Konstantin Shkurko

Konstantin Shkurko

Ph.D Student
School of Computing
University of Utah

Advisor: Erik Brunvand

School of Computing
50 S Central Campus Dr, RM 3190
Salt Lake City, UT 84112

kshkurko AT cs DOT utah DOT edu
kis9 AT cornell DOT edu

Real-Time Visualization Picture

Real-Time Volume Rendering of 3D Medical Scan Data

Konstantin Shkurko

M.S. Thesis - Cornell University, 2010

Abstract: The immense progress of imaging technologies has radically changed the practice of medicine both in terms of diagnosis and intravascular surgery. Using technologies such as magnetic resonance imaging (MRI), and computed axial tomography (CAT) scans, doctors are now able to "see" internal organs and structures in high-resolution detail. Today using expensive specialized hardware, one can generate three-dimensional visualizations providing accurate interpretations and revolutionizing the medical field.

This thesis presents a substantially different method to visualize volume datasets by treating them as a scattering volume and rendering the images on a small cluster of parallel computers. With sufficient computing power, the data can be explored interactively without any loss of information.

We utilize a basic raycasting algorithm with several acceleration techniques, such as global empty space skipping, early ray termination, a global gradient cache and increased data access coherency. By selecting efficient data subdivisions, we eliminate the memory and bus-bandwidth latencies and maximize the computing power of each core. The cache coherence of the data access due to the bricking scheme produced almost real-time rendering speeds that are independent of the viewing direction. We tested these algorithms on three different datasets at varying output image resolutions.

In the near future, with increased computing power and sufficient bandwidth, it will be possible to use a cluster of machines to render time-dependent datasets in real time and to deliver these images directly into an operating room.

Files:     document (pdf, 20.0 MB)     BibTex (bib, 250 B)     presentation (pdf, 3.71 MB)

Media: Videos were created to illustrate the speed of the entire system. We tested three datasets (930 and 770 slices at the resolution of 5122). Output images at the resolution of 10242 were generated at the rate above 10 Hz, with the actual refresh rate shown in the top right corner of the videos. We used 256 cores in parallel, each running at 2.6 GHz, to compute each frame.

The left video is at half resolution (5122) (H.264, mp4, 14.5 MB) and the one on the right is at full resolution (10242) (H.264, mp4, 39.0 MB).

MS Thesis movie, Small H.264 MS Thesis movie, Large H.264

Images: Pre-Operation Dataset

Pre-Op Dataset Angled Side View Pre-Op Dataset Bottom View Pre-Op Dataset Front View Pre-Op Dataset Side View

Post-Operation Dataset

Post-Op Dataset Angled Side View Post-Op Dataset Bottom View Post-Op Dataset Front View Post-Op Dataset Side View

CT 14 Dataset

CT 14 Dataset Angled Side View CT 14 Dataset Bottom View CT 14 Dataset Front View CT 14 Dataset Side View

Acknowledgements: This work would not have been possible without the support from Don Greenberg, Alex Vladimirsky, and everyone in the Program of Computer Graphics. This work was supported by the National Science Foundation ITR / AP: CCF-0205438, the Department of Architecture, and the Department of Computer Science.