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GPU-accelerated framework for intracoronary optical coherence tomography imaging at the push of a button.


ABSTRACT: Frequency domain optical coherence tomography (FD-OCT) has become one of the important clinical tools for intracoronary imaging to diagnose and monitor coronary artery disease, which has been one of the leading causes of death. To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional intracoronary FD-OCT images. In this paper, we present the first, to our knowledge, "push-of-a-button" graphics processing unit (GPU)-accelerated framework for intracoronary OCT imaging. Our framework visualizes 3D microstructures of the vessel wall with stent struts from raw binary OCT data acquired by the system digitizer as one seamless process. The framework reports the state-of-the-art performance; from raw OCT data, it takes 4.7 seconds to provide 3D visualization of a 5-cm-long coronary artery (of size 1600 samples x 1024 A-lines x 260 frames) with stent struts and detection of malapposition automatically at the single push of a button.

SUBMITTER: Han M 

PROVIDER: S-EPMC4400174 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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GPU-accelerated framework for intracoronary optical coherence tomography imaging at the push of a button.

Han Myounghee M   Kim Kyunghun K   Jang Sun-Joo SJ   Cho Han Saem HS   Bouma Brett E BE   Oh Wang-Yuhl WY   Ryu Sukyoung S  

PloS one 20150416 4


Frequency domain optical coherence tomography (FD-OCT) has become one of the important clinical tools for intracoronary imaging to diagnose and monitor coronary artery disease, which has been one of the leading causes of death. To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional  ...[more]

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