Image-Space Tensor Field Visualization Using a LIC-like Method

Abstract

Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. Physics-based visualization of tensor fields has proven to show the main features of symmetric second-order tensor fields, while still displaying the most important information of the data, namely the main directions in medical diffusion tensor data using texture and additional attributes using color-coding, in a continuous representation. Nevertheless, its application and usability remains limited due to its computational expensive and sensitive nature.We introduce a novel approach to compute a fabric-like texture pattern from tensor fields motivated by image-space line integral convolution (LIC). Although, our approach can be applied to arbitrary, non-selfintersecting surfaces, we are focusing on special surfaces following neural fibers in the brain. We employ a multi-pass rendering approach whose main focus lies on regaining three-dimensionality of the data under user interaction as well as being able to have a seamless transition between local and global structures including a proper visualization of degenerated points.

A Vector Field Texture Generation Method without Convolution Calculation

“…In future work, we will continue to improve the RIS algorithm so that it can be applied in more scenarios, including alterable velocity texture and textures in three dimensions. We will also study the application of textures in tensor field visualization [37].…”

Diffusion MRI visualization

“…LIC has been applied in cardiac DTI along the two principal eigenvector directions to visualize the sheet structure of the myocardium 79 and in brain DTI either along the major eigenvector field 80 or, in a multi-pass fashion, along all eigenvector fields. 75 More recently, it has also been used to texture surfaces 81 and been extended to multiple fiber directions. 82 Track-density imaging (TDI) 83,84 is based on tracing a huge number of fiber tracts (e.g.…”