An efficient and fast GPU-based algorithm for visualizing large volume of 4D data from virtual heart simulations. (May 2017)

• Record Type: Journal Article
• Authors: Yu, Shui; Zhang, Shanzhuo; Wang, Kuanquan; Xia, Yong; Zhang, Henggui
• Is Part Of: Biomedical signal processing and control. Volume 35(2017)
• Page Start: 8
• Page End: 18
• Publication Date: 2017-05
• Subjects: Time-varying visualization -- Vector quantization -- Ray casting -- Cardiac electrophysiology -- Virtual heart; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.01.015
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A 4D visualization method of large virtual heart simulation data is proposed. A N-nearest neighbor searching strategy for compressing original data is proposed. An adaptive sampling method for rendering compressed data is presented. The method obtains high image quality and real time rendering speed simultaneously. Abstract: An efficient and fast visualization algorithm is important for analyzing a large volume of physical data conformed to 3D anatomical geometry that evolves with time (i.e., 4D data). Such 4D visualization helps to study the evolution of cardiac excitation waves in normal and pathological conditions and understand the mechanisms underlying the genesis and maintenance of cardiac arrhythmias. However, due to limited hardware resources, so far we have not found any report about real time methods to visualize a large volume of 4D data of virtual heart simulation data. In this study, we propose a GPU-based method to address this issue, our method consists of two phases, and the first is the data compression phase, implementing an improved hierarchical vector quantization method with N-nearest neighbor searching strategy in GPU, which reduces compression time dramatically. In the second phase, the compressed data is directly decompressed in GPU and rendered with ray casting method. What is more, an adaptive sampling strategy and empty space skipping methods are further used to accelerate the rendering process, resulting in a high rendering speed. The proposed method has been evaluated for the visualization of large time-varying cardiac electrophysiological simulation data by using our simulation datasets and has achieved promising results. For about 27G bytes dataset, our method can render the data with above 35 frames per second (FPS), which exceeds the real-time frame rate for interactive observing. It significantly decreases the time in the compression phase and achieves real time rendering speed with high image quality in the visualization phase, which demonstrates the accuracy and efficiency of our method.