3D Reconstruction of Tubular Structures Using MRI Projection Images

After imaging information became available in digital form, techniques for acquiring volumetric data evolved. 3D reconstruction is mostly performed using multislice stack images. The objective of this dissertation is to introduce a simple magnetic resonance technique for imaging tubular structures, such as blood vessels and catheters, and 3D reconstruction of these structures. This study includes three major chapters: one on simulation and two on experiments with MRI projection images. First, a MATLAB simulation was created to analyze the reconstruction process; it was tested with different shapes of the structures and different numbers of projections. Second, triplanar projection imaging was evaluated on a phantom filled with a T1-shortening, Gd-based contrast agent embedded into a lipid matrix. The object is reconstructed from three mutually orthogonal projections of the volume that contain the structure of interest. The projected structures of the object were segmented out on each projection, back-projected to generate the segmented tubular object, and mesh-rendered in 3D. The accuracy of this approach was investigated by comparing the mesh-rendered tubular structure generated from projections with the mesh rendered from a multislice set of images of the same volume. Third, Inverse Radon Transform was implemented for 3D reconstruction of complex helical tubular structure from multiple radially deployed (oblique) projections. To compute the correctness of the 3D reconstruction processes, we compared the resulting meshes with the multislice-rendered meshes. Hausdorff distance and Point Cloud Comparison methods were used to evaluate the reconstruction error. The average error was less than 1 pixel for the triplanar projection images, and it was less than 2 pixels for the oblique orientation projection images. With further optimization and reduction of acquisition time, this method can be used for 3D fast imaging of interventional tools or segments of blood vessels with applications in interventional MRI.