The 4D Cluster Visualization project

Michael J. Redmond; Ethan K. Brodsky; Yu-Hen Hu; Tom M. Grist; Michael J. Schulte; Walter F. Block

doi:10.1117/12.537937

5 May 2004 The 4D Cluster Visualization project

Michael J. Redmond, Ethan K. Brodsky, Yu-Hen Hu, Tom M. Grist, Michael J. Schulte, Walter F. Block

Proceedings Volume 5367, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display; (2004) https://doi.org/10.1117/12.537937
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

It is becoming increasingly common to image time-resolved flow patterns through the vascular system in all three spatial dimensions using non-invasive methods. The capability to generate four-dimensional (4D) (x, y, z and time) vascular flow data is growing in several modalities. Vastly undersampled Isotropic PRojection (VIPR) is one such method using high-resolution, fast Magnetic Resonance Imaging (MRI) of the vasculature system during intravenous contrast injection. VIPR currently produces 4D data sets of twenty to forty frames of 256³ voxels each, and stronger magnets will allow higher resolution time series that generate gigabytes of data. Real-time visualization and analysis of 4D data can quickly overwhelm the memory and processing capabilities of desktop workstations. 4D Cluster Visualization (4DCV) offers a straightforward, scalable approach to interactively display and manipulate 4D, reconstructed, VIPR data sets. 4DCV exploits the inherently parallel nature of 4D frame data to interactively manipulate and render individual 3D data frames simultaneously across all nodes of a visualization cluster. An interactive animation is produced in real-time by reading back the 2D rendered results to a central animation console where the image sequence is assembled into a continuous stream for display. Basic 4DCV can be extended to allow rendering of multiple frames on one node, compression of image streams for serving remote clinical workstations, and local archival storage of 3D data frames at the cluster nodes for quick retrieval of medical exams. 4D Cluster Visualization concepts can also be extended to distributed and Grid implementations.

Citation Download Citation

Michael J. Redmond, Ethan K. Brodsky, Yu-Hen Hu, Tom M. Grist, Michael J. Schulte, and Walter F. Block "The 4D Cluster Visualization project", Proc. SPIE 5367, Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, (5 May 2004); https://doi.org/10.1117/12.537937

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