Anatomic surface reconstruction from sampled point cloud data and prior models.

ABSTRACT

In this paper, we propose an approach for reconstruction of an anatomic surface model from point cloud data using the Screened Poisson Surface Reconstruction algorithm, which requires a collection of points and their normal vectors. Various algorithms exist for estimating normal vectors for point cloud data; however, in this work we describe a novel approach to estimating the normal vectors from a high-resolution prior model. In many medical applications, a preoperative high-resolution scan is acquired for diagnostic and planning purposes, whereas intraoperative, lower fidelity imaging is utilized during the procedure. This approach assumes an already existing registration between intra-operatively acquired data and the preoperative model. We conducted simulation experiments to evaluate the effect of registration error, point sampling rate, and noise levels on the acquired point cloud data samples. In addition, we evaluated the effect of using both the closest point, as well as a neighborhood of closest points on the prior model for estimating the normal. Our results showed that surface reconstruction error increases with higher registration error; however, acceptable performance was achieved with clinically-acceptable registration error. In addition, the best reconstruction was obtained when estimating the normal using only the closest point on the prior model, as opposed to utilizing a neighborhood of points. When combining the effect of all factors (Gaussian sampling noise of zero mean and σ=1.8mm; Gaussian translational error of zero mean and σ=2.0mm; and Gaussian rotational error of zero mean and σ=3°) the overall RMS reconstruction error was 0.88±0.03mm.