TY - GEN
T1 - Remote volume rendering pipeline for mHealth applications
AU - Gutenko, Ievgeniia
AU - Petkov, Kaloian
AU - Papadopoulos, Charilaos
AU - Zhao, Xin
AU - Park, Ji Hwan
AU - Kaufman, Arie
AU - Cha, Ronald
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - CPU/GPU encoders medical applications
KW - high quality streaming video
KW - mHealth
KW - mobile health
KW - remote volume rendering
KW - thin-client architecture
UR - https://www.scopus.com/pages/publications/84901751523
U2 - 10.1117/12.2043946
DO - 10.1117/12.2043946
M3 - Conference contribution
AN - SCOPUS:84901751523
SN - 9780819498328
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2014 - PACS and Imaging Informatics
PB - SPIE
T2 - Medical Imaging 2014 - PACS and Imaging Informatics: Next Generation and Innovations
Y2 - 18 February 2014 through 20 February 2014
ER -