Speaker
Description
Accurate 3D cardiac model construction relies on high-quality segmentation and registration of 2D+t cardiac cine MRI (cMRI) data, with patient-specific ventricular models serving as a key building block for cardiac digital twins. 2D+t cMRI offers higher resolution compared to 3D acquisitions, but the 2D slices are prone to spatial misalignment caused by patient movement and inconsistent breath holds and heart beats during acquisition. Left uncorrected, such misalignments propagate into the 3D modeling stage, degrading anatomical accuracy. We present a semi-automated, modular pipeline for segmenting and registering 2D+t cMRIs and constructing personalized 3D anatomical models of the left and right ventricles and left ventricular myocardium (LV, RV, MYO). Data from the 'ILearnHeart' project consist of 2D+t cMRI scans in 2-chamber (2CH), 4-chamber (4CH), and stacked short-axis (SAX) views from seven healthy subjects, covering a full cardiac cycle. We segment the LV and RV blood pools and LV myocardium using nnU-Net and a Gaussian process-based few-shot approach. Registration proceeds in two steps: intensity-based alignment of 2CH and 4CH slices via normalized cross-correlation (NCC), followed by contours-based alignment of the SAX stack through intersection distance minimization. Intensity-based registration yields an average NCC improvement of (17.5 ± 16.7)% over unregistered slices. The registered contours serve as input for 3D surface reconstruction via NURBS lofting, yielding smooth, patient-specific ventricular surface models of the LV, RV, and MYO. Future work targets closed volumetric mesh generation and 4D extension across the cardiac cycle.