Library of realistic 4D digital beating heart models based on patient CT data.

Background: The prevalence of cardiovascular disease (CVD) has risen alongside new medical imaging technologies designed for its diagnosis and treatment. Computational phantoms play a crucial role in imaging research, supporting applications ranging from basic simulation studies to larger-scale virtual imaging trials (VITs).

Purpose: In this work, we develop a population of detailed, anatomically variable 4D beating heart models for medical imaging research.

Methods: 32 sets of 4D CT data from the PROspective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) national clinical trial served as the basis for the cardiac library. Each dataset was electrocardiogram-gated, containing 10 lower-resolution frames over the cardiac cycle and one high-resolution frame at mid-diastole. The 4D data for each patient was segmented using the AI-based Automatic Segmentation (AS) Cardio tool from Synopsys Simpleware. The segmented high-resolution frame was used to define the initial instance of the heart, each structure defined as a polygon mesh. The multi-channel Large Deformation Diffeomorphic Metric Mapping (MC-LDDMM) image registration algorithm was then used to calculate the frame-to-frame motion of the heart from the low-resolution segmentations. The motion was applied to the cardiac model, creating a time-changing mesh model. Cubic spline curves were fit to the time-changing vertex locations, creating a 4D continuous model from which any number of time points can be generated. An example heart model was imported into a whole-body XCAT computational phantom and imaged with the DukeSim CT simulator for demonstration.

Results: Compared to reference values, the image-based cardiac models mimic the twisting, contracting motion of the heart for anatomically variable subjects. When combined with DukeSim, realistic virtual cardiac imaging data can be produced.

Conclusions: 4D beating heart models were successfully created combining AI-based segmentation and image registration. The library of realistic cardiac models can be a vital tool for 4D cardiac imaging studies.