3D Reconstruction with Coronary Artery Based on Curve Descriptor and Projection Geometry-Constrained Vasculature Matching

This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed.

Projective Gravity

In [1] and [3] it was pointed out that octonians can replace an infinite dimensional Hilbert space and psi-waves descriptions concerning the states of deuteron which are finite in number. It is then clear that gravity needs projective and projection geometry to be described in a unified way with the three other basic forces of physics.

The concept of smooth generic camera calibration for optical metrology

Modern digital cameras with sophisticated optics enable accurate shape measurements via a plethora of metrological techniques. In order to take advantage of the data delivered by high-resolution cameras, one needs a sufficiently flexible model to describe the imaging geometry and an adequate calibration procedure to determine the model parameters and estimate their uncertainties. In this report, we suggest the concept of a smooth generic camera model that has several advantages compared to the state-of-the-art: the description is resolution-independent and takes advantage of the natural smoothness of the projection geometry; the global and the local symmetries inherent to the view ray-based parametrization are regularized in an explicitly unbiased manner. The calibration procedure uses dense datasets that can be obtained with the technique of active screens and consistently accounts for the uncertainty in the registration data that can be extracted during pre-processing. The concept is illustrated with synthetic examples based on physically-accurate rendering.

An accurate projection model for diffraction image formation and inversion using a polychromatic cone beam

Recently, the concept of X-ray diffraction contrast tomography (DCT) has been extended to the case of more widely available laboratory source CT systems. Using well known concepts from geometrical ray optics, an exact formulation is derived for the forward and backward projection geometry encountered under polychromatic cone beam illumination, and it is shown how this projection model can be efficiently implemented in practice. The new projection model is subsequently used for iterative tomographic reconstruction of the three-dimensional shape of a grain from a set of experimentally observed cone beam projections and shows a clear improvement compared to the simplified projection model used previously.