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Proceedings Paper

Remote volume rendering pipeline for mHealth applications
Author(s): Ievgeniia Gutenko; Kaloian Petkov; Charilaos Papadopoulos; Xin Zhao; Ji Hwan Park; Arie Kaufman; Ronald Cha
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Paper Abstract

We introduce a novel remote volume rendering pipeline for medical visualization targeted for mHealth (mobile health) applications. The necessity of such a pipeline stems from the large size of the medical imaging data produced by current CT and MRI scanners with respect to the complexity of the volumetric rendering algorithms. For example, the resolution of typical CT Angiography (CTA) data easily reaches 512^3 voxels and can exceed 6 gigabytes in size by spanning over the time domain while capturing a beating heart. This explosion in data size makes data transfers to mobile devices challenging, and even when the transfer problem is resolved the rendering performance of the device still remains a bottleneck. To deal with this issue, we propose a thin-client architecture, where the entirety of the data resides on a remote server where the image is rendered and then streamed to the client mobile device. We utilize the display and interaction capabilities of the mobile device, while performing interactive volume rendering on a server capable of handling large datasets. Specifically, upon user interaction the volume is rendered on the server and encoded into an H.264 video stream. H.264 is ubiquitously hardware accelerated, resulting in faster compression and lower power requirements. The choice of low-latency CPU- and GPU-based encoders is particularly important in enabling the interactive nature of our system. We demonstrate a prototype of our framework using various medical datasets on commodity tablet devices.

Paper Details

Date Published: 19 March 2014
PDF: 7 pages
Proc. SPIE 9039, Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations, 903904 (19 March 2014); doi: 10.1117/12.2043946
Show Author Affiliations
Ievgeniia Gutenko, Stony Brook Univ. (United States)
Kaloian Petkov, Stony Brook Univ. (United States)
Charilaos Papadopoulos, Stony Brook Univ. (United States)
Xin Zhao, Stony Brook Univ. (United States)
Ji Hwan Park, Stony Brook Univ. (United States)
Arie Kaufman, Stony Brook Univ. (United States)
Ronald Cha, Samsung Research America (United States)


Published in SPIE Proceedings Vol. 9039:
Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations
Maria Y. Law; Tessa S. Cook, Editor(s)

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