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

Han M, Kim K, Jang SJ, Cho HS, Bouma BE, Oh WY, Ryu S - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

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.

No MeSH data available.


Related in: MedlinePlus

Cutaway views with segmented stents and guide-wire.Data acquired with (a) 200 μm and (b) 100 μm longitudinal pitches, respectively. (c) Fly through view (100 μm pitch).
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pone.0124192.g008: Cutaway views with segmented stents and guide-wire.Data acquired with (a) 200 μm and (b) 100 μm longitudinal pitches, respectively. (c) Fly through view (100 μm pitch).

Mentions: Fig 8 shows sample images generated by the 3D visualization module in our framework. Fig 8(a) presents a cutaway view that illustrates the vessel wall in red, stent struts in yellow, and guide-wire in gray. Fig 8(b) shows a similar cutaway view for a different pullback, and Fig 8(c) shows its corresponding fly through view. While the cross-sectional images of the pullback in Fig 8(a) were apart by 200 μm in longitudinal direction, the images of the pullback in Fig 8(b) and 8(c) were apart by 100 μm. Fig 9(a) additionally shows malapposed stent struts in blue. Its corresponding 2D images in Fig 9(b) clearly show malapposed stent struts indicated by blue arrows.


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

Han M, Kim K, Jang SJ, Cho HS, Bouma BE, Oh WY, Ryu S - PLoS ONE (2015)

Cutaway views with segmented stents and guide-wire.Data acquired with (a) 200 μm and (b) 100 μm longitudinal pitches, respectively. (c) Fly through view (100 μm pitch).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4400174&req=5

pone.0124192.g008: Cutaway views with segmented stents and guide-wire.Data acquired with (a) 200 μm and (b) 100 μm longitudinal pitches, respectively. (c) Fly through view (100 μm pitch).
Mentions: Fig 8 shows sample images generated by the 3D visualization module in our framework. Fig 8(a) presents a cutaway view that illustrates the vessel wall in red, stent struts in yellow, and guide-wire in gray. Fig 8(b) shows a similar cutaway view for a different pullback, and Fig 8(c) shows its corresponding fly through view. While the cross-sectional images of the pullback in Fig 8(a) were apart by 200 μm in longitudinal direction, the images of the pullback in Fig 8(b) and 8(c) were apart by 100 μm. Fig 9(a) additionally shows malapposed stent struts in blue. Its corresponding 2D images in Fig 9(b) clearly show malapposed stent struts indicated by blue arrows.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

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.

No MeSH data available.


Related in: MedlinePlus