensive geometrical analysis of the RVOT anatomy. Upon 4D-CT availability, at a pre-defined reference time-point, the patient-specific anatomy is semi-automatically segmented to generate the corresponding three-dimensional surface, which is navigated through a graphical user interface to define the mid-section of the potential LZ. Based on the axial length of the intended device, the proximal and distal LZ cross-sections are automatically identified. An OF-based algorithm is used to track the three LZ cross-sections frame by frame throughout the cardiac cycle, taking RVOT out-of-plane motion into account to update RVOT contours on each cross-section and to elaborate LZ geometrical changes. Finally, LZ time-dependent geometrical features are quantified and extracted. The pipeline was successfully applied to a retrospective cohort of patients, with OF-based tracking reporting excellent agreement (r2 = 0.99) compared to manual processing, with a bias < 1% for both LZ area and perimeter, while also significantly improving time efficiency. CT-derived measurements extracted from LZ mid-section were the most influential covariates affecting the likelihood of TPVI feasibility. Among these, the minimum perimeter outperformed all other geometric LZ parameters in classifying patie