Abstract
Purpose: In recent years, there has been increased clinical interest in the right ventricle (RV) of the heart. RV dysfunction is an important prognostic marker for several cardiac diseases. Accurate modeling of the RV shape is important for estimating the performance. We have created computationally effective models that allow for accurate estimation of the RV shape.
Approach: Previous approaches to cardiac shape modeling, including modeling the RV geometry, has used Doo–Sabin surfaces. Doo–Sabin surfaces allow effective computation and adapt to smooth, organic surfaces. However, they struggle with modeling sharp corners or ridges without many control nodes. We modified the Doo–Sabin surface to allow for sharpness using weighting of vertices and edges instead. This was done in two different ways. For validation, we compared the standard Doo–Sabin versus the sharp Doo–Sabin models in modeling the RV shape of 16 cardiac ultrasound images, against a ground truth manually drawn by a cardiologist. A Kalman filter fitted the models to the ultrasound images, and the difference between the volume of the model and the ground truth was measured.
Results: The two modified Doo–Sabin models both outperformed the standard Doo–Sabin model in modeling the RV. On average, the regular Doo–Sabin had an 8-ml error in volume, whereas the sharp models had 7- and 6-ml error, respectively.
Conclusions: Compared with the standard Doo–Sabin, the modified Doo–Sabin models can adapt to a larger variety of surfaces while still being compact models. They were more accurate on modeling the RV shape and could have uses elsewhere.